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Discovering the genetic architecture of the mind Karlsson Linnér, R.

2019

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Download date: 02. Oct. 2021 CNCR Amsterdam

Discovering the genetic architecture of the mind – (Epi-)genome-wide association studies on human psychology and behavior

Richard Karlsson Linnér

Doctorate committee: Chair: Prof. Dr. August B. Smit Prof. Dr. Ralph Hertwig Prof. Dr. Sven Oskarsson Dr. Cornelius A. Rietveld Prof. Dr. Karin J. H. Verweij

Paranymphs: Casper A. P. Burik Hyeokmoon Kweon

Cover design: Cliff van Thillo Printing: Ridderprint BV

ISBN 978-94-6375-379-1 Copyright © Richard Karlsson Linnér, 2019

All rights reserved. No part of this thesis may be reproduced, stored in a retrieval system, or transmitted in any form or by any means without prior permission of the holder of the copyright.

VRIJE UNIVERSITEIT

DISCOVERING THE GENETIC ARCHITECTURE OF THE MIND

(Epi-)genome-wide association studies on human psychology and behavior

ACADEMISCH PROEFSCHRIFT

ter verkrijging van de graad Doctor aan de Vrije Universiteit Amsterdam, op gezag van de rector magnificus prof.dr. V. Subramaniam, in het openbaar te verdedigen ten overstaan van de promotiecommissie van de Faculteit der Bètawetenschappen op donderdag 6 juni 2019 om 9.45 uur in de aula van de universiteit, De Boelelaan 1105

door

Richard Karlsson Linnér

geboren te Kalmar Domk, Zweden

promotoren: prof.dr. P.D. Koellinger prof.dr. D. Posthuma

Contents PREFACE ...... L CHAPTER 1 INTRODUCTION ...... 1 BACKGROUND...... 2 PURPOSE AND RESEARCH QUESTIONS ...... 6 THESIS DISPOSITION ...... 7 THE GENETIC ARCHITECTURE OF TRAITS ...... 8 INTRODUCTION TO THE MAIN PHENOTYPES ...... 10 INDIVIDUAL CONTRIBUTIONS ...... 12 REFERENCES – PREFACE & CHAPTER 1 ...... 15 CHAPTER 2 GENOME-WIDE ASSOCIATION ANALYSES OF RISK TOLERANCE AND RISKY IN OVER ONE MILLION INDIVIDUALS IDENTIFY HUNDREDS OF LOCI AND SHARED GENETIC INFLUENCES ...... 25 ABSTRACT ...... 26 INTRODUCTION ...... 27 RESULTS ...... 27 DISCUSSION ...... 33 AUTHORS – CHAPTER 2 ...... 34 REFERENCES – CHAPTER 2...... 35 FIGURES AND TABLES – CHAPTER 2...... 38 CHAPTER 2 SUPPLEMENTARY METHODS...... 42 OVERVIEW OF MAIN GWAS ...... 42 GENOTYPING AND IMPUTATION ...... 43 ASSOCIATION ANALYSES ...... 43 MAIN REFERENCE PANEL ...... 44 UK BIOBANK GENOTYPING ARRAYS...... 46 DESCRIPTION OF MAJOR STEPS IN QUALITY-CONTROL (QC) ANALYSES ...... 47 META-ANALYSIS, ADJUSTMENT OF THE STANDARD ERRORS, AND TEST OF HETEROGENEITY OF EFFECT SIZES ACROSS COHORTS ...... 50 APPROXIMATELY INDEPENDENT LEAD SNPS, LOCI, AND CONDITIONAL ANALYSIS ...... 52 CHECK FOR LONG-RANGE LD REGIONS, CANDIDATE INVERSIONS, AND 1000 GENOMES STRUCTURAL VARIANTS ...... 53 INVESTIGATION OF THE NOVELTY OF OUR GWAS ASSOCIATIONS ...... 55 GWAS CATALOG LOOKUP ...... 55 CROSS-LOOKUP OF GWAS RESULTS ...... 56 ANNOTATION ...... 56 METHODS TO ESTIMATE GENOME-WIDE SNP HERITABILITY ...... 57 REFERENCES – CHAPTER 2 SUPPLEMENTARY METHODS ...... 59 CHAPTER 3 GENETIC VARIANTS ASSOCIATED WITH SUBJECTIVE WELL-BEING, DEPRESSIVE SYMPTOMS, AND NEUROTICISM IDENTIFIED THROUGH GENOME-WIDE ANALYSIS ...... 63 ABSTRACT ...... 64 INTRODUCTION ...... 65 RESULTS ...... 66 DISCUSSION ...... 71 AUTHORS – CHAPTER 3 ...... 73 REFERENCES – CHAPTER 3...... 74 FIGURES AND TABLES – CHAPTER 3...... 76 CHAPTER 4 AN -WIDE ASSOCIATION STUDY META-ANALYSIS OF EDUCATIONAL ATTAINMENT ...... 81 ABSTRACT ...... 82 INTRODUCTION ...... 83 METHODS ...... 83 RESULTS ...... 86

DISCUSSION ...... 90 CONCLUSION ...... 91 AUTHORS – CHAPTER 4 ...... 92 REFERENCES – CHAPTER 4...... 93 FIGURES AND TABLES – CHAPTER 4...... 97 CHAPTER 4 SUPPLEMENTARY METHODS...... 104 STUDY OVERVIEW ...... 104 SAMPLE INCLUSION CRITERIA ...... 104 PHENOTYPE DEFINITION AND SAMPLE DESCRIPTIVE STATISTICS ...... 105 DNA MEASUREMENT AND COHORT-LEVEL QUALITY CONTROL (QC) ...... 105 EPIGENOME-WIDE ASSOCIATION STUDY (EWAS) ...... 106 DESCRIPTION OF MAJOR STEPS IN META-LEVEL QUALITY-CONTROL (QC) ANALYSES ...... 107 META-ANALYSIS ...... 107 INVESTIGATION OF POSSIBLE CONFOUNDING WITH TOBACCO SMOKING ...... 108 ROBUSTNESS OF EWAS RESULTS IN THE NEVER-SMOKER SUBSAMPLE ...... 108 ENRICHMENT ANALYSES...... 111 PREDICTION OF EA WITH POLYGENIC METHYLATION SCORES ...... 111 CORRELATION OF EWAS ASSOCIATIONS WITH TISSUE-SPECIFIC METHYLATION ...... 112 METHQTL ANALYSIS ...... 113 REFERENCES – CHAPTER 4 SUPPLEMENTARY METHODS ...... 115 CHAPTER 5 META-ANALYSIS OF THE SEROTONIN TRANSPORTER VARIANT (5- HTTLPR) IN RELATION TO ADVERSE ENVIRONMENT AND ANTISOCIAL BEHAVIOR ...... 119 ABSTRACT ...... 120 INTRODUCTION ...... 121 MATERIALS AND METHODS ...... 123 RESULTS ...... 127 DISCUSSION ...... 129 REFERENCES – CHAPTER 5...... 132 FIGURES AND TABLES – CHAPTER 5...... 136 CHAPTER 6 DISCUSSION AND CONCLUSION ...... 145 CONCLUSION ...... 152 STUDY LIMITATIONS ...... 153 REFERENCES – CHAPTER 6...... 154 GENERAL SUMMARY ...... 157

Preface

Preface

“This needs some rephrasing – it's loaded with the assumption that there is a real world.” Anonymous refereea

My determination to understand the human condition, what it essentially means to be human, began in my teens, enticed by a desire to comprehend myself and the people around me. In essence, a philosophical and scientific quest driven by a will to grasp why we exist, why we behave the way we do, what shapes our experience of the world, and what the heck I am doing here. That strife has been weary and confusing at times. I have spent many nights awake reading what others have thought about the matter of existence and the human mind: “The element of a person that enables them to be aware of the world and their experiences, to think, and to feel; the faculty of consciousness and thought.”1 And I am not alone. At great lengths, philosophers have argued the fundamental nature of the mind, debating ideas such as whether anything actually exists beyond one’s own mind, solipsism; whether we are born as blank slates, tabula rasa; and whether consciousness is part of the physical reality or some other non-physical realm, the mind-body duality. I have often felt disappointed because of the seeming disagreement about the causes of conscious experience. Fortunately, my understanding of the physical, biological, and evolutionary basis of thought and behavior has since developed thanks to many great thinkers in fields like physics2, genetics3, and economics4. I am glad that these schools of thought eventually crossed my path. In the 19th century, another curious individual set out on a quest of his own to explain the origin of species. Charles Darwin introduced the revolutionary idea of natural selection, and by doing so, set the ball rolling for the development of evolutionary theory. Since then, others have with great ingenuity discovered that DNA is the carrier and transmitter of the instructions of life, a “selfish” replicator that resides within the nucleus of living cells5,6. I believe the frontiers of human knowledge have been pushed far beyond what our ancestors could possibly have imagined long ago when they conceived elaborate myths about the origin of life and the beginning of humankind. Nonetheless, the question whether not only physical, but also mental traits, can be inherited from parents to offspring through genetic transmission has been a subject of great controversy7,8. Importantly, this work is based on the premise that mental traits are indeed heritable, and I think many would agree, perhaps a bit reluctantly at first, that there is irrefutable evidence in favor of that position. In a time where academia is facing somewhat of a replicability crisis9,10, I feel lucky to have been embraced by a group of extraordinary mentors and researchers that have taken precise and truthful inference as their most serious endeavor. Whenever I have come up with an answer to a question, I have been asked whether it could be superseded by an alternative and more parsimonious explanation. Thankfully, I have never been requested to massage data for the purpose of reaching statistical significance nor to neglect evidence in support of alternative viewpoints. The last four years that have passed since I, as a rather naïve young scientist, began the journey of a doctorate has meant so much for my personal and academic development. That journey has been heavy at times, and I would like to express my gratitude to my supervisor Philipp Koellinger for his support and for always having his door open. Thank you for placing

a Oakes, K. (2014, December 12). 25 Brutally Honest Peer Review Comments from Scientists [Web log post]. Retrieved November 7, 2018, from https://www.buzzfeed.com/kellyoakes/more-like-smear-review-amirite

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Preface your trust in me, for all the encouragement at critical moments, and for taking me on this wild and crazy ride. I would also like to thank Daniel Benjamin, David Cesarini, Jonathan Beauchamp, and the other members of the Social Science Genetic Association Consortium for all their knowledge, guidance, and support. Next, I would like to thank my secondary supervisor Danielle Posthuma and the fantastic team she is directing at the Complex Trait lab at Vrije Universiteit Amsterdam. I am fortunate to have too many great colleagues to be able to acknowledge them all by name, but I would like to mention some of them in particular. Fleur Meddens for sharing many hardships, laughs, and late evenings in the office. Niels Rietveld, Ronald de Vlaming, and Aysu Okbay for all the skills you have taught me, and for taking the time to answer my long and dreadful emails. Maël Lebreton for introducing me to the life of an independent scientist, and for trying to get me to like whiskey. Casper Burik and Hyeokmoon Kweon for doing me the honor of being my paranymphs. Bart, Laura, Abdel, Michel, Yayouk, and the other remarkable people at the department of Biological Psychology for all the great moments together across the world. I am forever grateful to all the co-authors, collaborators, and study participants that have contributed to the studies in this thesis. I would like to thank the examination committee for bestowing me the honor of evaluating my work. I wish to thank my parents and family for teaching me about the world, for encouraging me to read and study, and for being there. I am grateful for all the friends I have and for their interest in my research, their support in good and bad times, and for the great times yet to come. Finally, I want express my deepest gratitude and sincerest appreciation of Mirjam Timmer, for your invaluable patience, encouragement, and love. You have kept me sane at times of intense struggle, and you have believed in me at times when my own beliefs have faded and faltered. This thesis is as much yours as it is mine, I could not have done it without you. Notwithstanding the boasting title, by no means do I claim to have completely discovered the genetic architecture of the mind nor of the mental traits studied in this thesis. The efforts presented here are but a small step towards that grand ambition. My dearest wish is that this piece of work will be able to contribute, if only a little bit, to our understanding of who we are, where we come from, and most importantly, where we are going.

Richard Karlsson Linnér Amsterdam, November 9, 2018

ii Introduction

“Why do people believe that there are dangerous implications of the idea that the mind is a product of the brain, that the brain is organized in part by the genome, and that the genome was shaped by natural selection?” Steven Pinker

“Everything we do, every thought we’ve ever had, is produced in the . But exactly how it operates remains one of the biggest unsolved mysteries, and it seems the more we probe its secrets, the more surprises we find.” Neil deGrasse Tyson 1

Chapter 1

Background

Humans have evolved large and complex brains as an adaption to cognitive, environmental, and social challenges11. Throughout the life course, our brains develop and that process influences how we think, feel, and act12. People are in many ways similar, but also display a range of individual differences in their psychology and behavior. For example, the personal attitude or willingness towards risk taking is a psychological characteristic, studied in this thesis, that varies substantially in humans13–16. In short, risk refers to the possibility of experiencing undesirable outcomes, which is usually accepted in exchange for the prospect of some benefit or reward. Some individuals are more willing to engage in risky activities, such as driving over the speed limit, while others behave more cautiously15,17,18. Individual differences in risk taking are associated with many other traits, such as health-risk behaviors, financial decision-making, and occupational choice14,17. Overall, it is intriguing to study the determinants of individual differences in mental traits as a way to understand ourselves as , and because of their importance for various health and life outcomes16,19–22. For the purpose of illustration, similar to the works of other scholars12, this introductory chapter refers to psychology and behavior, together with mental traits, interchangeably as a general concept, while the section Introduction to the main phenotypes introduces the primary phenotypes studied in this thesis. Ultimately, mind and consciousness arise from the brain2,12, and observable psychology and behavior come about foremost as a result of the brain’s circuits, processes, and functions12. Conceptually, I consider the phenotypes under study to be distinct, but yet related, measures of psychology and behavior that can be quantified, and thus, studied empirically with statistical methods23. This thesis is an investigation of how genetic factors influence these particular phenotypes, but it will also treat a few environmental factors. But before we get to that, I will first review what is known about the heritability of psychology and behavior.

The heritability of psychology and behavior

Individual differences in mental traits are the result of both genetic and environmental influences6,24,25. Decades of research in behavior genetics, the scientific study of the genetic and environmental underpinnings of human psychology and behavior13, have convincingly established that practically all mental traits are heritable to some degree25–27. Heritability estimates of psychometric measures of psychology and behavior, a measure of the proportion of phenotypic variability attributable to genetic factors, range from moderate to substantial25,28,29, and are on average about 50%12. Particular psychiatric disorders are highly heritable, such as -deficit hyperactivity disorder (ADHD), , and , and genetic factors explain about 75–80% of the variation in these traits30. In contrast, other mental traits are less heritable, such as risk preferences13,14, depression30, and educational attainment31,32, and roughly 20–50% of their variation is attributable to genetic factors. Thus, there is overwhelming evidence in support of the premise that psychology and behavior are influenced by a non-negligible genetic component, and it appears as if both and environment contribute substantially to individual differences in such traits. Based on recurring empirical findings, Turkheimer (2000) proposed three general “laws” of behavior genetics26,27. Beyond the first insight that all behaviors are heritable to some degree, the second insight is that the common effect of being raised in the same family is typically weaker than the effect of genetic factors. It is known that mental traits and disorders cluster in families and empirical estimates suggest that this clustering is primarily caused by a shared genetic liability, rather than the shared environment19,20,33. The third insight is that idiosyncratic environmental factors, unique to each individual, often account for a sizable share of the

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Discovering the genetic architecture of the mind phenotypic variation26,27. However, such factors are frequently unobserved in studies of heritability, and it is common to assume that idiosyncratic effects are independent of genetic differences and to model those as part of the stochastic residual variation34. 1 Beyond the three “laws” of behavior genetics, there are two more recent insights that deserve to be mentioned because of their implications for the research strategy and of this thesis. First, converging evidence suggests that the genetic effects on mental traits and disorders are shared across traits19,30,35, which can be referred to as pleiotropy or genetic overlap36–38. It appears as if the genetic liability that clusters in families not only predisposes family members towards a particular mental disorder, but it also increases the susceptibility towards others. and family studies, as well as more recent investigations based on molecular genetic data, consistently estimate moderate to substantial genetic overlap across many pairs of mental traits and disorders19,20,35. Yet, most of the particular genetic variants and regions that are responsible for the observed coheritability have not been identified35,39. Second, many studies suggest that the genetic influences on complex traits are polygenic, and thus, distributed across many genetic variants30,40,41. Overall, the aggregate influence of common variants can be substantial, but their individual effects appear to be smaller and distributed across a much larger set of variants than was previously thought, and maybe even more so for psychology and behavior than for many other complex traits27. This regularity has been dubbed the fourth “law” of behavior genetics by Chabris et al. (2015)27. As an example, the largest effect discovered in the first genome-wide association study (GWAS, see below and Chapter 2 – Supplementary Methods) on educational attainment explains only about 0.02% of the phenotypic variation31, i.e., a fiftieth of a percent. It is the exception rather than the rule to find common variants with large effects on mental traits30. Taken together, these observations conform with an infinitesimal effect-size model41. That model assumes that the additive genetic influence is spread over a very large number, and maybe even all, genetic variants of which many have near-zero effects41–44. Recently, it has been proposed that potentially all genetic variants involved in a trait-relevant tissue could exert some small yet important effect even when there is no obvious biological connection42. This hypothesis has been named the “omnigenic model” and it is based on the premise that regulatory gene networks are deeply interconnected. In summary, converging evidence suggests that there is much genetic overlap across mental traits, and that such traits are highly polygenic rather than monogenic.

Genome-wide association studies

Recent technological advancements have revolutionized the discovery of the genetic architecture of human traits and disorders45, defined in the section The genetic architecture of complex traits. Modern genotyping technologies first made it possible to measure particular genetic variants, and later, millions of variants across the genome. When the genetic factor is no longer latent, as it is in the classical twin design34, it becomes possible to actually discover which particular genetic variants are responsible for the trait inheritance. The GWAS method is a modern approach used to identify genetic associations43,46,47, and GWAS typically study the most occurring form of genetic variation, the single-nucleotide polymorphism (SNP; pronounced as “snip”). A SNP is defined as a single base pair in the genetic sequence that varies across individuals. The success of GWAS is strongly dependent on sample size48,49. Theoretically, a sample size of roughly 200,000 individuals is required to attain 80% power to detect an effect of 0.02% at genome-wide significanceb, similar to the effect mentioned above. Nonetheless, when performed in large samples, GWAS can identify common variants with high posterior probabilities even though their effects are small27,50. b Genome-wide significance is typically declared when the association P-value is less than 510–8.

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Chapter 1

Since its introduction, GWAS have successfully identified robust associations with hundreds of genetically complex traits43,45,49. But even so, the vast majority of the variants responsible for the narrow-sense heritability of brain structure, and the resulting variation in observable behavior, have not yet been identified30,51,52. The difference between the narrow-sense heritability and that which can be attributed to genome-wide significant associations is called the “missing heritability”30,53. The missing heritability can be divided into two parts; the “hiding heritability”, which is expected to be uncovered as a function of increasing GWAS sample size, and the “still-missing heritability”, which will not be revealed by traditional GWAS53. Instead, the still-missing heritability is expected to be attributed to other genetic factors that are typically not captured by traditional GWAS, such as rare and structural variation40,41,54. Methods that use millions of common SNPs across the genome to estimate the so-called “SNP heritability” are suggestive of the respective sizes of the hiding and still-missing heritability53,55. As described below, Chapters 2–3 perform large-scale GWAS of a couple of mental traits that, upon publication, had not yet been studied in samples as large as those reported here.

Psychosocial experiences and DNA methylation

Epigenetics is the study of various molecular mechanisms that influence genetic processes, such as the regulation of gene expression56–58. In contrast to the relative constancy of the genetic sequence59, epigenetic states vary across cells and tissues, and are affected by environmental influences60. In behavioral , DNA methylation appears to be the most studied mechanism61. Methylation refers to the addition of a methyl group on “top of” a genetic base pair, and it is known to play an important role in cellular development and processes56,62,63. In mammals, methylation occurs almost exclusively at the cytosine nucleotide in cytosine-guanine pairs63 connected by a phosphate link, referred to as CpG probes. Importantly, it is of scientific and societal interest to study methylation because of the repeated observation that it is associated with various medical conditions, such as autoimmune disease, cancers, and neuropsychiatric disorders62,64,65. Microarrays have recently been introduced that measure methylation at hundreds of thousands of CpG probes across the genome66. A common approach to identify associations with methylation is the epigenome-wide association study (EWAS), an extension of the GWAS method with some additional considerations67 (see Chapter 4 – Supplementary Methods). Interestingly, it has been proposed that environmental factors may shape behavior by influencing gene expression12, and a conceivable mechanism could be the methylation of genes involved in neural development56,61,68,69 . However, this is a new and highly active area of research and it has not been concluded what genes and environmental factors have the capability to affect behavior in such a way65. Interestingly, methylation has been offered as a biological explanation of the well-established relationship between adverse psychosocialc experiences in childhood with mental and physical disorders later in life70–74. It has even been proposed that methylation could be an adaptive mechanism that later turns maladaptive71. For ease of discussion, this thesis henceforth refers to this category of viewpoints as “the hypothesis that psychosocial experiences influence disease via methylation”. Notably, a range of adverse life events, ranging from trauma75; parental attachment76, abuse77, and neglect78,79; to socioeconomic hardship70,80–82, have been found associated with methylation. The notion that biological embedding of psychosocial , caused by unfavorable socioeconomic position, would cause behavioral changes and medical conditions via methylation appears to be an appealing and fascinating explanation for observed health

c Psychosocial – “Relating to the interrelation of social factors and individual thought and behaviour.”181

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Discovering the genetic architecture of the mind inequalities70,73,74,83. However, it should be noted that studies on this hypothesis are not free from criticism61,67,84,85, and great care must be taken in the choice of study design65,67. 1 On the contrary, it has been proposed that chemical influences with known biological effects must first be convincingly ruled out before prioritizing the correlation of psychosocial experiences with methylation as an explanation to medical conditions86. Importantly, there are several biologically proximate exposures that have particularly strong and long-lasting influences on both methylation and medical conditions, such as alcohol consumption87, diet and obesity88, and smoking89,90, in particular if experienced prenatally91,92. These risk factors are known to correlate with socioeconomic adversity and most, if not all, of the aforementioned adverse life experiences, such as child maltreatment93–98. Thus, there is a high risk that the positive findings reported in observational studies of this hypothesis are the result of confounding and/or omitted variable bias59,99. That particular criticism has already been raised because most of the aforementioned studies do not convincingly control for many important confounders67,84,85. Similarly, it has been observed that there is a tendency to infer causality in observational studies without the use of a causal research design67. Notwithstanding these critiques, the number of studies that investigate the hypothesis that psychosocial experiences influence disease via methylation is growing, and concerningly, many are performed in small samples79,100. In this thesis, Chapter 4 performs a large-scale EWAS on educational attainment, a correlate of cognitive function and personality31, which is also a major life experience that occurs day after day over many years of a person’s life. Because education is experienced over a substantial period of childhood, adolescence, and early adulthood it is not unreasonable to think that its variation could be quite strongly associated with methylation in the circumstance that life experiences would indeed influence methylation. In addition, educational attainment could be considered a good test case of that hypothesis because it can be studied in very large samples. On the other hand, because educational attainment, similar to adverse psychosocial experiences, could be considered biologically distal compared to biologically proximate confounders, such as alcohol consumption, smoking, and other health-risk behaviors98,101, it is also conceivable that its association with methylation could be much weaker or non-existent when such factors are accounted for.

Studies of candidate-genes and their interactions with the environment

Prior to GWAS, it was more common to study prioritized candidate genes102–104. In contrast, GWAS test millions of genetic variants across the genome for association, with little to no prioritization46. Already in the early 2000s103,104, the candidate-gene approach had received much criticism, primarily because of inadequate statistical power, but also other concerns, such as undisclosed multiple-testing and population stratification24,50,105–107. Thus, while GWAS could be criticized for having a high false-negative rate41, candidate-gene studies in psychology have instead been criticized for an unacceptable false-positive rate106. Similarly, many of the same critiques apply to the popular study of candidate gene-environment interactions105,108,109, and such studies must be undertaken with great care110. Disconcertingly, many candidate gene-environment interaction studies are performed with improper control for potential confounders109, and convincing replication of reported interactions is scarce in psychology research108. Therefore, to further investigate and raise awareness of the issues surrounding these studies, Chapter 5 performs a critical review and meta-analysis of a particular candidate gene-environment interaction. Specifically, that study investigates the reported literature that has tested whether antisocial behavior is associated with an interaction between stressful life events and 5-HTTLPR, a controversial, yet frequently hypothesized

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Chapter 1 candidate gene34,111. Although 5-HTTLPR is viewed with quite some skepticism it still receives much attention in recent gene-environment interaction studies112–114. Remarkably, a couple of notorious candidate genes108,110,115,116, including 5-HTTLPR, appear to have a renaissance in behavioral epigenetics, see e.g., refs. 73,76,78,79,100.

Problem statement

The objective of this thesis is to contribute to the on-going efforts in behavior genetics and related fields that aim to unravel the genetic, biological, and environmental basis of complex traits. The primary measures of psychology and behavior studied in this thesis, henceforth referred to as the “main phenotypes”, are the following: • Chapter 2. General risk tolerance, adventurousness, and the four risky behaviors: (1) automobile speeding propensity, (2) drinks per week, (3) ever smoker, and (4) number of sexual partners and the first principal component (PC) of the four risky behaviors; • Chapter 3. Subjective well-being, depressive symptoms, and neuroticism; • Chapter 4. Educational attainment; • Chapter 5. Antisocial behavior. The main phenotypes have been extensively studied in different fields with varying research traditions, for example in economics, epidemiology, psychology, and the social sciences. At the outset of this thesis, there were few to no genome-wide significant associations reported for the subset of the main phenotypes under study in Chapters 2 and 3, and larger genetic studies are needed to accelerate the discovery of their genetic architectures30,49,52. Similarly, larger studies could be considered required to further investigate whether life experiences are indeed associated with methylation (Chapter 4), as well as the hypothesis that antisocial behavior is associated with an interaction between stressful life events and 5-HTTLPR (Chapter 5). Because underpowered studies have a high chance of finding false positives and inflated effect- sizes estimates it is important that the studies in this thesis are adequately powered50,110,117. To the best of my knowledge, the primary genetic analyses were upon publication the largest for these phenotype-method pairs. In Chapters 2–3, the GWAS samples range from about 160,000 to almost 1 million individuals. In Chapter 4, the EWAS includes 10,767 individuals, which places it among the largest EWAS of any phenotype. Lastly, Chapter 5 is the largest investigation of that particular gene-environment interaction, n = 7,680, as it is a meta-analysis of the previous literature testing that hypothesis. In summary, the studies reported in this thesis are all performed in large samples with molecular genetic data. Purpose and research questions

The central purpose of this thesis is to investigate the genetic architecture of the main phenotypes. For that purpose, genetic variants will be tested for association using statistical methods from complex trait genetics applied on molecular genetic data. In more detail, the primary purpose of Chapters 2–3 is to identify associations between SNPs and general risk tolerance, adventurousness, and the four risky behaviors and their first principal component; as well as with subjective well-being, depressive symptoms, and neuroticism. Subsequently, the results will be used in a range of genetic and bioinformatic follow-up analyses with the aim to identify additional aspects of their genetic architectures, as well as biological pathways and mechanisms. The purpose of Chapter 5 is to identify associations between educational attainment and the methylation of CpG probes. The purpose of Chapter 6 is to critically review

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Discovering the genetic architecture of the mind and meta-analyze all previous studies that have tested whether antisocial behavior is associated with a candidate gene-environment interaction between adverse life events and 5-HTTLPR. 1 The thesis aims to answer the following main research questions: 1. a. Do GWAS in hundreds of thousands of individuals identify robustly associated SNPs with the main phenotypes studied in Chapters 2–3? b. What do the results reveal about their genetic architectures? c. What is the potential to use the results to strengthen inference in empirical research and to perform multi-trait analyses of genetically correlated traits? d. What do the results reveal about biological mechanisms and pathways? e. What do the results suggest about the biological pathways and candidate genes that have previously been hypothesized to influence risk taking? 2. Is educational attainment associated with CpG methylation and how does the strength of association compare to biologically proximate factors? What do the results suggest about the hypothesis that psychosocial experiences influence disease via methylation? 3. What is the overall quality and evidential value of the previous studies that have tested antisocial behavior for association with an interaction between adverse life events and 5- HTTLPR? Thesis disposition

The remainder of this thesis is structured as follows. The next section defines the concept of genetic architecture and motivates the discovery of genetic associations. Thereafter, the main phenotypes are briefly introduced. Lastly, I report my own and my co-authors’ contributions to the large-scale, collaborative efforts upon which this thesis is based. Following this introduction, the thesis consists of four chapters that are based on empirical studies published in international, peer-reviewed journals. Chapters 2–3 are published in Nature Genetics (Karlsson Linnér et al., 2019; Okbay et al., 2016), Chapter 4 in Molecular (Karlsson Linnér et al., 2017), and Chapter 5 in the American Journal of Medical Genetics Part B: (Tielbeek et al., 2016). For completeness, these studies are reported in their entirety though not all results are discussed in this thesis. Lastly, the sixth chapter discusses how a curated selection of the findings answer the main research questions, and it reports a conclusion and the study limitations.

Statistical methods

This thesis relies on a large number of statistical methods and procedures, and it is far beyond the scope of this introductory chapter to describe them all. For brevity, the methods to perform a selection of the analyses that I carried out are reported in the Supplementary Methods sections. Chapter 2 – Supplementary Methods details the method to perform GWAS, quality- control and meta-analysis, estimation of conditional GWAS associations, and three methods to estimate SNP heritability. Chapter 4 – Supplementary Methods reports the method to perform EWAS, quality-control and meta-analysis, robustness checks, the comparison of effect-size estimates with biologically proximate factors, among other methods. All other relevant methods and materials are described in great detail either within the thesis chapters, or in the accompanying Online Supplementary Materials.

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Chapter 1

The genetic architecture of complex traits

The genome consists of deoxyribonucleic acid (DNA), which is recombined and transmitted from parents to offspring6,118,119. DNA is the carrier of genetic information—the instructions required for the growth and development of all known cellular life. The genome is a sequence consisting of four molecular “letters” (base pairs, or nucleotides), and in humans that sequence is roughly 3 billion base pairs long. The base pairs are made out of the molecules adenine (A), cytosine (C), guanine (G), and thymine (T). DNA takes the form of two strands, shaped like a double helix, and because of the complementary property of the nucleotides, that is A is always paired with T and C with G, knowledge of the sequence on one chromosomal strand can be used to infer its complementary strand. In humans, DNA is structured as 22 chromosome pairs (autosomes), as well as a pair of sex chromosomes (allosomes). Because the chromosomes come in pairs, a specific location on the genome (locus) can either be homozygous or heterozygous in terms of the particular nucleotide sequence (alleles) at that locus. For most practical purposes, it is common to assume that an individual’s genetic sequence is stable over the life span, and identical across the trillions of cells and various tissues in the body (with the exception of sex cells, and other special cases). The genetic architecture of a particular trait can broadly be defined as the complete set of genetic influences attributable for the heritability45,120. The definition includes, but is not limited to, the set of causal variants; the distribution of their effect sizes; and their number, correlation (linkage), and locality in the genome (e.g., in coding, non-coding, or regulatory regions). A broader definition also includes the occurrence of the alleles (allele frequency) of variants within and across populations; whether the alleles at the same locus interact (dominance), and whether they interact with alleles at other loci (epistasis), or with environmental factors (gene- environment interaction, or GE)34. If we would allow an even broader definition, the genetic architecture also covers the states of epigenetic mechanisms, such as methylation, as well as the genetic overlap with other traits121. Many of these aspects, but not all, will be studied in the thesis chapters. Since most genetic variants appear to exert only a tiny influence on psychology and behavior, it is not unreasonable to question whether efforts to identify genetic associations are actually worthwhile. I argue that genetic discovery is both intrinsically valuable, because such efforts may increase our understanding of the etiology of individual differences, which has allured the interest of scholars across time and disciplines, as well as extrinsically valuable, because the findings from genetic discovery have many scientific benefits. The latter can be categorized along three lines. First, genetic associations can in many ways strengthen inference in empirical research. Second, genetic associations with a particular trait can be beneficial for multi-trait analyses of genetically correlated phenotypes. Third, genetic associations offer a window into the biological mechanisms and pathways involved in trait variation. The remainder of this section further explains these three lines of motivation.

Strengthening inference in empirical research

Polygenic scores (PGS) index, or summarize, the genetic effects estimated in GWAS across a large number of genetic variants122,123, and PGS can in many ways strengthen inference in empirical research. For example, PGS can be used as control variables to condition the effect of a variable of interest or to increase the statistical power to detect a treatment effect in randomized control trials31,50. In addition, with the use of PGS, the method genetic instrumental variable (GIV) regression can accurately estimate the effect of an exposure in the presence of pleiotropy124. Further, gene-environment interaction studies that use PGS, rather than single genetic variants, are likely to have greater statistical power to detect an interaction effect111.

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Because GWAS summary statistics can be used to construct PGS, among other reasons, it has become common to share these with the wider research community43,125. Thus, genetic discovery could even be considered a scientific public service. Overall, identification of genetic 1 associations has much to offer to observational studies in fields such as epidemiology, psychology, and the social sciences126. Next, it is of scientific and societal importance to establish causal relationships between environmental risk factors and health outcomes127,128, for example in the effort to reduce health inequalities83. However, concerns have been raised over the unbiasedness of estimates reported in observational studies that lack randomization99,117,129. Because of the possibility that bidirectional and other complex pathways account for the covariation between risk factors and disease there is a risk of simultaneity and other sources of endogeneity130,131. As a remedy, it has been proposed that the random assortment of genes that an offspring inherits, conditional on the parents, could be used as instrumental variables in so-called Mendelian Randomization132. However, using genes as instruments is not without limitations127,128,133. On the positive side, an individual’s genome is highly stable over time, and in many conceivable cases, exogenous to the variables of interest in the study of risk factors and disease59. Genetic instruments may be particularly useful in research settings where appropriate quasi- randomization is hard to find, and can be applied to a wide range of research questions126. Some have even gone so far as to claim that a genetically-informed research design is a requirement to actually understand environmental influences111. Overall, researchers who aim to disentangle the complex relationships between mental traits, environmental risk factors, and health outcomes, should not neglect the advantages offered by a genetically-informed research design126,131.

Multi-trait analyses of genetically correlated traits

Conceptually, the main phenotypes could be considered to be intermediate phenotypes and/or non-clinical measures of mental disorders. The premise that genetic studies of normal-range measures of mental traits can potentially advance our understanding of the disorders of the brain, in contrast to a narrow focus on clinical diagnoses and symptoms, is at the essence of a research framework called the Research Domain Criteria (RDoC)134–136. Thus, genetic association with the main phenotypes could aid the effort to identify genes and biomarkers for mental disorders, which has been declared a grand challenge by an international consortium of clinicians and mental-health researchers21. There are several multi-trait methods available that leverage the genetic overlap across traits, such as the proxy-phenotype method and the multi- trait method MTAG137,138. LD Score regression is a method that is frequently used to estimate genetic correlations139,140, a measure of the extent of overlap in genetic effects between pairs of traits39,139,141. The size of genetic correlations is informative of the possible benefit of multi-trait analysis of genetically correlated traits43,138,142. For example, multi-trait analysis can aid the identification of genetic associations with phenotypes for which there is currently a lack of large samples (such as rare disorders, or phenotypes that for some reason may be difficult to sample)137. When several phenotypes are studied in combination it is possible to increase the statistical power to find associations with a particular trait111,138. Also, genetic associations can be used to quasi- replicate findings from other studies that lack a replication sample143. Overall, the possibility to perform multi-trait analyses of genetically correlated traits is a strong motivation to discover genetic associations.

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Identification of biological mechanisms and pathways

Genetic associations can be used to identify biological mechanisms and pathways because genetic variants can be linked to functionally relevant products, such as ribonucleic acids (RNAs), proteins, and gene expression levels144–146. Evidently, biological pathways that were previously thought to be unrelated to particular medical conditions have been discovered with GWAS49. Identification of biological pathways can be considered of additional importance with respect to psychiatric disorders for which the identification of pathophysiology has proven difficult21,52,135. Also, knowledge of biological pathways can aid the study of comorbidity and disease heterogeneity19,35. However, methods that use genetic associations to discover biological mechanisms and pathways have several limitations. Typically, the strength of such bioinformatic analyses is strongly dependent on the availability of additional data sources, such as information on gene expression, function, and networks. Another limiting factor is the availability of relevant tissue samples. Notwithstanding these limitations, many still consider identification genetic discovery to be an important first step in the effort to reveal the biological underpinnings of human traits and disorders30,49. Since the main phenotypes ultimately stem from the brain, which could be considered an extremely complex organ, this thesis may have limited potential to accurately pinpoint biological mechanisms and pathways. Yet, bioinformatic analyses of the initial GWAS estimates may shed some light on previous findings, and can potentially yield new insights.

Introduction to the main phenotypes

General risk tolerance and risky behaviors

Many activities and decisions entail an element of risk. Semantically, risk can be defined as the possibility of experiencing danger, harm, or loss147. Notably, the inclination to take risks varies substantially across individuals, while it is a relatively stable personal characteristic. It decreases as a function of age and it has been observed that women are less willing to take risks than men15–17. In the literature, the willingness to accept the exposure to risk in exchange for the prospect of a reward is often referred to as risk attitudes, risk aversion, or risk preferences13,15,16. Notably, there is no clear consensus on how this tendency should be defined and measured. In economics, risky decision-making is often conceptualized based on statistical concepts such as expected values and the variability of outcomes15, and modeled with expected utility theory16,23. As a critique of that theory, Kahneman & Tversky (1979) proposed a different framework—prospect theory—that contests the external validity of many of the assumptions of expected utility theory148,149. In psychology, risk-taking spans across several dimensions and is considered similar to sensation seeking, adventurousness, and related personality types and traits150–152. Overall, risk is a somewhat fuzzy concept, but yet an integral part of the decision-making processes of living organisms153. In Chapter 2, the primary phenotype is general risk tolerance, which is defined here as the self- reported overall willingness to take risks. In addition, that chapter investigates the six supplementary phenotypes adventurousness, automobile speeding propensity, drinks per week, ever smoker, number of lifetime sexual partners, and the first principal component (PC) of the four risky behaviors. The supplementary phenotypes were deliberately chosen to represent real- world risk taking in specific contexts, and the first PC is intended to capture a general component of risk taking across some of those domains. There have been many attempts to identify genes and biological pathways involved in risk tolerance but few have been reliably

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Discovering the genetic architecture of the mind performed in large samples154. Overall, there are only two genome-wide significant associations reported in the literature155–157. Therefore, Chapter 2 sets out to discover the genetic variants responsible for the missing heritability of risk taking in a much larger sample than has 1 previously been done.

Subjective well-being, depressive symptoms, and neuroticism

Subjective well-being is defined as an individual’s subjective rating of his or her quality of life158–160. It is typically measured through self-reports, using a combination of survey items intended to capture overall happiness, life satisfaction, and positive affect. In addition, Chapter 3 investigates two genetically correlated traits161,162, depressive symptoms and neuroticism. Depression is a debilitating medical condition with a high disease burden, which is hard to treat52,163. Larger genetic studies of depression are motivated to identify druggable targets and to accelerate the development of new treatments. Neuroticism is a personality dimension characterized by an affinity to experience negative feelings, and studies have found that neuroticism is strongly associated with depression164–166. Also, depressive symptoms and neuroticism could largely be considered reverse-coded measures of subjective well-being. Overall, there are few reported genetic associations with these traits in the literature52,167, and the GWAS in large samples reported in Chapter 3 may be able to uncover the missing heritability of these traits.

Educational attainment

Educational attainment is a measure of an individual’s scholastic achievement. Conceptually, educational attainment is both an outcome that is influenced by personal characteristics and environmental circumstances, and at the same time, education is an environmental exposure and major life experience. It is well-established that variation in education is strongly associated with mental and physical health, as well as longevity98,168,169, and higher education is a strong predictor of positive health behaviors and lifestyle choices98. Overall, educational attainment can be considered a crude measure, but nonetheless, it captures a range of important differences across individuals in a population, and it is easy and inexpensive to measure in large samples. To date, there are three large-scale GWAS of educational attainment published in the literature31,170,171. In these studies, educational attainment is operationalized as a continuous trait measured in years-of-schooling equivalents. The most recent study, which analyzed close to 1.1 million individuals, identified more than twelve hundred near-independent genetic associations171. Thus, GWAS of educational attainment have already been very successful. So, Chapter 4 instead investigates the association between educational attainment and CpG methylation. To my knowledge, there are no previous studies on that particular relationship, while there are studies that have investigated its association with methylation by using educational attainment as proxy for, or in combination with, socio-economic status, and a few associations have been reported81,172,173. Notably, our study is several times larger than any previous investigation of the association between educational attainment and methylation.

Antisocial Behavior

Antisocial behavior includes various forms of aggressive and violent behavior, violation of social norms, and delinquency174,175. It is associated with many negative health and life outcomes, such as alcohol and substance use, as well as criminal conviction. The study of antisocial behavior is of societal importance because of the high financial and social costs associated with it and its correlates176. It has been estimated that antisocial behavior is highly heritable, but many studies have also identified a multitude of environmental risk factors177.

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For example, antisocial behavior is associated with experiencing various forms of childhood and adolescent adversities, such as child maltreatment and socioeconomic stress178,179. This observation has led to great scientific interest in the question whether the effects of environmental risk factors are contingent on genetic differences. As is applicable for many other traits studied in psychology34,109, it has been suggested that antisocial behavior could be associated with an interaction between environmental adversity and alleles of the gene region 5-HTTLPR. However, few large-scale studies have investigated this hypothesis180, and it is necessary to further investigate that hypothesis to corroborate or reject some positive findings reported in the literature.

Individual contributions

The empirical chapters of this thesis are all based on collaborative work, and most include a large number of co-authors that all contributed in various ways, e.g., with data collection, data preparation, and cohort-level analyses. Authors that are not listed in this section contributed primarily to such efforts and details of their contributions can be found in the Online Supplementary Material accompanying the published studies. All authors critically reviewed and commented on the manuscripts prior to publication. Beyond the many contributing authors, the central analyses and writing of the manuscripts were performed by a small, central team of junior and senior authors. A selection of the analyses that I conducted myself or was particularly involved in are reported in the Supplementary Methods in this thesis. To my knowledge, Chapters 2, 3 and 5 are part of other PhD theses. The following paragraphs summarize and report the contributions of the central team of authors, including my own. Chapter 2 – Genome-wide association analyses of risk tolerance and risky behaviors in over one million individuals identify hundreds of loci and shared genetic influences In this study, I was the lead analyst and contributed substantially to the analyses and the writing of the manuscript. The study was conceived by Jonathan Beauchamp, Philipp Koellinger, and Daniel Benjamin. I conducted the GWAS analyses in the UKB, and the quality-control and meta-analyses. I performed the conditional analyses of the identified genetic associations, the investigation of long-range LD regions, candidate inversions, and 1000 Genomes structural variants, and I summarized the overlap across the various GWAS. Juan Ramon Gonzalez and Tõnu Esko contributed data to the investigation of candidate inversions. The population stratification, replication, and proxy-phenotype analyses were conducted by Edward Kong. I performed the SNP-based heritability analyses. The genetic correlation analyses were conducted by Robbee Wedow, with assistance from Mark Fontana. I performed polygenic prediction in the STR cohort, and the prediction analyses were led by Pietro Biroli, and Edward Kong, Robbee Wedow, Abdel Abdellaoui, Ronald de Vlaming, Mark Fontana, Michel Nivard also contributed to those analyses. The MTAG analyses were conducted by Pietro Biroli and Christian Zünd. Fleur Meddens led the bioinformatics analyses, and was assisted by Jacob Gratten and Maciej Trzaskowski, Anke Hammerschlag, Gerardus Meddens, and Pascal Timshel. Maël Lebreton conducted the review of the literature attempting to link risk taking to genes and biological pathways, and I performed the SNP-based validation analysis of these previous hypotheses. I prepared the majority of the figures, with assistance from Edward Kong and Stephen Tino. Additional analyses were performed by Aysu Okbay, Niels Rietveld, and Stephen Tino. David Cesarini, Jacob Gratten, and James Lee provided helpful advice and feedback on various aspects of the study design. Jonathan Beauchamp, Pietro Biroli, Edward Kong, Fleur Meddens, Robbee Wedow, and I made especially major contributions to the writing and editing of the manuscript.

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All the contributing authors are listed on p. 34. For a complete description of the author contributions, see Supplementary Material section 13 in Karlsson Linnér et al. (2019). 1 Chapter 3 – Genetic variants associated with subjective well-being, depressive symptoms, and neuroticism identified through genome-wide analyses: This study was the first large-scale GWAS I was involved in, and I made a less prominent contribution than in Chapter 2. The study was designed by Meike Bartels, Daniel Benjamin, David Cesarini, Jan-Emmanuel De Neve, Philipp Koellinger, and Robert Krueger. The quality control and meta-analyses were performed by Aysu Okbay and Bart Baselmans. GWAS analyses in the UKB were conducted by Mark Fontana. Jonathan Beauchamp and Patrick Turley performed tests for population stratification, developed the “quantity-quality tradeoff”, and conducted the Bayesian credibility analyses. Aysu Okbay and Bart Baselmans conducted the polygenic score prediction analyses. Mark Fontana, Patrick Turley, and Jonathan Beauchamp performed the genetic correlation analyses. Aysu Okbay and I conducted the proxy- phenotype and cross-phenotype enrichment analyses. The bioinformatics analyses were performed by Jonathan Beauchamp, Mark Fontana, Fleur Meddens, Michel Nivard, and Tõnu Esko. I prepared the majority of the figures and Fleur Meddens the majority of the tables. The inversion polymorphisms were analyzed by Harm-Jan Westra and Juan Ramon Gonzalez. Daniel Benjamin, Meike Bartels, Jonathan Beauchamp, David Cesarini, Jan-Emmanuel De Neve, Michel Nivard, Philipp Koellinger, Aysu Okbay, and Patrick Turley made especially major contributions to the writing and editing of the manuscript. All the contributing authors are listed on p. 73. For a complete list of author contributions, see Supplementary Material section 11 in Okbay et al. (2016). Chapter 4 – An epigenome-wide association study meta-analysis of educational attainment: In this study, I was the lead analyst together with Riccardo Marioni and Niels Rietveld. I contributed substantially to the analyses and the writing of the manuscript. The study was designed by Daniel Benjamin, Philipp Koellinger, Ian Deary, Niels Rietveld, and Riccardo Marioni. Niels Rietveld and I performed the EWAS quality control and meta-analyses. Riccardo Marioni performed the epigenetic clock analyses, and Niels Rietveld and I assisted those analyses. The polygenic prediction was carried out by Niels Rietveld, Riccardo Marioni, Andrew Simpkin, and Neil Davies. Niels Rietveld and I conducted the robustness analyses of smoking. Niels Rietveld and I designed and performed the enrichment analyses. I designed and performed the tissue-specific methylation analyses. The FUMA expression analysis was performed by Kyoko Watanabe. The methQTL GWAS were performed by Riccardo Marioni and Niels Rietveld, and I performed the subsequent quality control and meta-analysis. Daniel Benjamin, Riccardo Marioni, Niels Rietveld, and I made especially major contributions to the writing and editing of the manuscript. All the contributing authors are listed on p. 92. The authors not listed above were involved in data collection, data preparation, or cohort-level EWAS analyses. Chapter 5 – Meta-analysis of the serotonin transporter promoter variant (5-HTTLPR) in relation to adverse environment and antisocial behavior: In this study, I was the lead analyst together with Jorim Tielbeek. I contributed substantially to the analyses and the writing of the manuscript. The study was designed by Tinca Polderman, Arne Popma, and Danielle Posthuma. Jorim Tielbeek, Koko Beers, and I performed the data collection. The quality-assessment analysis was performed by Jorim Tielbeek and Koko Beers, with assistance from Tinca Polderman. Jorim Tielbeek and I performed the meta-analysis, the summary of the results, and the publication-bias analyses. I prepared the majority of the figures.

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Jorim Tielbeek and I made especially major contributions to the writing and editing of the manuscript.

Additional publications

During the PhD, I also contributed to the following studies: Bansal, V., et al. (2018). GWAS results for educational attainment aid in identifying genetic heterogeneity of schizophrenia. Nature Communications 9; 3078. Fiorito, G., et al. (2017). Social adversity and epigenetic aging: a multi-cohort study on socioeconomic differences in peripheral blood DNA methylation. Scientific Reports 24; 16266. Lee, JL., et al. (2018). Gene discovery and polygenic prediction from a genome-wide association study of educational attainment in 1.1 million individuals. Nature Genetics 50; 1112–1121. Nave, G., Jung, WH., Karlsson Linnér, R., Kabel, J., & Koellinger, PD. (2018). Are bigger brains smarter? Evidence from a large-scale pre-registered study. Psychological Science 30; 43–54. Meddens, SFW., et al. (Unpublished). Genomic analysis of diet composition finds novel loci and associations with health and lifestyle. Manuscript under consideration.

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References – Preface & Chapter 1 1

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24 Genome-wide association analyses of risk tolerance and risky behaviors in over one million individuals identify hundreds of loci and shared genetic influences

“Never was anything great achieved without danger.” Niccolo Machiavelli

“So we shall let the reader answer this question for himself: who is the happier man, he who has braved the storm of life and lived or he who has stayed securely on shore and merely existed?” Hunter S. Thompson

“The only way to find true happiness is to risk being completely cut open.” Chuck Palahniuk

Based on Karlsson Linnér et al. (2019). Nature Genetics. 2

Chapter 2

Abstract

Humans vary substantially in their willingness to take risks. In a combined sample of over one million individuals, we conducted genome-wide association studies (GWAS) of general risk tolerance, adventurousness, and risky behaviors in the driving, drinking, smoking, and sexual domains. Across all GWAS we identified hundreds of associated loci, including 99 loci associated with general risk tolerance. We report evidence of substantial shared genetic influences across risk tolerance and the risky behaviors: 46 of the 99 general risk tolerance loci contain a lead SNP for at least one of our other GWAS, and general risk tolerance is genetically correlated (   ~ 0.25 to 0.50) with a range of risky behaviors. Bioinformatics analyses imply that genes near general-risk-tolerance-associated SNPs are highly expressed in brain tissues and point to a role for glutamatergic and GABAergic . We found no evidence of enrichment for genes previously hypothesized to relate to risk tolerance.

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Introduction

Choices in important domains of life, including health, fertility, finance, employment, and social relationships, rarely have consequences that can be anticipated perfectly. The degree of 2 variability in possible outcomes is called risk. Risk tolerance—defined as the willingness to take risks, typically to obtain some reward—varies substantially across humans and has been actively studied in the behavioral and social sciences. An individual’s risk tolerance may vary across domains, but survey-based measures of general risk tolerance (e.g., “Would you describe yourself as someone who takes risks?”) have been found to be good all-around predictors of risky behaviors such as portfolio allocation, occupational choice, smoking, drinking alcohol, and starting one’s own business1–3. Twin studies have established that various measures of risk tolerance are moderately heritable ( , although estimates in the literature vary3–5). Discovery of specific genetic variants associated with general risk tolerance could provide insights into underlying biological pathways; advance our understanding of how genetic influences are amplified and dampened by environmental factors; enable the construction of polygenic scores (indexes of many genetic variants) that can be used as overall measures of genetic influences on individuals; and help distinguish genetic variation associated with general versus domain-specific risk tolerance. Although risk tolerance has been one of the most studied phenotypes in social science genetics, most claims of positive findings have been based on small-sample candidate gene studies (Supplementary Table 1), whose limitations are now appreciated6. To date, only two loci associated with risk tolerance have been identified in genome-wide association studies (GWAS)7,8. Here, we report results from large-scale GWAS of self-reported general risk tolerance (our primary phenotype) and six supplementary phenotypes: “adventurousness” (defined as the self- reported tendency to be adventurous vs. cautious); four risky behaviors: “automobile speeding propensity” (the tendency to drive faster than the speed limit), “drinks per week” (the average number of alcoholic drinks consumed per week), “ever smoker” (whether one has ever been a smoker), and “number of sexual partners” (the lifetime number of sexual partners); and the first principal component (PC) of these four risky behaviors, which we interpret as capturing the general tendency to take risks across domains. All seven phenotypes are coded such that higher phenotype values are associated with higher risk tolerance or risk taking. Table 2.1 lists, for each GWAS, the datasets we analyzed and the GWAS sample sizes. Results

Association analyses

All seven GWAS were performed in European-ancestry subjects; included controls for the top 10 (or more) principal components of the genetic relatedness matrix and for sex and birth year (Supplementary Table 2); and followed procedures described in a pre-specified analysis plan and in the Supplementary Note. In the discovery phase of our GWAS of general risk tolerance (n = 939,908), we conducted a GWAS using the UK Biobank (UKB, n = 431,126) and then performed a sample-size-weighted meta-analysis of those results with GWAS results from a sample of research participants from 23andMe (n = 508,782). The UKB measure of general risk tolerance is based on the question:

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“Would you describe yourself as someone who takes risks? Yes / No.” The 23andMe measure is based on a question about overall comfort taking risks, with five response options ranging from “very comfortable” to “very uncomfortable.” The genetic correlation9 between the UKB and 23andMe cohorts (  = 0.77, SE = 0.02) is smaller than one but high enough to justify our approach of pooling the two cohorts (see Section 2 in the Supplementary Note of ref. 10 for a theoretical demonstration of the merits of pooling cohorts despite moderate heterogeneity of phenotype measures). The Q-Q plot (Supplementary Fig. 1a) from the discovery GWAS exhibits substantial inflation (GC = 1.41). According to the estimated intercept from a linkage disequilibrium (LD) Score regression11, only a small share of this inflation (~5%) in test statistics is due to confounding biases such as cryptic relatedness and population stratification. To account for these biases, we inflated GWAS standard errors by the square root of the LD Score regression intercept12. We identified 124 approximately independent SNPs (pairwise r2 < 0.1) that attained genome- wide significance (P < 510–8). These 124 “lead SNPs” are listed in Supplementary Table 3 and shown in Figure 2.1a. All have coefficients of determination (R2’s) below 0.02%, and the SNP with the largest per-allele effect is estimated to increase general risk tolerance by ~0.026 standard deviations in our discovery sample (Supplementary Fig. 2). To test if the lead SNPs’ effect sizes are heterogeneous across the 23andMe and UKB cohorts, we generated an omnibus test statistic by summing Cochran’s Q statistics across all lead SNPs; consistent with our genetic correlation estimate of less than unity between the two cohorts, we rejected the null hypothesis of homogeneity (P = 4.3210–5; Supplementary Note). To define genomic loci around the lead SNPs, we took the physical regions containing all SNPs in LD (pairwise r2 > 0.6) with the lead SNPs and merged loci within 250 kb of each other; the 124 lead SNPs are located in 99 such loci (Supplementary Table 3). We supplemented those analyses with a conditional and joint multiple-SNP (COJO) analysis13, which identified 91 genome-wide significant “conditional associations” (Supplementary Table 3). In the replication phase of our GWAS of general risk tolerance (combined n = 35,445), we meta-analyzed summary statistics from ten smaller cohorts. Additional details on cohort-level phenotype measures are provided in Supplementary Table 4. The cohorts’ survey questions differ in terms of their exact wording and number of response categories, but all questions ask subjects about their overall or general attitudes toward risk. The genetic correlation9 between the discovery and replication GWAS is 0.83 (SE = 0.13). 123 of the 124 lead SNPs were available or well proxied by an available SNP in the replication GWAS results. Out of these 123 SNPs, 94 have a concordant sign (P = 1.710–9) and 23 are significant at the 5% level in one-tailed t tests (P = 4.510–8) (Supplementary Fig. 3). This empirical replication record closely matches theoretical projections that take into account sampling variation and the winner’s curse (Supplementary Note). In the UKB we tested and confirmed that a much higher fraction of males (34%) than females (19%) described themselves as risk tolerant on the general risk tolerance measure (t-test P <  ; Supplementary Fig. 4), consistent with much prior research14,15. We used bivariate LD Score regression12 to calculate the genetic correlation between GWAS performed separately in the sample of females and in the sample of males in the UKB. Our estimate (  = 0.822, SE = 0.033) is high enough to justify our approach of pooling males and females in our other analyses to maximize statistical power10. Nonetheless, our estimate is significantly smaller than unity, suggesting that the autosomal genetic factors contributing to general risk tolerance, while largely similar across sexes, are not identical.

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Our six supplementary GWAS—of adventurousness, the four risky behaviors, and their principal component (n = 315,894 to 557,923; Supplementary Tables 4-5)—were conducted using methods comparable to those in the primary GWAS, except that they had no replication phases and most involved a single large cohort. Supplementary Fig. 1 shows Q-Q plots and Supplementary Fig. 5 shows Manhattan plots. Table 2.1 provides a summary overview of the seven GWAS. We identified a total of 864 “lead 2 associations”: the sum total of the 124 general-risk-tolerance lead SNPs together with the 740 lead SNPs from the six supplementary GWAS. (These 864 lead associations were obtained by considering each of our seven phenotypes separately and using the standard genome-wide significance P value threshold of 510–8. If we instead consider the seven GWAS jointly and use a Bonferroni-corrected P value threshold of 7.1109 (= 5108/7), we obtain 566 lead associations across the seven GWAS.) Since we did not have the data to conduct replication analyses of the lead associations from the supplementary GWAS, we calculated the “maxFDR”16, a theoretical upper bound on the false discovery rate (FDR), for each GWAS. The maxFDR estimates were low across all GWAS (the highest estimate was 1.22103, for automobile speeding propensity), thus providing reassurance about the robustness of the lead associations. Applying our locus definition, we identified a total of 703 “locus associations”: the sum total of the 99 general-risk-tolerance loci together with the 604 loci from the supplementary GWAS (Supplementary Note). Pooling the loci corresponding to the 703 locus associations, and merging loci within 250 kb from each other, yields 444 distinct loci. COJO analyses13 identified a sum total of 655 conditional associations across all seven GWAS. (If we instead consider the seven GWAS jointly and use a Bonferroni-corrected P value threshold of 7.1109 (= 5108/7), we obtain 464 locus associations and 505 conditional associations across the seven GWAS.) We verified that the results of the COJO analyses are consistent with those from multiple regressions using individual-level genotype-dosage data from the UKB (Supplementary Note). Supplementary Tables 3 and 6-7 report the lead SNPs, the genomic loci, and the results of the COJO analyses. Table 2.1 also shows the SNP heritabilities17 of the seven phenotypes, calculated from the GWAS results; the SNP heritabilities range from ~0.05 (for general risk tolerance) to ~0.16 (for the first PC of the four risky behaviors). We note that 212 of the 864 lead associations are located within long-range LD regions18 or candidate inversions (i.e., genomic regions that are highly prone to inversion polymorphisms; Supplementary Note). Of these, only 109 are also conditional associations, and 46 are in loci that contain no conditional associations, thus indicating that many lead associations in the long- range LD regions or candidate inversions may tag causal variants that are also tagged by other lead associations. We discuss some of these regions in the next section.

Genetic overlap

There is substantial overlap across the results of our GWAS. For example, 46 of the 99 general- risk-tolerance loci contain a lead SNP of at least one of the other GWAS, and 72 of the 124 general-risk-tolerance lead SNPs are in weak LD (pairwise r2 > 0.1) with a lead SNP of at least one of the other GWAS (including 45 for adventurousness and 49 for at least one of the four risky behaviors or their first PC). To empirically assess if this overlap could be attributed to chance, we conducted resampling exercises under the null hypothesis that the lead SNPs of our supplementary GWAS are distributed independently of the general-risk-tolerance loci and lead SNPs. We strongly rejected this null hypothesis (P < 0.0001; Supplementary Note).

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Several long-range LD regions, candidate inversions, and LD blocks19 stand out for being associated both with general risk tolerance and with all or most of the supplementary phenotypes. We tested whether the signs of the lead SNPs located in these regions tend to be concordant across our primary and supplementary GWAS. We strongly rejected the null hypothesis of no concordance (P < 310–30; Supplementary Note), suggesting that these regions represent shared genetic influences, rather than colocalization of causal SNPs. Figure 2.1b and Supplementary Fig. 6 show local Manhattan plots for some of these long-range LD regions and candidate inversions. The long-range LD region18 on chromosome 3 (~83.4 to 86.9 Mb) contains lead SNPs from all seven GWAS as well as the most significant lead SNP from the general-risk-tolerance GWAS, rs993137 (P = 2.1410–40), which is located in the gene CADM2. Another long-range LD region, on chromosome 6 (~25.3 to 33.4 Mb), covers the HLA-complex and contains lead SNPs from all GWAS except drinks per week. Three candidate inversions on chromosomes 7 (~124.6 to 132.7 Mb), 8 (~7.89 to 11.8 Mb), and 18 (~49.1 to 55.5 Mb) contain lead SNPs from six, five, and all seven of our GWAS, respectively. Finally, four other LD blocks19 that do not overlap known long-range LD or candidate inversion regions each contain lead SNPs from five of our GWAS (including general risk tolerance). While many of the lead SNPs in these regions are not conditional associations, the above results regarding the numbers of GWAS with lead SNPs in these regions also hold if we only consider the conditional associations instead of the lead SNPs in those regions. The two long-range LD regions and the three candidate inversions have previously been found to be associated with numerous phenotypes, including many cognitive and neuropsychiatric phenotypes20. To investigate genetic overlap at the genome-wide level, we estimated genetic correlations with self-reported general risk tolerance using bivariate LD Score regression9. (For this and all subsequent analyses involving general risk tolerance, we used the summary statistics from the combined meta-analysis of our discovery and replication GWAS.) The estimated genetic correlations with our six supplementary phenotypes are all positive, larger than ~0.25, and highly significant (P < 2.310–30; Figure 2.2), indicating that SNPs associated with higher general risk tolerance also tend to be associated with riskier behavior. The largest estimated genetic correlations are with adventurousness (  = 0.83, SE = 0.01), number of sexual partners (0.52, SE = 0.02), automobile speeding propensity (0.45, SE = 0.02), and the first PC of the four risky behaviors (0.50, SE = 0.02). Our estimates of the genetic correlations between general risk tolerance and the supplementary risky behaviors are substantially higher than the corresponding phenotypic correlations (Supplementary Tables 8 and 9). Although measurement error partly accounts for the low phenotypic correlations, the genetic correlations remain considerably higher even after adjustment of the phenotypic correlations for measurement error. The comparatively large genetic correlations support the view that a general factor of risk tolerance partly accounts for cross-domain correlation in risky behavior21,22 and imply that this factor is genetically influenced. The lower phenotypic correlations suggest that environmental factors are more important contributors to domain-specific risky behavior23,24. To increase the precision of our estimates of the SNPs’ effects on general risk tolerance, we leveraged the high degree of genetic overlap across our phenotypes by conducting Multi-Trait Analysis of GWAS (MTAG)16. We used as inputs the summary statistics of our GWAS of general risk tolerance, of our first five supplementary GWAS (i.e., not including the first PC of the four risky behaviors), and of a previously published GWAS on lifetime use25 (Supplementary Note). MTAG increased the number of general-risk-tolerance lead SNPs from 124 to 312 (Supplementary Fig. 7 and Supplementary Table 10).

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We also estimated genetic correlations between general risk tolerance and 28 additional phenotypes (Figure 2.2 and in Supplementary Table 9). These included phenotypes for which we could obtain summary statistics from previous GWAS, as well as five phenotypes for which we conducted new GWAS. The estimated genetic correlations for the personality traits extraversion (  = 0.51, SE = 0.03), neuroticism (–0.42, SE = 0.04), and openness to experience (0.33, SE = 0.03) are significantly distinguishable from zero after Bonferroni correction and 2 are substantially larger in magnitude than previously reported phenotypic correlations26, pointing to shared genetic influences among general risk tolerance and these traits. After Bonferroni correction, we also found significant positive genetic correlations with the neuropsychiatric phenotypes ADHD, bipolar disorder, and schizophrenia. Viewed in light of the genetic correlations we found with some supplementary phenotypes and additional risky behaviors classified as externalizing (e.g., substance use, elevated sexual behavior, and fast driving), these results suggest the hypothesis that the overlap with the neuropsychiatric phenotypes is driven by their externalizing component27.

Polygenic prediction

We constructed polygenic scores of general risk tolerance to gauge their potential usefulness in empirical research (Supplementary Note). We used the Add Health, HRS, NTR, STR, UKB- siblings, and Zurich cohorts as validation cohorts (Supplementary Table 5 provides an overview of these cohorts; the UKB-siblings cohort comprised individuals with at least one full sibling in the UKB). For each validation cohort, we constructed the score using summary statistics from a meta-analysis of our discovery and replication GWAS that excluded the cohort (for the UKB-siblings cohort, we reran our UKB GWAS after excluding individuals from that cohort). Our measure of predictive power is the incremental R2 (or pseudo-R2) from adding the score to a regression of the phenotype on controls for sex, birth year, and the top ten principal components of the genetic relatedness matrix. Our preferred score was constructed with LDpred28. Our largest validation cohort (  35,000) is the UKB-siblings cohort. In that validation cohort, the score’s predictive power is 1.6% for general risk tolerance, 1.0% for the first PC of the four risky behaviors, 0.8% for number of sexual partners, 0.6% for automobile speeding propensity, and ~0.15% for drinks per week and ever smoker. Across our validation cohorts, in which other phenotypes are measured, the score is also predictive of several personality phenotypes and a suite of real-world measures of risky behaviors in the health, financial, career, and other domains (Supplementary Figs. 8-9 and Supplementary Tables 11-14). The incremental R2 we observe for general risk tolerance is consistent with our theoretical prediction, given the GWAS sample sizes, the SNP heritability of general risk tolerance (Table 2.1), and the imperfect genetic correlations across the GWAS and validation cohorts29,30 (Supplementary Note).

Biological annotation

To gain insights into the biological mechanisms through which genetic variation influences general risk tolerance, we conducted a number of bioinformatics analyses using the results of the combined meta-analysis of our discovery and replication GWAS of general risk tolerance. First, we systematically reviewed the literature that aimed to link risk tolerance to biological pathways (Supplementary Note). Our review covered studies based on candidate genes (i.e., specific genetic variants used as proxies for biological pathways), pharmacological manipulations, biochemical assays, genetic manipulations in rodents, as well as other research designs. Our review identified 132 articles that matched our search criteria (Supplementary Table 2.1). This previous work has focused on five main biological pathways: the steroid

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Chapter 2 hormone cortisol, the monoamines and serotonin, and the steroid sex hormones estrogen and testosterone. Using a MAGMA31 competitive gene-set analysis, we found no evidence that SNPs within genes associated with these five pathways tend to be more associated with general risk tolerance than SNPs in other genes (Supplementary Table 15). Furthermore, none of the other bioinformatics analyses we report below point to these pathways. We also examined the 15 most commonly tested autosomal genes within the dopamine and serotonin pathways, which were the focus of most of the 34 candidate-gene studies identified by our literature review. We verified that the SNPs available in our GWAS results tag most of the genetic variants typically used to test the 15 genes. Across one SNP-based test and two gene-based tests, we found no evidence of non-negligible associations between those genes and general risk tolerance (Figure 2.1c and Supplementary Table 16). (We note, however, that some brain regions identified in analyses we report below are areas where dopamine and serotonin play important roles.) Second, we performed a MAGMA31 gene analysis to test each of ~18,000 protein-coding genes for association with general risk tolerance (Supplementary Note). After Bonferroni correction, 285 genes were significant (Supplementary Fig. 10 and Supplementary Table 17). To gain insight into the functions and expression patterns of these 285 genes, we looked them up in the Gene Network32 co-expression database. Third, to identify relevant biological pathways and identify tissues in which genes near general- risk-tolerance-associated SNPs are expressed, we applied the software tool DEPICT33 to the SNPs with P values less than 10–5 in our GWAS of general risk tolerance (Supplementary Note). Both the Gene Network and the DEPICT analyses separately point to a role for glutamate and GABA , which are the main excitatory and inhibitory neurotransmitters in the brain, respectively34 (Figure 2.3a and Supplementary Tables 18 and 19). To our knowledge, with the exception of a recent study35 prioritizing a much larger number of genes and pathways, no published large-scale GWAS of cognition, personality, or neuropsychiatric phenotypes has pointed to clear roles both for glutamate and GABA (although glutamatergic neurotransmission has been implicated in recent GWAS of schizophrenia36 and major depression37). Our results suggest that the balance between excitatory and inhibitory neurotransmission may contribute to variation in general risk tolerance across individuals. The Gene Network and the DEPICT tissue enrichment analyses also both separately point to enrichment of the prefrontal cortex and the basal ganglia (Figure 2.3b and Supplementary Tables 18, 20, and 21). The cortical and subcortical regions highlighted by DEPICT include some of the major components of the cortical-basal ganglia circuit, which is known as the in human and non-human primates and is critically involved in learning, motivation, and decision-making, notably under risk and uncertainty38,39. We caution, however, that our results do not point exclusively to the reward system. Lastly, we used stratified LD Score regression40 to test for the enrichment of SNPs associated with marks in 10 tissue or cell types (Supplementary Note). Central nervous system tissues are the most enriched, accounting for 44% (SE = 3%) of the heritability while comprising only 15% of the SNPs (Supplementary Fig. 11a and Supplementary Table 22). Immune/hematopoietic tissues are also significantly enriched. While a role for the immune system in modulating risk tolerance is plausible given prior evidence of its involvement in several neuropsychiatric disorders36,37, future work is needed to confirm this result and to uncover specific pathways that might be involved.

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Discussion

Our results provide insights into biological mechanisms that influence general risk tolerance. Our bioinformatics analyses point to the role of gene expression in brain regions that have been identified by neuroscientific studies on decision-making, notably the prefrontal cortex, basal ganglia, and midbrain, thereby providing convergent evidence with that from neuroscience38,39. Yet our analyses failed to find evidence for the main biological pathways that had been 2 previously hypothesized to influence risk tolerance. Instead, our analyses implicate genes involved in glutamatergic and GABAergic neurotransmission, which were heretofore not generally believed to play a noteworthy role in risk tolerance. Although our focus has been on the genetics of general risk tolerance and risky behaviors, environmental and demographic factors account for a substantial share of these phenotypes’ variation. We observe sizeable effects of sex and age on general risk tolerance in the UKB data (Supplementary Fig. 4), and life experiences have been shown to affect both measured risk tolerance and risky behaviors (e.g., refs. 41,42). The GWAS results we have generated will allow researchers to construct and use polygenic scores of general risk tolerance to measure how environmental, demographic, and genetic factors interact with one another. For the behavioral sciences, our results bear on an ongoing debate about the extent to which risk tolerance is a “domain-general” as opposed to a “domain-specific” trait. Low phenotypic correlations in risk tolerance across decision-making domains have been interpreted as supporting the domain-specific view23,24. Across the risky behaviors we study, we found that the genetic correlations were considerably higher than the phenotypic correlations (even after the latter are corrected for measurement error) and that many lead SNPs are shared across our phenotypes. These observations suggest that the low phenotypic correlations across domains are due to environmental factors that dilute the effects of a genetically-influenced domain- general factor of risk tolerance.

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Chapter 2

Authors – Chapter 2

Richard Karlsson Linnér, Pietro Biroli, Edward Kong, S Fleur W Meddens, Robbee Wedow, Mark Alan Fontana, Maël Lebreton, Stephen P Tino, Abdel Abdellaoui, Anke R Hammerschlag, Michel G Nivard, Aysu Okbay, Cornelius A Rietveld, Pascal N Timshel, Maciej Trzaskowski, Ronald de Vlaming, Christian L Zünd, Yanchun Bao, Laura Buzdugan, Ann H Caplin, Chia-Yen Chen, Peter Eibich, Pierre Fontanillas, Juan R Gonzalez, Peter K Joshi, Ville Karhunen, Aaron Kleinman, Remy Z Levin, Christina M Lill, Gerardus A Meddens, Gerard Muntané, Sandra Sanchez-Roige, Frank J van Rooij, Erdogan Taskesen, Yang Wu, Futao Zhang, 23andMe Research Team, eQTLgen Consortium, International Cannabis Consortium, Social Science Genetic Association Consortium, Adam Auton, Jason D Boardman, David W Clark, Andrew Conlin, Conor C Dolan, Urs Fischbacher, Patrick J F Groenen, Kathleen Mullan Harris, Gregor Hasler, Albert Hofman, Mohammad A Ikram, Sonia Jain, Robert Karlsson, Ronald C Kessler, Maarten Kooyman, James MacKillop, Minna Männikkö, Carlos Morcillo-Suarez, Matthew B McQueen, Klaus M Schmidt, Melissa C Smart, Matthias Sutter, A Roy Thurik, André G Uitterlinden, Jon White, Harriet de Wit, Jian Yang, Lars Bertram, Dorret I Boomsma, Tõnu Esko, Ernst Fehr, David A Hinds, Magnus Johannesson, Meena Kumari, David Laibson, Patrik K E Magnusson, Michelle N Meyer, Arcadi Navarro, Abraham A Palmer, Tune H Pers, Danielle Posthuma, Daniel Schunk, Murray B Stein, Rauli Svento, Henning Tiemeier, Paul R H J Timmers, Patrick Turley, Robert J Ursano, Gert G Wagner, James F Wilson, Jacob Gratten, James J Lee, David Cesarini, Daniel J Benjamin, Philipp D Koellinger, and Jonathan P Beauchamp

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References – Chapter 2

1. Dohmen, T. et al. Individual risk attitudes: Measurement, determinants, and behavioral consequences. J. Eur. Econ. Assoc. 9, 522–550 (2011). 2. Falk, A., Dohmen, T., Falk, A. & Huffman, D. The nature and predictive power of 2 preferences: Global evidence. IZA Discuss. Pap. (2015). 3. Beauchamp, J. P., Cesarini, D. & Johannesson, M. The psychometric and empirical properties of measures of risk preferences. J. Risk Uncertain. 54, 203–237 (2017). 4. Cesarini, D., Dawes, C. T., Johannesson, M., Lichtenstein, P. & Wallace, B. Genetic variation in preferences for giving and risk taking. Q. J. Econ. 124, 809–842 (2009). 5. Harden, K. P. et al. Beyond dual systems: A genetically-informed, latent factor model of behavioral and self-report measures related to adolescent risk-taking. Dev. Cogn. Neurosci. 25, 221–234 (2017). 6. Hewitt, J. K. Editorial policy on candidate gene association and candidate gene-by- environment interaction studies of complex traits. Behav. Genet. 42, 1–2 (2012). 7. Day, F. R. et al. Physical and neurobehavioral determinants of reproductive onset and success. Nat. Genet. 48, 617–623 (2016). 8. Strawbridge, R. J. et al. Genome-wide analysis of self-reported risk-taking behaviour and cross-disorder genetic correlations in the UK Biobank cohort. Transl. Psychiatry 8, 1–11 (2018). 9. Bulik-Sullivan, B. K. et al. An atlas of genetic correlations across human diseases and traits. Nat. Genet. 47, 1236–1241 (2015). 10. Okbay, A. et al. Genetic variants associated with subjective well-being, depressive symptoms, and neuroticism identified through genome-wide analyses. Nat. Genet. 48, 624–633 (2016). 11. Bulik-Sullivan, B. K. et al. LD Score regression distinguishes confounding from polygenicity in genome-wide association studies. Nat. Genet. 47, 291–295 (2015). 12. Bulik-Sullivan, B. K. et al. LD Score regression distinguishes confounding from polygenicity in genome-wide association studies. Nat. Genet. 47, 291–295 (2015). 13. Yang, J. et al. Conditional and joint multiple-SNP analysis of GWAS summary statistics identifies additional variants influencing complex traits. Nat. Genet. 44, 369–375 (2012). 14. Byrnes, J. P., Miller, D. C. & Schafer, W. D. Gender differences in risk taking: a meta- analysis. Psychol. Bull. 125, 367–383 (1999). 15. Croson, R. & Gneezy, U. Gender Differences in Preferences. J. Econ. Lit. 47, 448–474

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(2009). 16. Turley, P. et al. Multi-trait analysis of genome-wide association summary statistics using MTAG. Nat. Genet. 50, 229–237 (2018). 17. Shi, H., Kichaev, G. & Pasaniuc, B. Contrasting the genetic architecture of 30 complex traits from summary association data. bioRxiv 99, 1–28 (2016). 18. Price, A. L. et al. Long-range LD can confound genome scans in admixed populations. Am. J. Hum. Genet. 83, 132–139 (2008). 19. Berisa, T. & Pickrell, J. K. Approximately independent linkage disequilibrium blocks in human populations. Bioinformatics 32, 283–285 (2016). 20. Welter, D. et al. The NHGRI GWAS Catalog, a curated resource of SNP-trait associations. Nucleic Acids Res. 42, D1001-1006 (2014). 21. Einav, B. L., Finkelstein, A., Pascu, I. & Cullen, M. R. How general are risk preferences? Choices under uncertainty in different domains. Am. Econ. Rev. 102, 2606–2638 (2016). 22. Frey, R., Pedroni, A., Mata, R., Rieskamp, J. & Hertwig, R. Risk preference shares the psychometric structure of major psychological traits. Sci. Adv. 3, e1701381 (2017). 23. Weber, E. U., Blais, A. E. & Betz, N. E. A domain-specific risk-attitude scale: Measuring risk perceptions and risk behaviors. J. Behav. Decis. Mak. J. Behav. Dec. Mak. 15, 263– 290 (2002). 24. Hanoch, Y., Johnson, J. G. & Wilke, A. Domain specificity in experimental measures and participant recruitment: an application to risk-taking behavior. Psychol. Sci. 17, 300– 304 (2006). 25. Stringer, S. et al. Genome-wide association study of lifetime cannabis use based on a large meta-analytic sample of 32,330 subjects from the International Cannabis Consortium. Transl. Psychiatry 6, e769 (2016). 26. Becker, A., Deckers, T., Dohmen, T., Falk, A. & Kosse, F. The relationship between economic preferences and psychological personality measures. Annu. Rev. Econom. 4, 453–478 (2012). 27. Krueger, R. F. et al. Etiologic connections among substance dependence, antisocial behavior and personality: Modeling the externalizing spectrum. J. Abnorm. Psychol. 111, 411–424 (2002). 28. Vilhjálmsson, B. J. et al. Modeling linkage disequilibrium increases accuracy of polygenic risk scores. Am. J. Hum. Genet. 97, 576–592 (2015). 29. Daetwyler, H. D., Villanueva, B. & Woolliams, J. A. Accuracy of predicting the genetic risk of disease using a genome-wide approach. PLoS One 3, e3395 (2008).

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30. de Vlaming, R. et al. Meta-GWAS Accuracy and Power (MetaGAP) calculator shows that hiding heritability is partially due to imperfect genetic correlations across studies. PLoS Genet. 13, e1006495 (2017). 31. de Leeuw, C. A., Mooij, J. M., Heskes, T. & Posthuma, D. MAGMA: Generalized gene- set analysis of GWAS data. PLoS Comput. Biol. 11, 1–19 (2015). 2 32. Fehrmann, R. S. N. et al. Gene expression analysis identifies global gene dosage sensitivity in cancer. Nat. Genet. 47, 115–125 (2015). 33. Pers, T. H. et al. Biological interpretation of genome-wide association studies using predicted gene functions. Nat. Commun. 6, 5890 (2015). 34. Petroff, O. A. C. GABA and glutamate in the human brain. Neurosci. 8, 562–573 (2002). 35. Lee, J. et al. Gene discovery and polygenic prediction from a 1.1-million-person GWAS of educational attainment. Nat. Genet. 50, 1112–1121 (2018). 36. Ripke, S. et al. Biological insights from 108 schizophrenia-associated genetic loci. Nature 511, 421–427 (2014). 37. Wray, N. R. et al. Genome-wide association analyses identify 44 risk variants and refine the genetic architecture of major depression. bioRxiv (2017). doi:https://doi.org/10.1101/167577 38. Haber, S. N. & Knutson, B. The reward circuit: linking primate anatomy and human imaging. Neuropsychopharmacology 35, 4–26 (2010). 39. Tobler, P. N. & Weber, E. U. in 149–172 (Elsevier, 2014). doi:10.1016/B978-0-12-416008-8.00009-7 40. Finucane, H. K. et al. Partitioning heritability by functional annotation using genome- wide association summary statistics. Nat. Genet. 47, 1228–1235 (2015). 41. Sahm, C. R. How much does risk tolerance change? Q. J. Financ. 2, 1250020 (2012). 42. Malmendier, U. & Nagel, S. Depression babies: Do macroeconomic experiences affect risk taking? Q. J. Econ. 126, 373–416 (2011). 43. Frey, B. J. & Dueck, D. Clustering by passing messages between data points. Science 315, (2007).

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Figures and tables – Chapter 2

a

bc

Figure 2.1. Manhattan plots. In all panels, the x-axis is chromosomal position; the y-axis is the GWAS P value on a log10 scale (based on a two-tailed z-test); each lead SNP is marked by a red “”; each conditional association is marked by a red “o”; and each SNP that is both a lead SNP and a conditional association is marked by a red “”. a, Manhattan plots for the discovery GWAS of general risk tolerance (n = 939,908). b, Local Manhattan plots of a long- range LD region on chromosome 3 and a candidate inversion on chromosome 18 that contain lead SNPs for all seven of our GWAS. The gray background marks the locations of long-range LD or candidate inversion regions. c, Local Manhattan plots of the areas around the 15 most commonly tested candidate genes in the prior literature on the genetics of risk tolerance. Each local plot shows all SNPs within 500 kb of the gene’s borders that are in weak LD    with a SNP in the gene. The 15 plots are concatenated and shown together in the panel, divided by the black vertical lines. The 15 genes are not particularly strongly associated with general risk tolerance or the risky behaviors, as can be seen by comparing the results within each row across panels b and c (the three rows correspond to the GWAS of general risk tolerance, adventurousness (n = 557,923), and the first PC of the four risky behaviors (n = 315,894)).

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Discovering the genetic architecture of the mind

2

Figure 2.2. Genetic correlations with general risk tolerance. The genetic correlations were estimated using bivariate LD Score (LDSC) regression9. Error bars show 95% confidence intervals. For the supplementary phenotypes and the additional risky behaviors, green bars represent significant estimates with the expected signs, where higher risk tolerance is associated with riskier behavior. For the other phenotypes, blue bars represent significant estimates. Light green and light blue bars represent genetic correlations that are statistically significant at the 5% level, and dark green and dark blue bars represent correlations that are statistically significant after Bonferroni correction for 35 tests (the total number of phenotypes tested). Grey bars represent correlations that are not statistically significant at the 5% level. The two dotted vertical lines indicate genetic correlations of –0.5 and 0.5, respectively. All significance tests are two-sided.

39 Chapter 2

a

b Frontal cortex (BA9) 5 Anterior cingulate cortex (BA24) Cortex (basal ganglia) 4 Caudate (basal ganglia) Cerebellum

Amygdala 3 Cerebellar hemisphere value P  10 2 log <

1

0 Cells Artery Brain Breast C olon Heart Lung Nerve Skin Adipose Muscle Pituitary TestisThyroid Esophagus Pancreas Stomach Adrenal Gland Tissues Whole Blood

Figure 2.3. Results from selected biological analyses. a, DEPICT gene-set enrichment diagram. We identified 93 reconstituted gene sets that are significantly enriched (FDR < 0.01) for genes overlapping DEPICT-defined loci associated with general risk tolerance; using the Affinity Propagation method43, these were grouped into the 13 clusters displayed in the graph. Each cluster was named after its exemplary gene set, as chosen by the Affinity Propagation tool, and each cluster’s color represents the permutation P value of its most significant gene set. The “ part” cluster includes the gene set “glutamate receptor activity,” and several members of the “GABAA receptor activation” cluster are defined by gamma-aminobutyric acid signaling. Overlap between the named representatives of two clusters is represented by an edge. Edge width represents the Pearson correlation  between the two respective vectors of gene membership scores ( < 0.3, no edge; 0.3   < 0.5, thin edge; 0.5   < 0.7, intermediate edge;   0.7, thick edge). b, Results of DEPICT tissue enrichment analysis using GTEx data. The panel shows whether the genes overlapping DEPICT-defined loci associated with general risk tolerance are significantly overexpressed (relative to genes in random sets of loci matched by gene density) in various tissues. Tissues are grouped by organ or tissue type. The orange bars correspond to tissues with significant overexpression (FDR < 0.01). The y-axis is the significance on a log10 scale. See Supplementary Note for additional details.

40 Discovering the genetic architecture of the mind ; 13 is ”: mean ”: mean   association mate of the the of mate independent independent 2 SE) (

2 h SNP approximately approximately sample size; “Mean using 1000 Genomes phase 3 3 phase 1000 Genomes using

17 assoc. # cond. # cond. associations in the COJO analys in the COJO associations GWAS

”: n # loci # loci

lead SNPs”: number ofSNPs”: lead SNPs # lead lead # ) SE ( intercept LD Score LD Score of general risk“ tolerance. general of

 ond. number assoc.”: of conditional  Mean Mean le frequency (MAF) greater than 0.01; Scorefrequency intercept”: “LD (MAF)esti than le greater upplementary phenotypes. Replication analysis of the lead SNPs’ lead SNPs’ of the analysis Replication upplementary phenotypes.

n 975,353 975,353 1.87 557,923 1.98 (0.01) 1.04 414,343 132 (0.01) 1.05 1.61 167 107 (0.01) 1.03 137 97 85 126 (0.001) 0.045 62 (0.002) 0.098 61 (0.003) 0.085 370,711 370,711 1.77 315,894 1.77 (0.01) 1.04 (0.01) 1.05 117 106 97 89 88 84 (0.003) 0.128 (0.004) 0.156 Estimator from Summary Statistics (HESS) method (HESS) Statistics Summary from Estimator 518,633 1.97 (0.01) 1.05 223 183 172 (0.003) 0.109 44 ry; “repl.”: replication; “indep.”: ry; independent. “repl.”: replication; using HapMap3 SNPs with MAF greater with MAF than 0.01; SNPs “# HapMap3 using 11 only conducted for GWAS the discovery UKB; 23andMe; 10 indep. 10 23andMe; UKB; cohorts GWAS Cohorts analyzed analyzed Cohorts GWAS < 0.1) lead SNPs; “# loci”: number of associated loci; “# c associated of “# loci”: number lead SNPs; < 0.1) 2 r ”: SNP heritability estimated with the Heritability the with estimated SNP heritability ”: 2 h First PC of the four risky behaviors behaviors risky four the of PC First UKB General risk tolerance (disc. GWAS) GWAS) (disc. tolerance risk General 23andMe UKB; GWAS) (repl. tolerance risk General cohorts 10 indep. repl.) + (disc. tolerance risk General 939,908 35,445 propensity speeding Automobile 1.85 1.03 (0.01) 1.04 UKB (0.07) 1.00 124 0 99 0 91 (0.001) 0.046 404,291 0 1.53 (0.01) 1.03 42 -- 36 33 (0.003) 0.079 Adventurousness 23andMe 23andMe Adventurousness Drinks week per smoker Ever UKB Consortium TAG UKB; Number of sexual partners partners sexual of Number UKB (pairwise (pairwise SNPs with MAF greater than discove “disc.”: 0.05; GWAS chi-squared statistics across SNPs statistics HapMap3 with minor alle GWAS chi-squared regression Score LD from a intercept “SNP Table results GWAS 2.1 | s our and primary GWAS of the overview of an provides The table results in independent cohorts was was in independent cohorts results

41 Chapter 2

Chapter 2 Supplementary Methods

Overview of main GWAS

All analyses were performed at the cohort level according to a pre-specified and publicly archived analysis plan1. The original analysis plan was archived on February 4, 2016. For self- reported general risk tolerance, the analysis plan specified that the discovery GWAS would be conducted in the UKB and that the replication would be carried out in a meta-analysis of all other cohorts. We updated the analysis plan on November 9, 2016 to include the analysis of the four risky behaviors and their first PC. For these phenotypes, the analysis plan specified that the GWAS would be conducted in the UKB. We did not attempt replication for these phenotypes. We updated the analysis plan a second time on July 10, 2017 to add the 23andMe cohort to the discovery GWAS of self-reported general risk tolerance. Two minor updates were made on August 7 and September 14, 2017 to specify that follow-up analyses (such as polygenic prediction) would be performed, whenever possible, on a meta-analysis combining the discovery and replication GWAS of general risk tolerance, and that we would add a GWAS of adventurousness alongside the primary GWAS of general risk tolerance and the other supplementary GWAS. Cohorts other than the UKB could join this study by first supplying descriptive statistics and thereafter GWAS summary statistics from GWAS of self-reported general risk tolerance in November 2015 and December 2015, respectively. Two additional cohorts, Army STARRS and VHSS, joined the study later and provided descriptive and GWAS summary statistics in late 2016. Summary statistics were uploaded to a central, secure server and subsequently meta- analyzed. The lead PI of each cohort affirmed that the results contributed to the study were based on analyses approved by the local Research Ethics Committee and/or Institutional Review Board responsible for overseeing research. All participants provided written informed consent. We also obtained the descriptive and GWAS summary statistics from GWAS of self- reported general risk tolerance and adventurousness from 23andMe in late July 2017. An overview of the participating cohorts is reported in Supplementary Table 5. The analysis plan instructed all cohorts to limit the analysis to individuals of European ancestry, to exclude individuals with missing covariates, to remove samples that displayed a SNP call rate of less than 95%, and to apply cohort-specific standard quality control filters before imputation. The cohort-specific standard quality control filters are reported in Supplementary Table 24. GWAS were limited to the 22 autosomes. The cohorts were required to provide unfiltered GWAS summary statistics including the following information for each SNP: chromosome and base-pair position, rsID, effect-coded allele, other allele, sample size per SNP, coefficient estimate (beta), standard error of the coefficient estimate, P value of the association uncorrected for genomic control, effect-coded allele frequency (EAF), imputation status, imputation quality, and Hardy-Weinberg equilibrium exact test P value for directly genotyped markers. The analysis plan included power calculations assuming that 100,000 individuals in the UKB answered “Yes” (“cases”) to the general-risk-tolerance question, and 270,000 individuals answered “No” (“controls”). Under this assumption, our study would have 73% power to detect single nucleotide polymorphisms (SNPs) with a minor allele frequency (MAF) of 0.3 and an –8 odds ratio of 1.05 with a genome-wide significance threshold of P = 510 .

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For general risk tolerance, the final sample size for the “discovery GWAS” meta-analysis of the UKB and 23andMe cohorts was 939,908 individuals. Replication was performed in a meta- analysis of 10 independent cohorts from seven studies totaling 35,445 individuals. We will henceforth refer to this meta-analysis of the 10 replication cohorts as the “replication GWAS.” The follow-up analyses we describe in the following Supplementary Note sections were performed with GWAS summary statistics from a meta-analysis combining the discovery and replication GWAS (n = 975,353), except where otherwise noted. 2 For adventurousness, GWAS summary statistics from 23andMe were analyzed (n = 557,923). For three of the four risky behaviors, namely automobile speeding propensity (n = 404,291), drinks per week (n = 414,343), and number of sexual partners (n = 370,711), and for the first PC of the four risky behaviors (n = 315,894), GWAS were conducted in the UKB only, as specified in the updated analysis plan. For the remaining risky behavior, ever smoker, we meta- analyzed the summary statistics from the UKB GWAS (n = 444,598) with those from the Tobacco, Alcohol and Genetics (TAG) Consortium2 (n = 74,035), leading to a total sample size of 518,633 (the TAG consortium refers to the ever smoker phenotype as "smoking initiation").

Genotyping and imputation

Genotypingd was performed using a range of common, commercially available genotyping arrays. An overview of the genotyping and imputation procedure is provided in Supplementary Table 24. The participating cohorts were encouraged to use their standard quality-control protocols before imputation, as long as the applied filters satisfied the minimum requirements specified in the analysis plan (SNP call rate > 95%, HWE exact test P value > 10– 6, MAF > 1%). For the UKB, different filters were used, following ref.3. The cohorts, except for 23andMe, Army STARRS, BASE-II, UKB, and VHSS imputed markers using the 1000 Genomes phase 1 reference panel (March 2012 release version 3). 23andMe used the 1000 Genomes phase 1 (September 2013 haplotype release). Army STARSS used the 1000 Genomes phase 1 (August 2012 haplotype release). BASE-II used the more recent reference panel 1000 Genomes phase 3 (October 2014 haplotype release version 5). UKB used a customized reference panel based on the Haplotype Reference Consortium release 1.1 combined with the UK10K haplotype reference panel4. VHSS used the Haplotype Reference Consortium release 1.15. All genetic positions reported in this study are denoted with those of the Genome Reference Consortium’s human assembly 37 (GRCh37, sometimes referred to as the National Center for Biotechnology Information hg19). Association analyses

Cohorts were encouraged to exclude individuals with SNP call rates less than 95%, with excessive autosomal heterozygosity, and with sex mismatch. Family-based cohorts were informed to control for family structure either with mixed linear modeling or with a procedure selecting only one individual in each pair that displayed relatedness greater than 5% in a genetic relatedness matrix. The genome-wide association analysis performed in each cohort estimated the following regression for each SNP, as in Okbay et al. 20166:

d The UKB genotype data was handled with QCtool, available at http://www.well.ox.ac.uk/~gav/qctool/#overview

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 6 < 0 : 16 :'&)+:()*:&),:%6 where 6 is the phenotype for individual , 6 is the number of effect-coded alleles of the e SNP , '&) is a vector of principal components of the genetic relatedness matrix after application of the pre-imputation filters described above, and () is a vector of control variables. In all cohorts, ( included controls for sex and birth year. In most cohorts (including the 23andMe cohort), these included sex, birth year, birth year squared, birth year cubed, and the interactions between sex and the three birth-year variables; in the UKB, these included sex-specific birth year fixed effects. &) is a vector containing cohort-specific controls and technical covariates (such as dummy variables for genotyping array and genotyping batches) that are recommended in the analysis plan. All associations were performed with males and females pooled. A summary of the GWAS association models and control variables for each cohort is reported in Supplementary Table 2. In practice, the phenotype was often residualized by first regressing the phenotype on the control variables, and the residualized phenotype was then regressed on the genotypes. This approach leads to almost identical results as estimating the full regression model directly while drastically reducing the computation time needed for the GWAS.

Linear mixed models in the UKB

The association analyses in the UKB were performed with linear mixed models (LMM) with the BOLT-LMM v2.2 software7. The benefit of LMM is that the method accounts for cryptic relatedness and population structure, which allows the inclusion of related individuals in the sample, thereby yielding a larger sample size and greater statistical power. Using LMM is computationally intensive, and the BOLT algorithm is a new method that makes LMM analysis of hundreds of thousands of individuals computationally feasible. The method requires a set of SNPs to be included in the genetic variance component, and we included 483,680 directly genotyped bi-allelic autosomal SNPs with MAF > 0.005 and HWE P value > 10–6. We included individuals based on self-reported ancestry, specifically those who self-reported to be of “white” ancestry (i.e., self-reported white, British, Irish, or any other white background). In addition, we limited the GWAS to individuals for whom the value of the first principal component of the genetic relatedness matrix was less than “0,” which identifies the cluster of individuals of European ancestry. We dropped individuals whose reported sex did not match their genetic sex, individuals with putative sex chromosome aneuploidy, individuals that did not pass the UKB internal genotype quality control, and individuals with missing values. Main reference panel

The full release of the UK Biobank genetic data was imputed with haplotypes from the Haplotype Reference Consortium v1.1 (HRC) and the UK10K haplotype reference panel4. A recommendation was communicated soon after the release of the genotype data in July 2017. It was recommended that only SNPs available in the HRC be used for analysis, because a subset of variants imputed from the UK10K reference panel have wrongfully imputed genomic positions, while none of the HRC SNPs are affected. We therefore used the HRC v.1.1 as the reference panel for quality control of the GWAS summary statistics, and to determine the independence of significant SNPs. The following section describes our quality control of the

e For imputed SNPs we used best-guess data for samples that were imputed with IMPUTE46, and dosage data for samples that were imputed with MaCH/Minimac47.

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HRC whole-genome sequence data (WGS) when constructing the reference panel. We will hereafter refer to the resulting reference panel as the “main reference panel.”

Quality-control of the main reference panel

The HRC haplotypes were downloaded from the European Genome-phenome Archive (EGA) on August 1, 2017. Strict internal quality control had already been applied to the WGS data, as 2 described in-depth elsewhere5, and we restricted the main reference panel to variants that passed all pre-applied genotype call filters (i.e., variants whose VCF FILTER status is “PASS”; these pre-applied filters included, among other filters, a filter to remove variants with minor allele count (MAC)  5.) Before our internal QC the WGS data contained 40,405,506 autosomal and X-chromosome SNPs. The following protocol was restricted to the 39,131,579 autosomal SNPs because the pre-registered analysis plan restricts our analyses to the autosomes. The HRC reference panel does not include any structural variants, such as INDELs5. We performed a series of best-practice alignments of the WGS data for consistent and unique identification of variants8 with the open-source software BCFtoolsf created by the Wellcome Trust Sanger Institute. Because PLINK cannot properly handle truly multi-allelic variants, we split multi-allelic variants into multiple bi-allelic variants. We then confirmed that all reference alleles and genomic positions matched the Genome Reference Consortium Human genome build 37 (GRCh37)9. To avoid issues with chromosomal positions mapping to multiple NCBI marker IDs (rsIDs), all rsIDs were removed, and all variants were given a unique identifier (henceforth referred to the as the “unique ID”) in the form of chromosome, base-pair position, reference allele, and alternative allele, separated by colons (e.g., 1:123456:C:T). Using this format for variant IDs, together with the alignment to the reference genome GRCh37, ensures a unique representation of all SNPs and a lack of duplicate variants with switched reference alleles (e.g., 1:123456:C:T and 1:123456:T:C). To avoid possible issues with inconsistencies with the UK10K haplotype reference in future work that may use that reference, we investigated possible strand and allele issues across the reference panels. By comparing the reference alleles and allele frequencies we found 24,394 variants with inconsistent alleles, and we decided to drop these from the main reference panel so that they would be removed during QC of the GWAS summary statistics. We converted the VCF data to PLINK binary format with PLINK v.1.9b3.4610, and we removed all multi-allelic variants (without retaining any of the multi-allelic variants coded as bi-allelic SNPs). Monomorphic SNPs (i.e., SNPs with MAF = 0) were kept in the reference panel as recommended5. We thereafter excluded one member of each pair of individuals with genomic relatedness greater than 0.025 from the sample, which removed 4,917 individuals of the 22,691 individuals for whom data were available for all autosomes in the VCF data. In summary, the main reference panel consists of 17,774 individuals and includes 38,889,224 bi-allelic autosomal SNPs that passed QC. rsID mapping

Since rsIDs were removed from the SNPs to ensure unique identification, we created a map file so that each SNP could be assigned an rsID after meta-analysis, which is performed on the unique ID format described above (e.g., 1:123456:C:T). The map file contains the rsIDs from the HRC v1.1 sites list, and because we removed all multi-allelic variants there are no duplicate rsIDs.

f BCFtools can be downloaded here: http://samtools.github.io/bcftools/bcftools.html

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UK Biobank genotyping arrays

Combining data from the UK BiLEVE and the UK Biobank Axiom arrays

The participants of the UKB were genotyped with two different but similar genotyping arrays4,11–13. UKB participants who were enrolled in the UK BiLEVE study (a study of smoking behavior, lung function, and chronic obstructive pulmonary disease11) were genotyped with the UK BiLEVE array (n ~ 50,000), and the remaining participants were genotyped with the UK Biobank Axiom array (n ~ 400,000). Henceforth, we will refer to these two sets of UK Biobank participants as the UK BiLEVE and the UKB Axiom cohorts. While the UK Biobank (UKB) is a population-based study12 collected during 2006–2010, the sample selection for the UK BiLEVE study began at a later stage, in 2012. The UK BiLEVE participants were selected from among the European-ancestry individuals in the UK Biobank11, based on being in the “middles and extremes of the forced expiratory volume in 1 s (FEV1) distribution among heavy smokers (mean 35 pack-years) and never smokers.”11 Because the UKB Axiom cohort is the complement of the UK BiLEVE cohort, the UK Axiom cohort is also non-random, being under-sampled on heavy smokers and never smokers. It is thus only the complete UKB that is a population-based study without any particular sampling scheme based on lung function and smoking, while the UK BiLEVE and UKB Axiom cohorts are selected subsamples of the UKB. We decided to analyze the UKB as a single cohort, rather than treating the UK BiLEVE and the UKB Axiom cohorts as two separate cohorts to be analyzed separately and then included in the meta-analysis as separate cohorts. (As indicated in Supplementary Note section 2.3, we included fixed effects to control for the genotyping arrays in the GWAS analyses.) Several factors led us to analyze the UKB as a single cohort. First, this allowed us to control for cryptic relatedness across the BiLEVE and Axiom samples with linear mixed models (LMM) with the BOLT-LMM v2.2 software7. Analyzing the two cohorts separately would have necessitated dropping individuals who have relatives in the other cohort. Second, to our knowledge no published studies have analyzed the two cohorts separately. The pre-print of the UKB flagship paper4, as well as many other recent large-scale GWAS14–16, also analyzed the UKB as a single cohort. During the revision stage, a Referee raised the point that our GWAS results could be sensitive to our decision of analyzing the UKB as a single cohort. Though we believe it is preferable to treat the UKB as a single cohort, it would be worrying if our results were sensitive to that decision. To verify that, we repeated our discovery GWAS of general risk tolerance and our GWAS of ever smoker, this time treating the UK BiLEVE and the UKB Axiom cohorts as two separate cohorts (and meta-analyzing the results). As we report in Supplementary Note section 3.5, the results barely changed.

Quality control of allele-frequency differences between the UK Biobank genotyping arrays

It was communicated that the first release of UKB data contained a small set of 65 autosomal SNPs that appeared to have flipped reference alleles contingent on the array. A subset of these had unfortunately been used during the imputation procedure. We already control for genotype array and batch during GWAS analyses, but as an additional QC step beyond excluding the 65 previously reported flipped SNPs, we investigated the allele frequencies across the arrays to be sure that our results were unaffected by artifacts from the genotyping procedure. It should be noted that the participants genotyped with the UK BiLEVE array were chosen based on lung

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Supplementary methods function and smoking behavior11, but the sample is in all other respects comparable to the rest of the UK Biobank17. We restricted the imputed genotype data to unrelated individuals of British ancestry to ensure that allele-frequency differences across the genotyping arrays would not be caused by differences in the proportion of ancestries or be affected by dependent observations. With PLINK10, we calculated the allele frequencies contingent on the genotyping array for both the 2 directly genotyped and imputed SNPs. Because our quality-control protocol, described in the next section, restricts the GWAS to SNPs with MAF A 0.001, we chose not to investigate SNPs with MAF < 0.001 (in the imputed genotype data) for allele-frequency differences between the UKB genotyping arrays. SNPs available on only one of the genotyping arrays and SNPs that were not available in our main reference panel were not considered in this investigation of allele-frequency differences. For each SNP included in the investigation, three quantities measuring differences in allele frequencies were examined: the absolute value of the difference between the two arrays and the absolute value of the difference between the main reference panel and each of the arrays. We flagged a SNP as problematic if it fulfilled the following two conditions: (1) if the absolute value of the difference between the two arrays was greater than 0.25; and (2) if the absolute value of the difference between the main reference panel and at least one of the genotyping arrays was greater than 0.25. The comparison resulted in 600 flagged autosomal SNPs (including the 65 SNPs that were already reported as problematic) that were removed from the UKB summary statistics during QC in Supplementary Note section 2.6.2.

Description of major steps in quality-control (QC) analyses

For each cohort, we applied a stringent quality-control protocol based on the EasyQC software (version 9.2) developed by the GIANT consortium18, as well as additional steps developed by the Social Science Genetic Association Consortium6,19. All issues raised during implementation of the protocol described below were resolved through iterations between the meta-analyst and the cohort analysts before any GWAS summary statistics were forwarded for meta-analysis.

Pre-QC verification and harmonization of GWAS summary statistics

All cohorts were asked to supply descriptive statistics and phenotype definitions according to the pre-specified analysis plan1. The completeness of these documents was assessed as the first step of the quality control, together with examination of the uploaded GWAS summary statistics. All GWAS summary statistics were harmonized to ensure that the SNP identifier was in an admissible format (i.e. either an rsID, or in a format containing the chromosome, base pair (bp), and the two alleles of the SNP), that the missing string operator was set to “NA,” and that all files had the same column delimiter.

Filters applied before EasyQC protocol

Following recommendations provided by the UK Biobank, we removed the 65 autosomal SNPs from the UKB that had been flagged as having incorrect annotation, together with the additional 535 SNPs that we flagged in section 1.5, before applying the EasyQC protocol described below.

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Also, for cohorts imputed with the September or December 2013 haplotype release of the 1000 Genomes imputation reference panel, we removed 737 SNPs with incorrect strand alignmentg.

EasyQC protocol

The filters applied in the EasyQC software are explained below in chronological order of implementation. Note that the order of the filters does not influence the outcome of the cleaned GWAS summary statistics (although it affects at which specific filter a SNP is removed). The number of SNPs filtered at each step of the EasyQC protocol is reported in Panel A of Supplementary Table 25. Step 1 in the EasyQC protocol filtered out SNPs for which either the effect-coded allele or the other allele has values different from “A,” “C,” “G,” or “T.” This step removed all structural variants such as INDELs. Step 2 filtered out SNPs with missing values for one or more of the following variables: P value, an estimated effect size (beta) or its standard error, frequency of the reference allele, imputation status, and imputation accuracy (conditional on the SNP being imputed). This filter also removed SNPs with nonsense values outside of permissible ranges such as negative or infinite standard errors, nonsensical P values, allele frequencies greater than 1 or below 0, as well as imputation status not equal to 1 or 0. The thresholds chosen for the filters applied in steps 3 to 5 are summarized in Supplementary Table 26. Step 3 filtered out SNPs with a MAF below 0.1% for the UKB and 23andMe cohorts and below 1% for all other cohorts; this effectively removed any SNPs that were monomorphic in the summary statistics. Step 4 excluded SNPs based on imputation accuracy with a threshold contingent on the cohort-specific imputation software (0.6 for MACH, 0.7 for IMPUTE, and 0.8 for PLINK). Step 5 filtered out directly genotyped SNPs with a Hardy-Weinberg equilibrium exact test P value below a threshold contingent on the cohort sample size. The applied thresholds were 10–3 if >, 10–4 if  @>, and 10–5 if  @ >. Two additional filters were applied to ensure that only high-quality SNPs were being forwarded to the meta-analysis; step 6 removed SNP j if  #F  5 H7 ?   E 7  7 B ;  7C where #F5 is the standard deviation of the phenotype, 7 is the sample size,7 is the standard error of the coefficient estimate for SNP j, and  7 is the minor allele frequency of SNP j. This filter eliminates SNPs whose coefficient estimates have standard errors that are more than ~40% larger than what would be expected given the sample size, the MAF of the SNP, and the standard deviation of the phenotype. The second additional filter, step 7, removes SNPs with coefficient estimates larger than what would correspond to an R2 greater than 5%. We adapted the filter from Okbay et al.6 using an approximation to the R2: SNP j is dropped if

g The announcements are available on the webpage of IMPUTE2 https://mathgen.stats.ox.ac.uk/impute/data_download_1000G_1_integrated_SHAPEIT2_16-06-14.html and https://mathgen.stats.ox.ac.uk/impute/data_download_1000G_phase1_integrated_SHAPEIT2_9-12-13.html

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  #F  5  I J7I ?  E  7 B ;  7C

Step 8 filtered out duplicate SNPs (SNPs with identical NCBI build 37 (UCSC hg19) chromosome and base-pair positions). This was implemented after the chromosome and base- 2 pair positions of the SNPs had been harmonized with the main reference panel described above. Step 9 aligned the SNPs to the main reference panel to ensure that the effect-coded allele was the same for all SNPs across the cohorts. This step removed SNPs that were not available in the main reference panel as well as SNPs that displayed an allele mismatch when compared to the reference (e.g., a SNP with the alleles A and T in the GWAS summary statistics would be removed if the alleles according to the reference panel were A and G). Step 10 removed SNPs that deviated from the main reference panel in terms of allele frequency. A SNP was removed if the absolute difference between its allele frequencies in the cohort’s data and in the main reference panel was greater than 0.2. Step 10 was applied to all cohorts including the UKB (for the UKB, this filter was thus applied in addition to the filter described in Supplementary Note section 2.5 and Supplementary Note section 2.6.2, the purpose of which was to avoid potential strand issues caused by the two different UKB genotyping arrays). The output from the quality control was examined to see if any filters removed an unusual or unexpected number of SNPs. Some cohorts required iterations with the analysts to ensure that all possible errors were resolved. The number of SNPs filtered at each step of the final quality control iteration is reported in Panel A of Supplementary Table 25 together with the estimated genomic inflation factor (!43). Visual inspection of diagnostic plots

Once low-quality SNPs were filtered out, the remaining SNPs were used to produce several diagnostic plots for each cohort, most of which are the standard output of the EasyQC software. Visual inspection of these plots enabled the identification of possible issues or errors in the GWAS summary statistics of each cohort; for a more thorough discussion we refer the interested reader to Winkler et al.18. For any potential issues observed in these plots, we contacted the cohort-specific analyst and ensured that the observed issues were completely resolved. The following plots were examined: Allele Frequency Plots (AF Plots): The AF plot contrasts the observed allele frequencies with the expected allele frequencies calculated according to the main reference panel, and the plot was created before step 10 of the EasyQC protocol. If the sample closely resembles the reference panel in terms of allele frequencies, then the SNPs should align in a diagonal with positive slope. This plot enables the analyst to detect deviations in ancestry from the reference as well as issues related to the alignment of the effect-coded allele. If the wrong effect-coded allele has been specified, then the AF plot shows a diagonal with negative slope. P-Z Plots: Inspection of this plot shows if the reported P values are consistent with the reported coefficient estimates and their standard errors. One common problem observable with the P-Z plot is an erroneous column header in the GWAS summary statistics, such that the wrong column is used for either the beta estimates, standard errors or P values in the analysis. Q-Q Plots: Inspection of Q-Q plots enables visualization of unaccounted-for stratification in the cohorts. No cohort displayed premature lift-off in the Q-Q plot associated with

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unaccounted-for stratification. The genomic inflation factors !43 are displayed in Panel A of Supplementary Table 25.

SE Plots: We plotted the observed standard errors (7 ) of the coefficient estimates versus the standard errors expected given the  7 and the sample size 7 of a given SNP j, and the standard deviation of the phenotype #F5 (which is equal to 1 if the phenotype has been standardized). This enables visual inspection to identify groups of outlier SNPs with regard to the observed standard error. The expected standard error was calculated according to the following formula:  #F5  H7 =  E 7  7 B ;  7C

All cohorts had to pass visual inspection as well as inspection of the number of excluded SNPs at each of the exclusion filters described in the previous subsection before being passed on for the meta-analysis. Meta-analysis, adjustment of the standard errors, and test of heterogeneity of effect sizes across cohorts

Meta-analysis

Sample-size weighted meta-analysis of the cleaned cohort-level GWAS summary statistics were carried out using the METAL software20. We conducted four main meta-analyses: (1) we meta-analyzed the discovery GWAS combining the UKB and 23andMe cohorts; (2) we meta- analyzed the results of the 10 replication cohorts without the UKB and 23andMe discovery cohorts, to obtain our replication GWAS; (3) we meta-analyzed the results of the 10 replication cohorts together with those of the UKB and 23andMe discovery cohorts for the follow-up analyses that use GWAS summary statistics; and (4) we meta-analyzed the results from our UKB GWAS of ever smoker with those from the TAG Consortium2. No meta-analyses were conducted for the five other supplementary GWAS, because data for these GWAS each came from only one cohort (either the UKB or the 23andMe cohort). All meta-analyses were performed with the unique ID format as the identifier of each SNP (e.g., 1:123456:C:T)19. All meta-analyses were restricted to SNPs with a sample size greater than half of the maximum sample size across all the SNPs in the GWAS. Thus, because the discovery GWAS of general risk tolerance consists only of the 23andMe and UKB cohorts and because the 23andMe cohort is slightly larger than the UKB, all 9,284,738 SNPs available in the 23andMe cohort (and no other SNPs) were analyzed in the discovery GWAS of general risk tolerance. Of these 9,284,738 SNPs, 8,989,321 are available in both the UKB and the 23andMe cohorts and have a sample size of 931,651 or 939,908h, and 295,417 are available only in the 23andMe cohort and have a sample size of 500,525 or 508,782i. Of the 124 general-risk- tolerance lead SNPs we report below in Supplementary Note section 3.3, all but one (rs13251864) are present in both the 23andMe and UKB cohorts. For the replication GWAS of general risk tolerance, 6,986,015 SNPs were analyzed; 9,339,358 SNPs were analyzed in the GWAS of adventurousness; and ~11,515,000 SNPS were analyzed in the GWAS of the four

h 8,949,622 SNPs have a sample size of 939,908 and 39,699 SNPs have a sample size of 931,651. i 290,259 SNPs have a sample size of 508,782 and 5,158 SNPs have a sample size of 500,525.

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Supplementary methods risky behaviors and their first PC. Panel B of Supplementary Table 25 reports the number of SNPs for all the main GWAS.

Adjustment of the standard errors

Instead of applying genomic control with the over-conservative !43, we inflated the standard errors by the square root of the estimated intercept from an LD Score regression. This procedure 2 allows us to correct only for inflation of test statistics caused by population stratification and other confounding factors rather than polygenicity21. For the discovery and replication GWAS of general risk tolerance and for the GWAS of ever smoker—all of which involved meta-analyses of cohort-level data—we only inflated the meta- level standard errors (i.e., we did not inflate the cohort-level standard errors before the meta- analysis). Likewise, for the meta-analysis of the discovery and replication GWAS for the follow-up analyses, we only inflated the meta-level standard errors. We also inflated the standard errors of the other supplementary GWAS. In practice, for a given meta-analysis, the METAL software20 outputs the SNPs’ meta-analyzed z-statistics, deflated by the square root of the estimated intercept from an LD Score regression. We use SNP j’s GWAS sample size 7 and minor allele frequency  7, as well as the phenotype’s standard deviation #F8, to approximate the inflated standard error of our estimate of SNP j’s effect size:

#F8 H7 = D  E 7  7B ;  7C where D is the square-root of the LD Score intercept used to deflate the z-statistic in the meta-analysis.

We then used SNP j’s deflated z-statistics 7 to approximate SNP j’s effect size as J7 = 7 H9 j. Since the general-risk-tolerance phenotype is not measured in natural units, and since its standard deviation differs across cohorts, we normalize it to have a standard deviation of one when we estimate the SNPs’ effect sizes and standard errors. For consistency, we make the same assumption when approximating the SNPs’ effect sizes and standard errors for the risky behaviors and their first PC. Hence, our estimated effect sizes (the J7’s) are expressed in standard-deviation units of the phenotype per effect-coded allele; we hereafter refer to this as a “standardized beta,” although it is only standardized in terms of standard deviation units of the phenotype (and not with respect to the genotype). The standard deviations of the phenotypes, as originally measured, are reported in Supplementary Table 4. The coefficient of determination of SNP j is approximated as22:

j Since 7 is deflated and H is inflated by the square root of the intercept from the LD score regression, J7 is neither deflated nor inflated.

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    B ;   C J2 2 7 7 7 7 = 2  #F8

Evaluation of effect-size heterogeneity across cohorts for general risk tolerance

Following a Referee’s suggestion, we computed Cochran’s Q statistic for the lead SNPs of our discovery GWAS of general risk tolerance, to evaluate the heterogeneity of our estimates across the 23andMe and UKB cohorts. (We note, however, that the power of Cochran’s Q test is limited in our setting23,24, because the discovery meta-analysis consists of only two cohort.) In addition to examining the P values of Cochran’s Q test for each lead SNP (after Bonferroni correction for the number of lead SNPs), we generated an omnibus test statistic for heterogeneity by summing the Cochran Q statistics across all lead SNPs25. Because there are two cohorts, the Q statistic for each lead SNP has a $2 distribution with one degree of freedom. The sum of these Q statistics is therefore (approximately) $2-distributed with the number of degrees of freedom being equal to the number of lead SNPs. We report the results in Supplementary Note section 3.3.2. Approximately independent lead SNPs, loci, and conditional analysis

Approximately independent lead SNPs

To identify approximately independent genome-wide significant “lead SNPs”, we used PLINK10 to apply a “clumping algorithm” to the GWAS results. (We define a SNP as “genome- wide significant” if its GWAS P value is less than 510–8.) Our clumping algorithm uses four parameters: a primary P value threshold (510–8), a secondary P value threshold (110–4), an r2 threshold (0.1), and a SNP window defined in kilobases (1,000,000 kb). First, the SNP with the lowest P value (less than the primary P value threshold) is taken as the “lead SNP” in the first clump, and the first clump is formed by all SNPs with a P value smaller than the secondary P value thresholdk, with an r2 greater than 0.1 with the clump’s lead SNP, and within a distance less than the SNP window from the lead SNP. (We used a very wide SNP window of 1,000,000 kb, which effectively makes the r2 and P value thresholds the only binding parameters for the PLINK clumping algorithm.) Next, the SNP with the second lowest P value (less than the primary P value threshold) outside the first clump becomes the lead SNP of the second clump, and the second clump is created analogously but using only the SNPs outside of the first clump. This process continues until every SNP with a P value less than the primary P value threshold is either defined as the lead SNP of a clump or clumped with another lead SNP. The r2 was calculated with the main reference panel. Thus, a “lead SNP” is the most significant genome- wide significant SNP in an approximately independent clump, and a lead SNP cannot be in the clump of another lead SNP for the same phenotype.

Definition of non-overlapping, continuous genomic loci

For the purpose of defining non-overlapping, continuous genomic loci, we followed Ripke et al.26. Ripke et al. defined a locus as “the physical region containing all SNPs correlated at r2 > 0.6 with [one of the lead] SNPs”, and merged associated loci within 250kb of each other into a single locus. For each of the seven GWAS, we followed this definition and created a set of loci. We report the identified loci in Supplementary Note section 3, and the loci are listed k The secondary P value threshold lowers the computational effort by allowing the algorithm to ignore SNPs with large P values.

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Supplementary Table 3 and 3.2. In that section, we also report the loci we obtained after pooling all the loci from across the seven GWAS and merging loci within 250kb of each other; those loci are listed in Supplementary Table 7.

Conditional and joint multiple-SNP (COJO) analysis with GCTA

Because we consider approximately independent (pairwise r2 < 0.1) lead SNPs and loci (rather 2 than fully independent lead SNPs and loci), some of our lead SNPs could in principle be secondary associations that are driven by their LD with extremely strong primary associations. We thus performed conditional and joint multiple-SNP (COJO) analysis27 with GCTA. For each of the seven GWAS, we restricted the analysis to the set of SNPs that (1) pass all GWAS quality control filters, and (2) are located within the loci of the phenotype (which includes all the lead SNPs). We analyzed the summary statistics using the stepwise model-selection algorithm detailed in the original COJO publication27. The analysis requires two input parameters: (1) the distance in kb at which perfect linkage equilibrium (r2 = 0) is assumed, and (2) an r2 threshold that prevents the stepwise model selection from adding SNPs that are highly correlated with a previously selected SNP. We used the default parameters, which assume perfect linkage equilibrium for SNPs separated by 10 Mb and which do not add SNPs in strong LD (r2 > 0.9) with an already selected SNP. The COJO analysis was performed with LD estimated in our main reference panel (described in Supplementary Note section 2.4). We report the results of the COJO analysis in Supplementary Note section 3. As we discuss in Supplementary Note section 3.6, we also conducted a multiple regression analysis with individual-level data from the UKB. In that analysis, for each phenotype (except adventurousness, for which there is no UKB data), for each chromosome we regressed the phenotype on all the phenotype’s lead SNPs located on the chromosome (and on control variables). The results were consistent with those of the COJO conditional analysis. Check for long-range LD regions, candidate inversions, and 1000 Genomes structural variants

Check for long-range LD regions

We investigated if the lead SNPs were located in larger structural variation in the form of long- range LD regions, by using a set of long-range LD regions from Price et al.28. Price et al. identified 24 long-range LD regions from 327 European-ancestry individuals, which replicated in two independent samples comprising 1593 European-ancestry Americans and 3004 British individuals. We lifted the genomic positions of the long-range LD regions to build 37 (GRCh37) with the UCSC genome-annotation lift-over tooll, and there were nine long-range LD regions that could not be lifted due to non-overlapping genome sequences or ambiguous mapping across the builds. Hence, the combined map contains 15 long-range LD regions that have non-ambiguous genomic positions available in build 37. These range from ~2.5 to ~8 Mb in genomic size. We checked if each lead SNP from the GWAS was within a long-range LD region, or within 250 bp from the breakpoints of such a region. The results are reported in Supplementary Table 3 and Supplementary Table 6.

l The lift-over tool is available here: https://genome.ucsc.edu/cgi-bin/hgLiftOver

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Check for candidate inversions

We also investigated if the lead SNPs were located in larger structural variation in the form of candidate inversions, by using a list of genomic segments highly prone to inversion polymorphisms from an unpublished resource by Gonzalez, J.R. & Esko, T., (2017, unpublished). The genomic segments highly prone to inversion polymorphisms were identified based on the knowledge that submicroscopic human inversions are typically flanked by highly homologous flanking repeats29, which predisposes their occurrence by non-allelic homologous recombination. Therefore, a set of segments was selected that may be prone to submicroscopic inversions, consisting of all single copy segments in the Genome Reference Consortium’s human reference sequence build 36 (GRCh36) between 0.1 and 8 Mb in length, and flanked by segmental duplications with 90% identity (across the flanking duplications). In total, there were 173 segments that met these criteria and that were thus considered as genomic segments highly prone to inversions. As detectable traces of inversions in SNPs depend on many factors—such as being frequent, ancient and nonrecurring—we tested whether the segments showed inversion patterns in any of two different SNP datasets. First, 69 (40%) of the segments overlapped with the inversions that Caceres et al.30 obtained in the phased genotypes of CEU individuals from the HapMap III project. Second, inversion-like haplotypes31 were inferred in a subsample of 882 Estonians for which gene expression data was available in peripheral blood. In this case 65 (38%) of the 173 segments were significantly associated with the expression of single copy genes within the segment. In total 104 (60%) of the 173 segments showed an inversion signal, indicating their predisposition for inversion occurrence. We lifted the genomic positions of the genomic segments highly prone to inversion polymorphisms to build 37 (GRCh37) with the UCSC genome-annotation lift-over toolm, and there were 19 genomic segments that could not be lifted due to non-overlapping genome sequences or ambiguous mapping across the builds. Hence, the combined map contains 154 segments prone to inversion polymorphisms (hereafter referred to as “the 154 candidate inversions”), that have non-ambiguous genomic positions available in build 37. These range from ~500 kb to ~8 Mb in genomic size. We checked if each lead SNP from the GWAS was within such a candidate inversion, or within 250 bp from the breakpoints of such a candidate inversion. The results are reported in Supplementary Table 3 and Supplementary Table 6.

Check for 1000 Genomes structural variants

Sudmant et al. (2015)32 called and classified a large number of structural variants (SVs) with the final version of the 1000 Genomes Project phase 3 reference panel. They have released an integrated map of 37,250 smaller structural variants, together with enhanced resolution of the size and breakpoint compared to previous publications, and we hereafter refer to these as the “1000G structural variants.” The structural variants range from 1 bp to ~445 kb in genomic size. The smallest variants are generally insertions of 1 bp (~6,900 variants), and the larger variants are generally deletions larger than 50 bp. The majority of these structural variants are in LD with proximate SNPs, and we therefore checked if any of the lead SNPs from our main GWAS, and SNPs in strong LD with those lead SNPs, were located within the start and end positions of any of the 37,250 structural variants. We defined strong LD as an r2 greater than 0.8, which is the definition used by the 1000 Genomes Project Consortium32. The SNPs in LD were extracted using PLINK10 and the main reference panel. We report the results in Supplementary Table 3 and Supplementary Table 6. m The lift-over tool is available here: https://genome.ucsc.edu/cgi-bin/hgLiftOver

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Investigation of the novelty of our GWAS associations

To investigate the novelty of our GWAS associations, we performed lookups of our lead SNPs (and the SNPs in LD with the lead SNPs, r2 > 0.1) in the NHGRI-EBI GWAS Catalog database (revision 2017-08-15)33 of genome-wide significant associations from previous GWAS. We also looked up our lead SNPs (and the SNPs in LD, r2 > 0.1) in some recent GWAS articles that have not been catalogued in the NHGRI-EBI GWAS Catalog database. The NHGRI-EBI 2 GWAS Catalog is a resource that aims to catalogue all associations reported in published GWAS. For general risk tolerance, we performed a search with the term “risk” in the index of phenotypes, and we did not find any previous studies on general risk tolerance in the Catalog. We know of one previous study that identified one independent genome-wide significant association with general risk tolerance, and of one concurrent study that identified a second genome-wide significant association, both using the first UKB data release34,35; the authors of ref.34 referred to the phenotype as “risk-taking propensity,” and the authors of ref.35 referred to it as “risk-taking behavior.” The first genome-wide significant association is replicated in an online publication published in advance36. We added the first of these two studies (i.e., Day et al.34) to our investigation of the novelty of our general-risk-tolerance lead SNPs, and the second we consider concurrent. We also note that, in Supplementary Note section 11.1, we report the results of a literature search of association studies of risk tolerance; that literature search identified no previously reported genome-wide significant associations. The phenotypes drinks per week, ever smoker, and number of sexual partners (or related phenotypes such as alcoholism and age at first sex), were available in the NHGRI-EBI GWAS Catalog database (revision 2017-08-15). Since the GWAS Catalog is not always up-to-date, we additionally performed a literature search for genome-wide significant findings that might not yet have been included in the Catalog. We searched the Pubmed literature database on March 6 2017 and September 13 2017, for the term “genome-wide association study” together with each of the terms “alcohol,” “sexual,” and “smoking” individually. We screened the abstracts and compared the resulting articles with the article list of the GWAS catalog. In addition to what was already reported in the GWAS Catalog (revision 2017-08-15), we found three additional studies with genome-wide significant findings on alcohol consumption, two additional studies on smoking, and no additional studies on sexual behaviors. To our knowledge, this is the first GWAS of adventurousness, automobile speeding propensity, and of the first PC of the four risky behaviors; unsurprisingly, we could not find previous GWAS on any of these phenotypes in the NHGRI-EBI GWAS Catalog database (revision 2017- 08-15). GWAS catalog lookup

We investigated whether the lead SNPs from our discovery GWAS of general risk tolerance and from our supplementary GWAS have previously been associated at genome-wide significance with any phenotypes in the NHGRI-EBI GWAS Catalog database33 (revision 2017-08-15). We query the GWAS Catalog with our list of lead SNPs, and the SNPs in LD with a lead SNP (r2 > 0.6). Since the NGRI-EBI GWAS Catalog is not always up-to-date with results from the most recent publications (especially in non-peer reviewed outlets such as BioRxiv), we also queried the summary statistics of the most recently published GWAS on attention deficit hyperactivity

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Chapter 2 disorder37, autism spectrum disorder38, and anorexia nervosan. We also queried the genome- wide significant findings from the additional GWAS on alcohol intake and smoking, which we found in addition to the GWAS Catalog, as detailed in the previous section. We queried the additional GWAS on alcohol intake and smoking because they contained at least one genome- wide significant result; because their results were publicly available; and because the meta- analysis that produced them did not include the UK Biobank (that comprise our discovery sample together with 23andMe). We perform these lookups because the existence of SNPs and genes associated with both one of our studied phenotypes and another phenotype can point to a common genetic etiology. However, it is important to note that two phenotypes that share a genetic locus do not necessarily have to share the same causal variant at that locus due to the widespread LD that characterizes the human genome. Moreover, even if two phenotypes do share a single causal variant, they do not have to share general underlying genetic etiologies. For instance, recent work39 has shown that two types of autoimmune diseases (rheumatoid arthritis and ulcerative colitis/Crohn’s disease) that are known to share risk loci are not genetically correlated at a genome-wide level. Here, the reason was the lack of an overall directional trend: some risk alleles for one disease were also risk alleles for the other disease, but some alleles that were protective for the one disease were risk alleles for the other. This resulted in a nearzero correlation at the genomewide level. Thus, we emphasize that the current lookup does not make it possible to determine etiological overlap, but only hints at overlapping loci, between general risk tolerance or the supplementary GWAS phenotypes, and the phenotypes reported in the GWAS Catalog. Cross-lookup of GWAS results

We performed cross-lookups of the lead SNPs across our discovery GWAS of our primary and supplementary phenotypes. Specifically, for each lead SNP in each of the GWAS, we checked if the SNP is in LD (with an r2 greater than 0.1) with lead SNPs in the other GWAS. LD was calculated using PLINK10 and the main reference panel. The results of the cross-lookups are reported in Supplementary Table 3 and Supplementary Table 6. As we describe above, we also investigated if there were any long-range LD regions or candidate inversions that contained lead SNPs for multiple GWAS.

Gene annotation

We annotated the lead SNPs with gene information using the National Institute of Health (NIH) National Center for Biotechnology Information (NCBI) gene ontology database (version 2016- 05-25)o. As the general rule, a SNP was annotated to its most proximate gene. If a SNP was located between two genes, then we compared the distance to the end coordinate of the gene upstream with the distance to the start coordinate of the gene downstream to find the most proximate gene. If a SNP was located within multiple overlapping genes, then the SNP was annotated to the gene with the most proximate start coordinate. This means that all lead SNPs were annotated to a single gene. The approach roughly partitions the SNPs throughout the genome into separate genomic segments. The annotations are displayed in Supplementary Table 3 and Supplementary Table 6, where we also indicate if a lead SNP is located within n The Psychiatric Genomics Consortium’s GWAS summary statistics for attention deficit hyperactivity disorder, autism spectrum disorder, and anorexia nervosa (referred to as “ED,” i.e. ) are publicly available and can be downloaded here: https://www.med.unc.edu/pgc/results-and-downloads. o The NCBI gene ontology database is available here: ftp://ftp.ncbi.nlm.nih.gov/gene/DATA/GENE_INFO/Mammalia/.

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Supplementary methods or outside the gene’s start and end coordinates. We also checked if there were genes to which lead SNPs from multiple GWAS were annotated (the results are reported in Supplementary Note section 3.2).

Methods to estimate genome-wide SNP heritability

Risk tolerance (both self-reported and experimentally elicited) has been found to be moderately 2 heritable in twin studies, with heritability estimates ranging from 20% to 60%40–42. In this section, we employ three different methods to obtain estimates of the SNP heritability of our primary and supplementary GWAS phenotypes. A phenotype’s SNP heritability is the fraction of the phenotype’s variance that is accounted for by the additive genetic effects of a set of SNPs. We used three methods, GCTA43, LD Score regression21, and Heritability Estimator from 44 2 Summary Statistics (HESS) , to estimate the genome-wide SNP heritability K4L. The GCTA method estimates the heritability of a phenotype directly from the individuals’ genotypic data, while the LD Score and HESS methods use GWAS summary statistics as inputs. For comparability across phenotypes and methods, for the LD Score and HESS methods, we used summary statistics from the UKB GWAS only for all phenotypes except adventurousness. For the adventurousness phenotype, we only report estimates that were obtained using the LD Score regression and HESS methods using the 23andMe summary statistics, because this phenotype is not available in the UKB and we did not have access to the individual-level genotypic data from the 23andMe cohort (and so could not obtain estimates with the GCTA method). We also computed HESS SNP heritability estimates using summary statistics from our seven main GWAS (and not only from GWAS conducted in the UKB or in the 23andMe cohort).

The GCTA method

The GCTA method is based on restricted maximum-likelihood estimation and uses the genetic relationship matrix (GRM) to estimate the SNP heritability. Under the assumptions discussed in Yang et al. (2011)43, the method leads to unbiased estimates of the genome-wide SNP heritability. However, it is computationally intensive, and it is thus necessary to limit the number of SNPs and individuals included in the analysis in order to be computationally feasible. Therefore, we restricted the GCTA analysis to a random subset of 30,000 individuals out of the full sample from the discovery GWAS. We thereafter dropped one individual in each pair of individuals with a cryptic relatedness exceeding 0.025, to obtain a set of unrelated individuals. For comparability we used the same initial subset of 30,000 individuals for the GCTA estimation for all phenotypes, though the sample size varies slightly across phenotypes because of missing phenotypic observations. The final sample sizes for each phenotype are presented in Supplementary Table 30. In total 646,855 directly genotyped SNPs with MAF > 0.01 were included in the GCTA heritability estimation.

The LD Score regression method

Under the assumptions discussed in Bulik-Sullivan et al. (2015)21, a SNP’s GWAS $2 statistic is linearly related to its LD score, defined as the sum of the squared correlation coefficients between any single SNP and all the other SNPs. The slope of the LD Score regression (of the SNPs GWAS $2 statistics on their LD scores and an intercept) can be rescaled to obtain an estimate of the heritability explained by the SNPs included in the LD Score analysis by dividing the slope by the sample size divided by the number of SNPs, i.e., by n/M. We used the

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“eur_w_ld_chr/” files of LD scores computed by Finucane et al.45 and made available on https://github.com/bulik/ldsc/wiki/Genetic-Correlation, accessed on March 14, 2016. These LD scores were computed with genotypes from the European-ancestry samples in the 1000 Genomes Project. Only HapMap3 SNPs with MAF > 0.01 were included in the LD Score regression; for every phenotype, ~1.3 million SNPs were used for the LD Score heritability estimation. Since Genomic Control (GC) will tend to bias the intercept of the LD Score regression downward, we did not apply GC to the summary statistics prior to estimating the LD Score regressions.

The HESS method

The HESS estimator can be described in brief as an analytical variance decomposition method that, unlike the GCTA and LD Score regression methods, assumes that the SNP effect sizes are fixed effects rather than random effects. The method assumes that the SNPs are randomly distributed in the population and requires a pre-specified SNP covariance matrix as input. The SNP covariance matrix can be estimated in the sample of interest if individual genotypic data is available, or with an external reference panel such as the 1000 Genomes. As Shi et al.44 show using simulations, heritability estimates from LD Score regression are sensitive to the true proportion of causal SNPs, and the HESS estimator yields more accurate heritability estimates than LD Score regression under a wider range of proportions of truly causal SNPs. We used the reference panel distributed with the HESS software for the calculation of the covariance matrix. That panel is the European subsample of the 1000 Genomes phase 3 version 5 reference panel, restricted to common variants (MAF > 0.05), which is the same as the reference panel used for the construction of the LD Scoresp. For every phenotype, a total of ~4.9 million SNPs were used in the HESS heritability estimation. As with the LD Score regressions, we did not apply GC prior to estimating heritability with HESS.

p While the same reference panel was used for the construction of the LD scores, as indicated above HapMap3 SNPs with MAF > 0.01 were included in the LD score regression.

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References – Chapter 2 Supplementary methods

1. SSGAC. Pre-registered Analysis Plan - GWAS Risk tolerance. Open Science Framework (2016). Available at: https://osf.io/cjx9m/. 2. The Tobacco and Genetics Consortium. Genome-wide meta-analyses identify multiple loci associated with smoking behavior. Nat. Genet. 42, 441–7 (2010). 2 3. Marchini, J. L. et al. Genotype imputation and genetic association studies of UK Biobank. (2015). 4. Bycroft, C. et al. Genome-wide genetic data on ~500,000 UK Biobank participants. bioRxiv (2017). doi:http://dx.doi.org/10.1101/166298 5. McCarthy, S. et al. A reference panel of 64,976 haplotypes for genotype imputation. Nat. Genet. 48, 1279–1283 (2016). 6. Okbay, A. et al. Genome-wide association study identifies 74 loci associated with educational attainment. Nature 533, 539–542 (2016). 7. Loh, P.-R. et al. Efficient Bayesian mixed-model analysis increases association power in large cohorts. Nat. Genet. 47, 284–290 (2015). 8. Tan, A., Abecasis, G. R. & Kang, H. M. Unified representation of genetic variants. Bioinformatics 31, 2202–2204 (2015). 9. Huang, J. et al. Improved imputation of low-frequency and rare variants using the UK10K haplotype reference panel. Nat. Commun. 6, 8111 (2015). 10. Purcell, S. et al. PLINK: A tool set for whole-genome association and population- based linkage analyses. Am. J. Hum. Genet. 81, 559–575 (2007). 11. Wain, L. V. et al. Novel insights into the genetics of smoking behaviour, lung function, and chronic obstructive pulmonary disease (UK BiLEVE): A genetic association study in UK Biobank. Lancet Respir. Med. 3, 769–781 (2015). 12. Sudlow, C. et al. UK Biobank: An open access resource for identifying the causes of a wide range of complex diseases of middle and old age. PLoS Med. 12, e1001779 (2015). 13. Marchini, J. L. et al. Genotype Imputation and Genetic Association Studies of Uk Biobank: Interim Data Release. (2015). 14. Pilling, L. C. et al. Human longevity: 25 genetic loci associated in 389,166 UK biobank participants. Aging (Albany. NY). 9, 2504–2520 (2017). 15. Jansen, P. R. et al. Genome-wide Analysis of Insomnia (N=1,331,010) Identifies Novel Loci and Functional Pathways. bioRxi (2018). 16. Yengo, L. et al. Meta-analysis of genome-wide association studies for height and body mass index in ~700,000 individuals of European ancestry. bioRxiv (2018). doi:10.1101/274654 17. UK Biobank. Genotyping and Quality Control of UK Biobank, a Large-Scale, Extensively Phenotyped Prospective Resource: Information for Researchers. Interim Data Release. (2015). 18. Winkler, T. W. et al. Quality control and conduct of genome-wide association meta- analyses. Nat. Protoc. 9, 1192–212 (2014).

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19. Okbay, A. et al. Genetic variants associated with subjective well-being, depressive symptoms, and neuroticism identified through genome-wide analyses. Nat. Genet. 48, 624–633 (2016). 20. Willer, C. J., Li, Y. & Abecasis, G. R. METAL: Fast and efficient meta-analysis of genomewide association scans. Bioinformatics 26, 2190–2191 (2010). 21. Bulik-Sullivan, B. K. et al. LD Score regression distinguishes confounding from polygenicity in genome-wide association studies. Nat. Genet. 47, 291–295 (2015). 22. Rietveld, C. A. C. A. et al. GWAS of 126,559 individuals identifies genetic variants associated with educational attainment. Science (80-. ). 340, 1467–1471 (2013). 23. Pereira, T. V., Patsopoulos, N. A., Salanti, G. & Ioannidis, J. P. A. Critical interpretation of Cochran’s Q test depends on power and prior assumptions about heterogeneity. Res. Synth. Methods 1, 149–161 (2010). 24. Ioannidis, J. P. A., Patsopoulos, N. A. & Evangelou, E. Heterogeneity in meta-analyses of genome-wide association investigations. PLoS One 2, (2007). 25. Cochran, W. G. The Combination of Estimates from Different Experiments. Biometrics 10, 101–129 (1954). 26. Ripke, S. et al. Biological insights from 108 schizophrenia-associated genetic loci. Nature 511, 421–427 (2014). 27. Yang, J. et al. Conditional and joint multiple-SNP analysis of GWAS summary statistics identifies additional variants influencing complex traits. Nat. Genet. 44, 369– 375 (2012). 28. Price, A. L. et al. Long-range LD can confound genome scans in admixed populations. Am. J. Hum. Genet. 83, 132–139 (2008). 29. Cáceres, A. & González, J. R. Following the footprints of polymorphic inversions on SNP data: from detection to association tests. Nucleic Acids Res. 43, e53 (2015). 30. Cáceres, A., Sindi, S. S., Raphael, B. J., Cáceres, M. & González, J. R. Identification of polymorphic inversions from genotypes. BMC Bioinformatics 13, 28 (2012). 31. Ma, J. & Amos, C. I. Investigation of inversion polymorphisms in the human genome using principal components analysis. PLoS One 7, e40224 (2012). 32. Sudmant, P. H. et al. An integrated map of structural variation in 2,504 human genomes. Nature 526, 75–81 (2015). 33. Welter, D. et al. The NHGRI GWAS Catalog, a curated resource of SNP-trait associations. Nucleic Acids Res. 42, D1001-1006 (2014). 34. Day, F. R. et al. Physical and neurobehavioral determinants of reproductive onset and success. Nat. Genet. 48, 617–623 (2016). 35. Strawbridge, R. J. et al. Genome-wide analysis of self-reported risk-taking behaviour and cross-disorder genetic correlations in the UK Biobank cohort. Transl. Psychiatry 8, 1–11 (2018). 36. Boutwell, B. et al. Replication and characterization of CADM2 and MSRA genes on human behavior. Heliyon 3, e00349 (2017). 37. Demontis, D. et al. Discovery of the first genome-wide significant risk loci for ADHD. bioRxiv (2017). doi:10.1101/145581

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38. The Autism Spectrum Disorders Working Group of The Psychiatric Genomics Consortium. Meta-analysis of GWAS of over 16,000 individuals with autism spectrum disorder highlights a novel locus at 10q24.32 and a significant overlap with schizophrenia. Mol. Autism 8, 21 (2017). 39. Bulik-Sullivan, B. K. et al. An atlas of genetic correlations across human diseases and traits. Nat. Genet. 47, 1236–1241 (2015). 2 40. Beauchamp, J. P., Cesarini, D. & Johannesson, M. The psychometric and empirical properties of measures of risk preferences. J. Risk Uncertain. 54, 203–237 (2017). 41. Cesarini, D., Dawes, C. T., Johannesson, M., Lichtenstein, P. & Wallace, B. Genetic variation in preferences for giving and risk taking. Q. J. Econ. 124, 809–842 (2009). 42. Zyphur, M. J., Narayanan, J., Arvey, R. D. & Alexander, G. J. The genetics of economic risk preferences. J. Behav. Decis. Mak. 22, 367–377 (2009). 43. Yang, J., Lee, S. H., Goddard, M. E. & Visscher, P. M. GCTA: a tool for genome-wide complex trait analysis. Am. J. Hum. Genet. 88, 76–82 (2011). 44. Shi, H., Kichaev, G. & Pasaniuc, B. Contrasting the genetic architecture of 30 complex traits from summary association data. Am. J. Hum. Genet. 99, 139–153 (2016). 45. Finucane, H. K. et al. Partitioning heritability by functional category using GWAS summary statistics. Nat. Genet. 47, 1228–1235 (2015). 46. Howie, B. N., Donnelly, P. & Marchini, J. A flexible and accurate genotype imputation method for the next generation of genome-wide association studies. PLoS Genet. 5, e1000529 (2009). 47. Li, Y., Willer, C. J., Ding, J., Scheet, P. & Abecasis, G. R. MaCH: Using sequence and genotype data to estimate haplotypes and unobserved genotypes. Genet. Epidemiol. 34, 816–834 (2010).

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62 Genetic variants associated with subjective well-being, depressive symptoms, and neuroticism identified through genome-wide analysis

“We’re living in primate heaven. We’re warm, dry, we’re not hungry, we don’t have fleas and ticks and infections. So why are we so miserable? Steven Pinker

When I believe, I am crazy. When I don’t believe, I suffer psychotic depression. Philipp K. Dick

Based on Okbay et al. (2016). Nature Genetics. 3

Chapter 3

Abstract

We conducted genome-wide association studies of three phenotypes: subjective well-being (N = 298,420), depressive symptoms (N = 161,460), and neuroticism (N = 170,910). We identified three variants associated with subjective well-being, two with depressive symptoms, and eleven with neuroticism, including two inversion polymorphisms. The two depressive symptoms loci replicate in an independent depression sample. Joint analyses that exploit the high genetic correlations between the phenotypes (-G =. /) strengthen the overall credibility of the findings, and allow us to identify additional variants. Across our phenotypes, loci regulating expression in central nervous system and adrenal/pancreas tissues are strongly enriched for association.

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Discovering the genetic architecture of the mind

Introduction

Subjective well-being—as measured by survey questions on life satisfaction, positive affect, or happiness—is a major topic of research within psychology, economics, and epidemiology. Twin studies have found that subjective well-being is genetically correlated with depression (characterized by negative affect, anxiety, low energy, bodily aches and , pessimism, and other symptoms) and neuroticism (a personality trait characterized by easily experiencing negative such as anxiety and fear)1–3. Depression and neuroticism have received much more attention than subjective well-being in genetic-association studies, but the discovery of associated genetic variants with either of them has proven elusive4,5. 3 In this paper, we report a series of separate and joint analyses of subjective well-being, depressive symptoms, and neuroticism. Our primary analysis is a genome-wide association study (GWAS) of subjective well-being based on data from 59 cohorts (N = 298,420). This GWAS identifies three loci associated with subjective well-being at genome-wide significance (p < 510-8). We supplement this primary analysis with auxiliary GWAS meta-analyses of depressive symptoms (N = 180,866) and neuroticism (N = 170,910), performed by combining publicly available summary statistics from published studies with new genome-wide analyses of additional data. In these auxiliary analyses we identify two loci associated with depressive symptoms and eleven with neuroticism, including two inversion polymorphisms. In depression data from an independent sample (N = 368,890), both depressive symptoms associations replicate (p = 0.004 and p = 0.015). In our two joint analyses, we exploit the high genetic correlation between subjective well-being, depressive symptoms, and neuroticism (i) to evaluate the credibility of the 16 genome-wide significant associations across the three phenotypes, and (ii) to identify novel associations (beyond those identified by the GWAS). For (i), we investigate whether our three subjective well-being-associated SNPs “quasi-replicate” by testing them for association with depressive symptoms and neuroticism. We similarly examine the quasi-replication record of the depressive symptoms and neuroticism loci by testing them for association with subjective well-being. We find that the quasi-replication record closely matches what would be expected given our statistical power if none of the genome-wide significant associations were chance findings. These results strengthen the credibility of (most of) the original associations. For (ii), we use a “proxy phenotype” approach6: we treat the set of loci associated with subjective well-being at p < 10-4 as candidates, and we test them for association with depressive symptoms and neuroticism. At the Bonferroni-adjusted 0.05 significance threshold, we identify two loci associated with both depressive symptoms and neuroticism and another two associated with neuroticism. In designing our study, we faced a tradeoff between analyzing a smaller sample with a homogeneous phenotype measure versus attaining a larger sample by jointly analyzing data from multiple cohorts with heterogeneous measures. For example, in our analysis of subjective well-being, we included measures of both life satisfaction and positive affect, even though these constructs are conceptually distinct7. In Supplementary Note and Supplementary Figure 1, we present a theoretical framework for evaluating the costs and benefits of pooling heterogeneous measures. In our context, given the high genetic correlation across measures, the framework predicts that pooling increases statistical power to detect variants. This prediction is supported by our results.

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Results

GWAS of subjective well-being Following a pre-specified analysis plan, we conducted a sample-size-weighted meta-analysis (N = 298,420) of cohort-level GWAS summary statistics. The phenotype measure was life satisfaction, positive affect, or (in some cohorts) a measure combining life satisfaction and positive affect. We confirmed previous findings9 of high pairwise genetic correlation between life satisfaction and positive affect using bivariate LD Score regression10 ("F = 0.981 (SE = 0.065); Supplementary Table 1). Details on the 59 participating cohorts, their phenotype measures, genotyping, quality-control filters, and association models are provided in Online Methods, Supplementary Note, and Supplementary Tables 2-6. 11 As expected under polygenicity , we observe inflation of the median test statistic (GC = 1.206). The estimated intercept from LD Score regression (1.012) suggests that nearly all of the inflation is due to polygenic signal rather than bias. We also performed family-based analyses that similarly suggest minimal confounding due to population stratification (Online Methods). Using a clumping procedure (Supplementary Note), we identified three approximately independent SNPs reaching genome-wide significance (“lead SNPs”). These three lead SNPs are indicated in the Manhattan plot (Figure 3.1a) and listed in Table 3.1. The SNPs have estimated effects in the range 0.015 to 0.018 standard deviations (SDs) per allele (each R2= 0.01%). We also conducted separate meta-analyses of the components of our subjective well-being measure, life satisfaction (N = 166,205) and positive affect (N = 180,281) (Online Methods). Consistent with our theoretical conclusion that pooling heterogeneous measures increased power in our context, the life satisfaction and positive affect analyses yielded fewer signals across a range of p-value thresholds than our meta-analysis of subjective well-being (Supplementary Table 7).

GWAS of depressive symptoms and neuroticism We conducted auxiliary GWAS of depressive symptoms and neuroticism (see Online Methods, Supplementary Note, and Supplementary Tables 8-12 for details on cohorts, phenotype measures, genotyping, association models, and quality-control filters). For depressive symptoms (N = 180,866), we meta-analyzed publicly available results from a study performed by the Psychiatric Genomics Consortium (PGC)12 together with new results from analyses of the initial release of the UK Biobank data (UKB)13 and the Resource for Genetic Epidemiology Research on Aging (GERA) Cohort14. In UKB (N = 105,739), we constructed a continuous phenotype measure by combining responses to two questions, which ask about the frequency in the past two weeks with which the respondent experienced feelings of unenthusiasm/disinterest and depression/hopelessness. The other cohorts had ascertained case- control data on major depressive disorder (GERA: Ncases = 7,231, Ncontrols = 49,316; PGC: Ncases = 9,240, Ncontrols = 9,519). For neuroticism (N = 170,910), we pooled summary statistics from a published study by the Genetics of Personality Consortium (GPC)4 with results from a new analysis of UKB data. The GPC (N = 63,661) harmonized different neuroticism batteries. In UKB (N = 107,245), our measure was the respondent’s score on a 12-item version of the Eysenck Personality Inventory Neuroticism scale15. In both the depressive symptoms and neuroticism GWAS, the heterogeneous phenotypic measures are highly genetically correlated (Supplementary Table 1). As in our subjective

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Discovering the genetic architecture of the mind well-being analyses, there is substantial inflation of the median test statistics (GC = 1.168 for depressive symptoms, GC = 1.317 for neuroticism), but the estimated LD Score intercepts (1.008 and 0.998, respectively) suggest that bias accounts for little or none of the inflation. For depressive symptoms, we identified two lead SNPs, indicated in the Manhattan plot (Figure 3.1b). For neuroticism, our meta-analysis yielded 16 loci that are independent according to our locus definition (Figure 3.1c). However, 6 of these reside within a well-known inversion polymorphism16 on chromosome 8. We established that all genome-wide significant signals in the inversion region are attributable to the inversion, and we confirmed that the inversion is associated with neuroticism in both of our neuroticism datasets, the GPC and the UKB (Online Methods and Supplementary Note). In our list of lead SNPs (Table 3.1), we only retain the 3 most strongly associated SNP from these 6 loci to tag the chromosome 8 inversion. Another lead SNP associated with neuroticism, rs193236081, is located within a well-known inversion polymorphism on chromosome 17. We established that this association is attributable to the inversion polymorphism (Online Methods and Supplementary Note). Because this inversion yields only one significant locus and is genetically complex17, we hereafter simply use its lead SNP as its proxy. Our neuroticism GWAS therefore identified 11 lead SNPs, two of which tag inversion polymorphisms. A concurrent neuroticism GWAS using a subset of our sample reports similar findings18. As shown in Table 3.1, the estimated effects of all lead SNPs associated with depressive symptoms and neuroticism are in the range 0.020 to 0.031 SDs per allele (R2 = 0.02% to 0.04%). In the UKB cohort we estimated the effect of an additional allele of the chromosome 8 inversion polymorphism itself on neuroticism to be 0.035 SDs (Supplementary Table 13). The inversion explains 0.06% of the variance in neuroticism (roughly the same as the total variance explained jointly by the 6 SNPs in the inversion region).

Genetic overlap across subjective well-being, depressive symptoms, and neuroticism Figure 3.2a shows that the three pairwise genetic correlations between our phenotypes, estimated using bivariate LD Score regression10, are substantial: -0.81 (SE = 0.046) between subjective well-being and depressive symptoms, -0.75 (SE = 0.034) between subjective well- being and neuroticism, and 0.75 (SE = 0.027) between depressive symptoms and neuroticism. Using height as a negative control, we also examined pairwise genetic correlations between each of our phenotypes and height and, as expected, found all three to be modest, e.g., 0.07 with subjective well-being (Supplementary Table 1). The high genetic correlations between subjective well-being, depressive symptoms, and neuroticism may suggest that the genetic influences on these phenotypes are predominantly related to processes common across the phenotypes, such as , rather than being phenotype-specific.

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Chapter 3

Quasi-replication and Bayesian credibility analyses We assessed the credibility of our findings using a standard Bayesian framework19,20 in which a positive fraction of SNPs have null effects and a positive fraction have non-null effects (Online Methods). For each phenotype, the non-null effect sizes are assumed to be drawn from a normal distribution whose variance is estimated from the GWAS summary statistics. As a first analysis, for each lead SNP’s association with its phenotype, we calculated the posterior probability of null association after having observed the GWAS results. We found that, for any assumption about the fraction of non-null SNPs in the range 1% to 99%, the probability of true association always exceeds 95% for all 16 loci (and always exceeds 98% for 14 of them). To further probe the credibility of the findings, we performed “quasi-replication” exercises (Online Methods) in which we tested the subjective well-being lead-SNPs for association with depressive symptoms and neuroticism. We similarly tested the depressive symptoms lead-SNPs and the neuroticism lead-SNPs for association with subjective well-being. Below, we refer to the phenotype for which the lead SNP was identified as the first-stage phenotype and the phenotype used for the quasi-replication as the second-stage phenotype. To avoid sample overlap, for each quasi-replication analysis we omitted any cohorts that contributed to the GWAS of the first-stage phenotype. Results of the quasi-replication of the three subjective well-being lead-SNPs are shown in Figure 3.3a. For ease of interpretation, the reference allele for each association in the figure is chosen such that the predicted sign of the second-stage estimate is positive. We find that two out of the three subjective well-being lead-SNPs are significantly associated with depressive symptoms (p = 0.004 and p = 0.001) in the predicted direction. For neuroticism, where the second-stage sample size (N = 68,201) is about half as large, the subjective well-being- increasing allele has the predicted sign for all three SNPs, but none reach significance. Figures 3.3b and 3.3c show the results for the depressive symptoms and neuroticism lead- SNPs, respectively. In each panel, the blue crosses depict results from the quasi-replications where subjective well-being is the second-stage phenotype. We find that the two depressive symptoms lead-SNPs have the predicted sign for subjective well-being, and one is nominally significant (p = 0.04). Finally, of the eleven neuroticism lead-SNPs, nine have the predicted sign for subjective well-being. Four of the eleven are nominally significantly associated with subjective well-being, all with the predicted sign. One of the four is the SNP tagging the inversion on chromosome 816. That SNP’s association with neuroticism (and likely with subjective well-being) is driven by its correlation with the inversion (Supplementary Fig. 2). To evaluate what these quasi-replication results imply about the credibility of the 16 GWAS associations, we compared the observed quasi-replication record to the quasi-replication record expected given our statistical power. We calculated statistical power using our Bayesian framework, under the hypothesis that each lead SNP has a non-null effect on both the first- and second-stage phenotypes. Our calculations take into account both the imperfect genetic correlation between the first- and second-stage phenotypes and inflation of the first-stage estimates due to the well-known problem of winner’s curse (Online Methods). Of the 19 quasi- replication tests, our calculations imply that 16.7 would be expected to yield the anticipated sign and 6.9 would be significant at the 5% level. The observed numbers are 16 and 7. Our quasi-replication results are thus consistent with the hypothesis that none of the 16 genome- wide significant associations are chance findings, and in fact strengthen the credibility of our GWAS results (Supplementary Table 14).

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Lookup of depressive symptoms and neuroticism lead-SNPs Investigators of an ongoing large-scale GWAS of major depressive disorder (N = 368,890) in the 23andMe cohort shared association results for the loci identified in our depressive symptoms and neuroticism analyses (Online Methods and Supplementary Table 15)21. Because the depression sample overlaps with our subjective well-being sample, we did not request a lookup of the subjective well-being-associated SNPs. In Figures 3.3b and 3.3c, the results are depicted as green crosses. For interpretational ease, we chose the reference allele so that positive coefficients imply that the estimated effect is in the predicted direction. All 13 associations have the predicted sign. Of the 11 neuroticism polymorphisms, four are significantly associated with depression at the 5% level. Both of the 3 depressive symptoms lead-SNPs replicate (p = 0.004 and p = 0.015), with effect sizes (0.007 and -0.007 SDs per allele), close to those predicted by our Bayesian framework (0.008 and - 0.006) (Supplementary Table 14 and Supplementary Table 15). Panel A of Table 3.1 summarizes the results for the 16 lead SNPs identified across our separate GWA analyses of the three phenotypes. The right-most column summarizes the statistical significance of the quasi-replication and depression lookup analyses of each SNP.

Proxy-phenotype analyses To identify additional SNPs associated with depressive symptoms, we conducted a two-stage “proxy phenotype” analysis (Online Methods). In the first stage, we ran a new GWAS of subjective well-being to identify a set of candidate SNPs. Specifically, from each locus exhibiting suggestive evidence of association (p < 10-4) with subjective well-being, we retained the SNP with the lowest p-value as a candidate. In the second stage, we tested these candidates for association with depressive symptoms at the 5% significance threshold, Bonferroni-adjusted for the number of candidates. We used an analogous two-stage procedure to identify additional SNPs associated with neuroticism. The first-stage subjective well-being sample differs across the two proxy-phenotype analyses (and from the primary subjective well-being GWAS sample) because we assigned cohorts across the first and second stages so as to maximize statistical power for the overall procedure. For depressive symptoms, there are 163 candidate SNPs. 115 of them (71%) have the predicted direction of effect on depressive symptoms, 20 are significantly associated at the 5% significance level (19 in the predicted direction), and two remain significant after Bonferroni adjustment. For neuroticism, there are 170 candidate SNPs. 129 of them (76%) have the predicted direction of effect, all 28 SNPs significant at the 5% level have the predicted sign, and four of these remain significant after Bonferroni adjustment (Supplementary Fig. 3 and Supplementary Tables 16 and 17). Two of the four are the SNPs identified in the proxy- phenotype analysis for depressive symptoms. Table 3.1 lists the four SNPs in total identified by the proxy-phenotype analyses.

Biological analyses To shed some light on possible biological mechanisms underlying our findings, we conducted several analyses. We began by using bivariate LD Score regression10 to quantify the amount of genetic overlap between each of our three phenotypes and ten neuropsychiatric and physical health phenotypes. Figures 3.2b and c display the estimates for subjective well-being and the negative of the

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Chapter 3 estimates for depressive symptoms and neuroticism (since subjective well-being is negatively genetically correlated with depressive symptoms and neuroticism). Subjective well-being, depressive symptoms, and neuroticism have strikingly similar patterns of pairwise genetic correlation with the other phenotypes. Figure 3.2b shows the results for the five neuropsychiatric phenotypes we examined: Alzheimer’s disease, anxiety disorders, autism spectrum disorder, bipolar disorder, and schizophrenia. For four of these phenotypes, genetic correlations with depression (but not neuroticism or subjective well-being) were reported in Bulik-Sullivan et al.10. For schizophrenia and bipolar disorder, our estimated correlations with depressive symptoms, 0.33 and 0.26, are substantially lower than Bulik-Sullivan et al.’s point estimates but contained within their 95% confidence intervals. By far the largest genetic correlations we estimate are with anxiety disorders: 0.73 with subjective well-being, 0.88 with depressive symptoms, and 0.86 with neuroticism. Genetic correlations estimated from GWAS data have not been previously reported for anxiety disorders. Figure 3.2c shows the results for five physical health phenotypes that are known or believed to be risk factors for various adverse health outcomes: body mass index (BMI), ever-smoker status, coronary artery disease, fasting glucose, and triglycerides. The estimated genetic correlations are all small in magnitude, consistent with earlier work, although the greater precision of our estimates allows us to reject null effects in most cases. The signs are generally consistent with those of the phenotypic correlations reported in earlier work between our phenotypes and outcomes such as obesity22, smoking23,24, and cardiovascular health25. Next, to investigate whether our GWAS results are enriched in particular functional categories, we applied stratified LD Score regression26 to our meta-analysis results. In our first analysis, we report estimates for all 53 functional categories included in the “baseline model”; the results for subjective well-being, depressive symptoms, and neuroticism are broadly similar (Supplementary Tables 18-20) and are in line with what has been found for other phenotypes26. In our second analysis, the categories are groupings of SNPs likely to regulate gene expression in cells of a specific tissue. The estimates for subjective well-being, depressive symptoms, and neuroticism are shown in Figure 3.4a, alongside height, which is again included as a benchmark27 (see also Supplementary Table 21). We found significant enrichment of CENTRAL NERVOUS SYSTEM for all three phenotypes and, perhaps more surprisingly, enrichment of ADRENAL/PANCREAS for subjective well-being and depressive symptoms. The cause of the ADRENAL/PANCREAS enrichment is unclear, but we note that the adrenal glands produce several hormones, including cortisol, epinephrine, and norepinephrine, known to play important roles in the bodily regulation of mood and stress. It has been robustly found that blood serum levels of cortisol in patients afflicted by depression are elevated relative to controls28. While the above analyses utilize the genome-wide data, we also conducted three analyses (Online Methods) restricted to the 16 GWAS and four proxy-phenotype SNPs in Table 3.1. In brief, we ascertained whether each SNP (or a variant in strong linkage disequilibrium (LD) with it) falls into any of the following three classes: (i) resides in a locus for which genome-wide significant associations with other phenotypes have been reported (Supplementary Table 22), (ii) is nonsynonymous (Supplementary Table 23), and (iii) is an eQTL in blood or in one of 14 other tissues (although the non-blood analyses are based on smaller samples) (Supplementary Table 24). Here we highlight a few particularly interesting results. We found that five of the 20 SNPs are in loci in which genome-wide significant associations have previously been reported. Two of these five are schizophrenia loci. Interestingly, one of them harbors the gene DRD2, which encodes the D2 subtype of the dopamine receptor, a target

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Discovering the genetic architecture of the mind for antipsychotic drugs29 that is also known to play a key role in neural reward pathways30. Motivated by these findings, as well as by the modest genetic correlations with schizophrenia reported in Figure 3.2b, we examined whether the SNPs identified in a recent study of schizophrenia31 are enriched for association with neuroticism in our non-overlapping UKB sample (N = 107,245). We conducted several tests and found strong evidence of such enrichment (Supplementary Note). For example, we found that the p-values of the schizophrenia SNPs tend to be much lower than the p-values of a randomly selected set of SNPs matched on allele frequency (p = 6.5010-71). Perhaps the most notable pattern that emerges from our biological analyses is that the inversions on chromosomes 8 and 17 are implicated consistently across all analyses. The inversion-tagging 3 SNP on chromosome 8 is in LD with SNPs that have previously been found to be associated with BMI32 and triglycerides33 (Supplementary Table 22). We also conducted eQTL analyses in blood for the inversion itself and found that it is a significant cis-eQTL for 7 genes (Supplementary Table 24). As shown in Figure 3.4b, all 7 genes are positioned in close proximity to the inversion breakpoints, suggesting that the molecular mechanism underlying the inversion’s effect on neuroticism could involve the relocation of regulatory sequences. Two of the genes (MSRA, MTMR9) are known to be highly expressed in tissues and cell types that belong to the nervous system, and two (BLK, MFHAS1) in the immune system. In the tissue- specific analyses, we found that the SNP tagging the inversion is a significant eQTL for two genes, AF131215.9 (in tibial nerve and thyroid tissue analyses) and NEIL2 (tibial nerve tissue), both of which are also located near the inversion breakpoint. The SNP tagging the chromosome 17 inversion is a significant cis-eQTL for five genes in blood and is an eQTL in all 14 other tissues (Supplementary Table 24). It alone accounts for 151 out of the 169 significant associations identified in the 14 tissue-specific analyses. Additionally, the SNP is in near-perfect LD (R2 > 0.97) with 11 missense variants (Supplementary Table 23) in three different genes, one of which is MAPT. MAPT, which is also implicated in both the blood and the other tissue-specific analyses, encodes a protein important in the stabilization of microtubules in . Associations have been previously reported between SNPs in MAPT (all of which are in strong LD with our inversion-tagging SNP) and neurodegenerative disorders, including Parkinson’s disease34 and progressive supranuclear palsy35, a rare disease whose symptoms include depression and apathy.

Discussion

The discovery of genetic loci associated with subjective well-being, depression, and neuroticism has proven elusive. Our study identified several credible associations for two main reasons. First, our analyses had greater statistical power than prior studies because ours were conducted in larger samples. Our GWAS findings—three loci associated with subjective well- being, two with depressive symptoms, and eleven with neuroticism—support the view that GWAS can successfully identify genetic associations with highly polygenic phenotypes in sufficiently large samples5,36. A striking finding is that two of our identified associations are with inversion polymorphisms. Second, our proxy-phenotype analyses further boosted power by exploiting the strong genetic overlap between our three phenotypes. These analyses identified two additional loci associated with neuroticism and two with both depressive symptoms and neuroticism. Through our quasi- replication tests, we also demonstrated how studying genetically overlapping phenotypes in concert can provide evidence on the credibility of GWAS findings. Our direct replication of the two genome-wide significant associations with depressive symptoms in an independent

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Chapter 3 depression sample provides further confirmation of those findings (Fig. 3.2b and Supplementary Table 15). We were able to assemble much larger samples than prior work in part because we combined data across heterogeneous phenotype measures. Our results reinforce the conclusions from our theoretical analysis that doing so increased our statistical power, but our strategy also has drawbacks. One is that mixing different measures may make any discovered associations more difficult to interpret. Research studying higher quality measures of the various facets of subjective well-being, depressive symptoms, and neuroticism is a critical next step. Our results can help facilitate such work because if the variants we identify are used as candidates, studies conducted in the smaller samples in which more fine-grained phenotype measures are available can be well powered. Another limitation of mixing different measures is that doing so may reduce the heritability of the resulting phenotype, if the measures are influenced by different genetic factors. Indeed, our estimates of SNP-based heritability10 for our three phenotypes are quite low: 0.040 (SE = 0.002) for subjective well-being, 0.047 (SE = 0.004) for depressive symptoms, and 0.091 (SE = 0.007) for neuroticism. We correspondingly find that polygenic scores constructed from all measured SNPs explain a low fraction of variance in independent samples: ~0.9% for subjective well- being, ~0.5% for depressive symptoms, and ~0.7% for neuroticism (Online Methods). The low heritabilities imply that even when polygenic scores can be estimated using much larger samples than ours, they are unlikely to attain enough predictive power to be clinically useful. According to our Bayesian calculations, the true explanatory power (corrected for winner’s curse) of the SNP with the largest posterior R2 is 0.003% for subjective well-being, 0.002% for depressive symptoms, and 0.011% for neuroticism (Supplementary Table 14). These effect sizes imply that in order to account for even a moderate share of the heritability, hundreds or (more likely) thousands of variants will be required. They also imply that our study’s power to detect variants of these effect sizes was not high—for example, our statistical power to detect the lead SNP with largest posterior R2 was only ~13%—which in turn means it is likely that there exist many variants with effect sizes comparable to our identified SNPs that evaded detection. These estimates suggest that many more loci will be found in studies with sample sizes realistically attainable in the near future. Consistent with this projection, when we meta- analyze the 54 SNPs reaching p < 10-5 in our analyses of depressive symptoms together with the 23andMe replication sample for depression, the number of genome-wide significant associations rises from 2 to 5 (Supplementary Table 15).

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Authors – Chapter 3

Aysu Okbay, Bart M.L. Baselmans, Jan-Emmanuel De Neve, Patrick Turley, Michel G. Nivard, Mark Alan Fontana, S. Fleur W. Meddens, Richard Karlsson Linnér, Cornelius A. Rietveld, Jaime Derringer, Jacob Gratten, James J. Lee, Jimmy Z. Liu, Ronald de Vlaming, Tarunveer S. Ahluwalia, Jadwiga Buchwald, Alana Cavadino, Alexis C. Frazier-Wood, Nicholas A. Furlotte, Victoria Garfield, Marie Henrike Geisel, Juan R. Gonzalez, Saskia Haitjema, Robert Karlsson, Sander W. van der Laan, Karl-Heinz Ladwig, Jari Lahti, Sven J. van der Lee, Penelope A. Lind, Tian Liu, Lindsay Matteson, Evelin Mihailov, Michael B. Miller, Camelia C. Minica, Ilja M. Nolte, Dennis Mook-Kanamori, Peter J. van der Most, Christopher Oldmeadow, Yong Qian, 3 Olli Raitakari, Rajesh Rawal, Anu Realo, Rico Rueedi, Börge Schmidt, Albert V. Smith, Evie Stergiakouli, Toshiko Tanaka, Kent Taylor, Gudmar Thorleifsson, Juho Wedenoja, Juergen Wellmann, Harm-Jan Westra, Sara M. Willems, Wei Zhao, LifeLines Cohort Study, Najaf Amin, Andrew Bakshi, Sven Bergmann, Gyda Bjornsdottir, Patricia A. Boyle, Samantha Cherney, Simon R. Cox, Gail Davies, Oliver S.P. Davis, Jun Ding, Nese Direk, Peter Eibich, Rebecca T. Emeny, Ghazaleh Fatemifar, Jessica D. Faul, Luigi Ferrucci, Andreas J. Forstner, Christian Gieger, Richa Gupta, Tamara B. Harris, Juliette M. Harris, Elizabeth G. Holliday, Jouke-Jan Hottenga, Philip L. De Jager, Marika A. Kaakinen, Eero Kajantie, Ville Karhunen, Ivana Kolcic, Meena Kumari, Lenore J. Launer, Lude Franke, Ruifang Li-Gao, David C. Liewald, Marisa Koini, Anu Loukola, Pedro Marques-Vidal, Grant W. Montgomery, Miriam A. Mosing, Lavinia Paternoster, Alison Pattie, Katja E. Petrovic, Laura Pulkki-Råback, Lydia Quaye, Katri Räikkönen, Igor Rudan, Rodney J. Scott, Jennifer A. Smith, Angelina R. Sutin, Maciej Trzaskowski, Anna E. Vinkhuyzen, Lei Yu, Delilah Zabaneh, John R. Attia, David A. Bennett, Klaus Berger, Lars Bertram, Dorret I. Boomsma, Harold Snieder, Shun-Chiao Chang, Francesco Cucca, Ian J. Deary, Cornelia M. van Duijn, Johan G. Eriksson, Ute Bültmann, Eco J.C. de Geus, Patrick J.F. Groenen, Vilmundur Gudnason, Torben Hansen, Catharine A. Hartman, Claire M.A. Haworth, Caroline Hayward, Andrew C. Heath, David A. Hinds, Elina Hyppönen, William G. Iacono, Marjo-Riitta Järvelin, Karl-Heinz Jöckel, Jaakko Kaprio, Sharon L.R. Kardia, Liisa Keltikangas-Järvinen, Peter Kraft, Laura D. Kubzansky, Terho Lehtimäki, Patrik K.E. Magnusson, Nicholas G. Martin, Matt McGue, Andres Metspalu, Melinda Mills, Renée de Mutsert, Albertine J. Oldehinkel, Gerard Pasterkamp, Nancy L. Pedersen, Robert Plomin, Ozren Polasek, Christine Power, Stephen S. Rich, Frits R. Rosendaal, Hester M. den Ruijter, David Schlessinger, Helena Schmidt, Rauli Svento, Reinhold Schmidt, Behrooz Z. Alizadeh, Thorkild I.A. Sørensen, Tim D. Spector, John M. Starr, Kari Stefansson, Andrew Steptoe, Antonio Terracciano, Unnur Thorsteinsdottir, A. Roy Thurik, Nicholas J. Timpson, Henning Tiemeier, André G. Uitterlinden, Peter Vollenweider, Gert G. Wagner, David R. Weir, Jian Yang, Dalton C. Conley, George Davey Smith, Albert Hofman, Magnus Johannesson, David I. Laibson, Sarah E. Medland, Michelle N. Meyer, Joseph K. Pickrell, Tõnu Esko, Robert F. Krueger, Jonathan P. Beauchamp, Philipp D. Koellinger, Daniel J. Benjamin, Meike Bartels, David Cesarini

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References – Chapter 3

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19. Meuwissen, T. H., Hayes, B. J. & Goddard, M. E. Prediction of total genetic value using genome-wide dense marker maps. Genetics 157, 1819–1829 (2001). 20. Vilhjálmsson, B. J. et al. Modeling Linkage Disequilibrium Increases Accuracy of Polygenic Risk Scores. Am. J. Hum. Genet. 97, 576–592 (2015). 21. Hyde, C. L. et al. Common genetic variants associated with major depressive disorder among individuals of European descent. Nat. Genet. 22. Roberts, R. E., Kaplan, G. a, Shema, S. J. & Strawbridge, W. J. Are the obese at greater risk for depression? Am. J. Epidemiol. 152, 163–170 (2000). 23. Glassman, A. J. et al. Smoking, , and major depression. J. Am. Med. 3 Assoc. 264, 1546–1549 (1990). 24. Shahab, L. & West, R. Differences in happiness between smokers, ex-smokers and never smokers: Cross-sectional findings from a national household survey. Drug Alcohol Depend. 121, 38–44 (2012). 25. Rugulies, R. Depression as a predictor for coronary heart disease: A review and meta- analysis. Am. J. Prev. Med. 23, 51–61 (2002). 26. Finucane, H. K. et al. Partitioning heritability by functional category using GWAS summary statistics. Nat. Genet. 47, 1228–1235 (2015). 27. Wood, A. R. et al. Defining the role of common variation in the genomic and biological architecture of adult human height. Nat. Genet. 46, 1173–1186 (2014). 28. Stetler, C. & Miller, G. E. Depression and hypothalamic-pituitary-adrenal activation: a quantitative summary of four decades of research. Psychosom. Med. 73, 114–126 (2011). 29. Seeman, P. Dopamine D2 receptors as treatment targets in schizophrenia. Clin. Schizophr. Relat. Psychoses 4, 56–73 (2010). 30. Vallone, D., Picetti, R. & Borrelli, E. Structure and function of dopamine receptors. Neurosci. Biobehav. Rev. 24, 125–132 (2000). 31. Ripke, S. et al. Biological insights from 108 schizophrenia-associated genetic loci. Nature 511, 421–427 (2014). 32. Shungin, D. et al. New genetic loci link adipose and insulin biology to body fat distribution. Nature 518, 187–196 (2015). 33. Kathiresan, S. et al. Common variants at 30 loci contribute to polygenic dyslipidemia. Nat. Genet. 41, 56–65 (2009). 34. Spencer, C. C. A. et al. Dissection of the genetics of Parkinson’s disease identifies an additional association 5' of SNCA and multiple associated haplotypes at 17q21. Hum. Mol. Genet. 20, 345–353 (2011). 35. Höglinger, G. U. et al. Identification of common variants influencing risk of the tauopathy progressive supranuclear palsy. Nat. Genet. 43, 699–705 (2011). 36. Sullivan, P. F. Don’t give up on GWAS. Mol. Psychiatry 17, 2–3 (2012).

75 Chapter 3 Neuroticism Neuroticism (c) (c) = 180,866), N ). Each approximately independent Each independent approximately ). 5  = 10 p -value SNP within the locus, as defined by our our by defined as the locus, within SNP -value p scale. The upper line marks the threshold dashed  Depressive symptoms ( symptoms Depressive   (b) (b) = 298,420), N shold for nominal significance ( nominal for shold . Each lead SNP is the lowestEach is SNP the . lead  -axis is the is -axis significance on a y Subjective well-being ( Subjective well-being ). ); the lower line marks the thre the marks line lower ); the 8  10  on (“lead SNP”) is marked by by marked is on (“lead SNP”) = 5 p Supplementary Note Supplementary -axis is chromosomal position, the and chromosomal is -axis x Manhattan plots of GWAS results. (a) (a) results. of GWAS plots Manhattan

= 170,911).The a b c N ( clumping algorithm ( clumping algorithm Figures andFigures tables – Chapter 3 Figure 3.1. for genome-wide significance ( for significance genome-wide significant associati genome-wide

76 Discovering the genetic architecture of the mind

Figure. 3.2. Genetic correlations with bars representing 95% confidence intervals. The correlations are estimated using bivariate LD Score (LDSC) regression. (a) Genetic correlations between subjective well-being, depressive symptoms, and neuroticism (“our three phenotypes”), as well as between our three phenotypes and height. (b) Genetic correlations between our three phenotypes and selected neuropsychiatric phenotypes. (c) Genetic correlations between our three phenotypes and selected physical health phenotypes. In (b) and (c), we report the negative of the estimated correlation with depressive symptoms and neuroticism (but not subjective well-being).

a 3

b

c

77 Chapter 3 = 368,890). = 368,890). N mined lead whether redicted Listed direction. ) lead SNPs identified in the the in identified SNPs lead ) a neuroticism are associated with subjective with subjective associated are neuroticism ) lead SNPs identified ) analyses lead in of the depressive b an independent sample ( ofcustomers 23andMe ). Bars represent 95% CIs (not adjusted for multiple testing). For For testing). multiple for (not adjusted CIs 95% represent Bars ). c ) and ( ) and b itive coefficients imply that the estimated effect is in the p the in is effect the estimated that imply itive coefficients to non-overlapping cohorts. In a separate lookup exercise, we exa separate In a cohorts. to non-overlapping ) lead SNPsanalysesin of identified lead the ) In quasi-replicationIn analyses,( whether we examined c nd neuroticism are associated with depression in with depression associated are nd neuroticism a b c belowthe each lead SNP nearest is gene. Figure 3.3. Quasi-replication and lookup of lead SNPs. lead of lookup and Quasi-replication Figure 3.3. subjective well-being meta-analyses are associated with depressive symptoms or neuroticism, ( neuroticism, or symptoms with depressive associated are meta-analyses well-being subjective ( and well-being, with subjective associated are symptoms The results from this lookup are depicted as green crosses in ( crosses green depicted as lookup are this from The results pos that so reference allele the we choose interpretational ease, well-being. The quasi-replication sample is always restricted always is The sample well-being. quasi-replication a for symptoms SNPs depressive

78 Discovering the genetic architecture of the mind

Figure 3.4. Results from selected biological analyses. (a) Estimates of the expected increase in the phenotypic variance accounted for by a SNP due to the SNP’s being in a given category  ( ), divided by the LD Score heritability of the phenotype ( ). Each estimate of  comes from a separate stratified LD Score regression, controlling for the 52 functional annotation categories in the “baseline model.” The bars represent 95% CIs (not adjusted for multiple testing). To benchmark the estimates, we compare them to those obtained from a recent study of height27. (b) Inversion polymorphism on chromosome 8 and the 7 genes for which the inversion is a significant cis-eQTL at FDR < 0.05. The upper half of the figure shows the Manhattan plot for neuroticism for the inversion and surrounding regions. The bottom half shows the squared correlation between the SNPs and the principal component that captures the 3 inversion. The inlay plots the relationship, for each SNP in the inversion region, between the SNP’s significance and its squared correlation with the principal component that captures the inversion. a

b

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Table 3.1 | Summary of polymorphisms identified across analyses. EA: effect allele. EAF: effect allele frequency. All effect sizes are reported in units of SDs per allele. “Quasi-Repl.”: phenotypes for which SNP was found to be nominally associated in quasi-replication analyses conducted in independent samples. *significant at the 5%-level, **significant at the 1%-level, ***significant at the 0.1%-level. #inversion-tagging polymorphism on chromosome 8. ##inversion-tagging polymorphism on chromosome 17. †proxy for rs6904596 (R2 = 0.98).

Panel A. Genome-Wide Significant Associations

Subjective Well-Being (SWB, N = 298,420) SNPID CHR BP EA EAF Beta SE R2 p-value N Quasi-Repl -9 rs3756290 5 130,951,750 A 0.24 -0.0177 0.0031 0.011% 9.610 286,851 -8 rs2075677 20 47,701,024 A 0.76 0.0175 0.0031 0.011% 1.510 288,454 DS** rs4958581 5 152,187,729 T 0.66 0.0153 0.0027 0.011% 2.310-8 294,043 DS*** Neuroticism (N = 170,908) SNPID CHR BP EA EAF Beta SE R2 p-value N Quasi-Repl rs2572431# 8 11,105,077 T 0.41 0.0283 0.0035 0.039% 4.210-16 170,908 SWB* rs193236081## 17 44,142,332 T 0.77 -0.0284 0.0045 0.028% 6.310-11 151,297

-10  rs10960103 9 11,699,270 C 0.77 0.0264 0.0038 0.024% 2.110 165,380     , rs4938021 11 113,364,803 T 0.34 0.0233 0.0037 0.024% 4.010-10 159,900    SWB* rs139237746 11 10,253,183 T 0.51 -0.0204 0.0034 0.021% 2.610-9 170,908 -9  rs1557341 18 35,127,427 A 0.34 0.0213 0.0036 0.021% 5.610 165,579    rs12938775 17 2,574,821 A 0.47 -0.0202 0.0035 0.020% 8.510-9 163,283 SWB* rs12961969 18 35,364,098 A 0.2 0.0250 0.0045 0.020% 2.210-8 156,758 rs35688236 3 34,582,993 A 0.69 0.0213 0.0037 0.019% 2.410-8 161,636 rs2150462 9 23,316,330 C 0.26 -0.0217 0.0038 0.018% 2.710-8 170,907

-8  rs12903563 15 78,033,735 T 0.50 0.0198 0.0036 0.020% 2.910 157,562   ,SWB* Depressive Symptoms (DS, N = 180,866) SNPID CHR BP EA EAF Beta SE R2 p-value N Quasi-Repl/Repl -9  rs7973260 12 118,375,486 A 0.19 0.0306 0.0051 0.029% 1.810 124,498    rs62100776 18 50,754,633 A 0.56 -0.0252 0.0044 0.031% 8.5 10-9 105,739  ,SWB*     Panel B. SNPs Identified via Proxy-Phenotype Analyses of SWB Loci with p-value<10-4

Depressive Symptoms in Non-Overlapping Cohorts 2 SNPID CHR BP EA EAF BetaDS SEDS R pDS Bonferroni NDS -5 rs4346787† 6 27,491,299 A 0.113 -0.023 0.0059 0.011% 9.810 0.0160 142,265 -4 rs4481363 5 164,483,794 A 0.524 0.014 0.0038 0.009% 3.110 0.0499 142,265

Neuroticism in Non-Overlapping Cohorts 2 SNPID CHR BP EA EAF Betaneuro SEneuro R pneuro Bonferroni Nneuro rs10838738 11 47,663,049 A 0.49 0.0178 0.0039 0.016% 5.010-6 0.0009 131,864 rs10774909 12 117,674,129 C 0.52 -0.0150 0.0039 0.011% 1.210-4 0.0203 131,235 rs6904596 6 27,491,299 A 0.09 -0.0264 0.0072 0.012% 2.510-4 0.0423 116,335 rs4481363 5 164,474,719 A 0.49 0.0151 0.0040 0.011% 1.910-4 0.0316 122,592

80 An epigenome-wide association study meta-analysis of educational attainment

“Keeping an open mind is a virtue—but, as the space engineer James Oberg once said, not so open that your brains fall out.” Carl Sagan

“Study hard what interests you the most in the most undisciplined, irreverent and original manner possible.” Richard Feynman

Based on Karlsson Linnér et al. (2017). Molecular Psychiatry. 4

Chapter 4

Abstract

The epigenome is associated with biological factors, such as disease status, and environmental factors, such as smoking, alcohol consumption, and body mass index. Although there is a widespread that environmental influences on the epigenome are pervasive and profound, there has been little evidence to date in humans with respect to environmental factors that are biologically distal. Here, we provide evidence on the associations between epigenetic modifications—in our case, CpG methylation—and educational attainment (EA), a biologically distal environmental factor that is arguably among the most important life-shaping experiences for individuals. Specifically, we report the results of an epigenome-wide association study meta-analysis of EA based on data from 27 cohort studies with a total of 10,767 individuals. We find nine CpG probes significantly associated with EA. However, robustness analyses show that all nine probes have previously been found to be associated with smoking. Only two associations remain when we perform a sensitivity analysis in the subset of never-smokers, and these two probes are known to be strongly associated with maternal smoking during pregnancy, and thus their association with EA could be due to correlation between EA and maternal smoking. Moreover, the effect sizes of the associations with EA are far smaller than the known associations with the biologically proximal environmental factors alcohol consumption, BMI, smoking and maternal smoking during pregnancy. Follow-up analyses that combine the effects of many probes also point to small methylation associations with EA that are highly correlated with the combined effects of smoking. If our findings regarding EA can be generalized to other biologically distal environmental factors, then they cast doubt on the hypothesis that such factors have large effects on the epigenome.

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Introduction

The epigenome has been shown to be associated with biological factors such as disease status1,2. While there is a widespread perception in the social sciences that a variety of social environmental factors have an effect on the epigenome3–10, virtually all of the replicated evidence to date in humans relates to environmental factors that have a fairly direct biological impact, such as smoking11–13, alcohol consumption14,15, and excess energy intake resulting in increased body mass index (BMI)16,17. Here, we study the associations between epigenetic modifications—specifically, the methylation of cytosine-guanine pairs connected by a phosphate link (CpG methylation)—and educational attainment (EA). EA is biologically distal, and yet it is arguably among the most important life-shaping experiences for individuals in modern societies. EA therefore provides a useful test case for whether and to what extent biologically distal environmental factors may affect the epigenome. In this paper, we report the results of a large-scale epigenome-wide association study (EWAS) 4 meta-analysis of EA. By meta-analyzing harmonized EWAS results across 27 cohort studies, we were able to attain an overall sample size of 10,767 individuals of recent European ancestry, making this study one of the largest EWAS to date13,15,18. A large sample size is important because little is known about plausible EWAS effect sizes for complex phenotypes such as EA, and an underpowered analysis would run a high risk of both false negatives and false positives19,20. As is standard in EWAS, we used data on CpG DNA methylation. This is the most widely studied epigenetic mark in large cohort studies1. Methylation level was measured by the beta value, which is the proportion of methylated molecules at each CpG locus, a continuous variable ranging between 0 and 121. The Illumina 450k Bead Chip measures methylation levels at over 480,000 loci in human DNA and has been used in many cohort studies1. We report results from two common methods for the analysis of such methylation datasets. The first main analysis is an EWAS, which considers regression models for each CpG loci with EA. Using the EWAS results we then performed a series of follow-up analyses: enrichment analyses, prediction analyses, correlation with tissue-specific methylation, and gene-expression analysis (Supplementary Note). The second main analysis uses the ‘epigenetic clock’22,23 method, which employs a weighted linear combination of a subset of probes (i.e., measured CpG methylation loci) to predict an individual's so-called ‘biological age.’ The resulting variable can then be linked to phenotypes and health outcomes. EWAS studies to date have found associations between DNA methylation and, for example, smoking11,12, body mass index (BMI)16,24, traumatic stress25, alcohol consumption14,26, and cancer2,27. In prior work, an age-accelerated epigenetic clock (i.e. an older biological than chronological age) has been linked to increased mortality risk28, poorer cognitive and physical health29, greater Alzheimer’s disease pathology30, Down’s syndrome31, high lifetime stress32, and lower income33.

Methods

Participating cohorts

We obtained summary-level association statistics from 27 independent cohort studies across 15 cohorts located in Europe, the US, and Australia (Supplementary Table S1.1). The total sample size comprised 10,767 individuals of recent European ancestry. All participants provided written informed consent, and all contributing cohorts confirmed compliance with their Local Research Ethics Committees or Institutional Review Boards.

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Educational attainment measures

Following earlier work of the Social Science Genetic Association Consortium (SSGAC)34,35, EA was harmonized across cohorts. The EA variable is defined in accordance with the ISCED 1997 classification (UNESCO), leading to seven categories of EA that are internationally comparable. The categories are translated into US years-of-schooling equivalents, which have a quantitative interpretation (Supplementary Table S1.2–3).

Participant inclusion criteria

To be included in the current analysis, participants had to satisfy six criteria: 1) participants were assessed for educational attainment at or after 25 years of age; 2) participants were of European ancestry; 3) all relevant covariate data were available for each participant; 4) participants passed the cohort-level methylation quality control; 5) participants passed cohort- specific standard quality controls (for example, genetic outliers were excluded); and 6) participants were not disease cases from a case/control study.

DNA methylation measurement and cohort-level quality control

Whole-blood DNA CpG methylation was measured genome-wide in all cohorts using the Illumina 450k Human Methylation chip. We standardized the cohort-level quality control and pre-processing of the methylation data as much as possible, while ensuring some degree of flexibility to keep the implementation feasible for all cohorts (leading to slight variation in preprocessing across cohorts, as is common13,15,17). Cohort-specific information regarding technical treatment of the data, such as background-correction36, normalisation37, and quality control, is reported in Supplementary Table S1.4.

Epigenome-wide association study (EWAS)

Our analyses were performed in accordance with a pre-registered analysis plan archived at Open Science Framework (OSF) in September 2015 (available at: https://osf.io/9v3nk/). We first performed a meta-analysis of the EWAS of EA to investigate associations with individual methylation markers (Supplementary Note 2). As is standard, the EWAS was performed as a set of linear regressions in each cohort, one methylation marker at a time, with the methylation beta value (0–1) as the dependent variable. The key independent variable was EA. We estimated two regression models that differ in the set of covariates included. In the basic model, the covariates were age, sex, imputed or measured white blood-cell counts, technical covariates from the methylation array, and four genetic principal components to account for population stratification. In the adjusted model, we additionally controlled for body mass index (BMI, kg/m2), smoker status (three categories: current, previous, or never smoker), an interaction term between age and sex, and a quadratic term for age. Since BMI and smoking are correlated with EA38,39 and known to be associated with methylation13,17, the basic model may identify associations with EA that are actually due to BMI or smoking. While the adjusted model reduces that risk, it may also reduce power to identify true associations with EA (by controlling for factors that are correlated with EA). While we present the results for both models, we focus on the adjusted model because it is more conservative. Details of cohort-specific covariates are presented in Supplementary Table S1.4.

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EWAS quality control and meta-analysis

Each participating cohort uploaded EWAS summary statistics to a central secure server for quality control (QC) and meta-analysis. The number of CpG probes filtered at each step of the QC is presented in Supplementary Table S1.5. We removed: (a) probes with missing P value, standard error, or coefficient estimate; (b) probes with a call rate less than 95%; (c) probes with a combined sample size less than 1,000; (d) probes not available in the probe-annotation reference by Price et al. (2013)40; (e) CpH probes (H = A/C/T); (f) probes on the sex chromosomes; and (g) cross-reactive probes highlighted in a recent paper by Chen et al.41. We performed a sample-size-weighted meta-analysis of the cleaned results using METAL42. We used single genomic control, as is common in genome-wide association studies (GWAS)43, to stringently correct the meta-analysis P values for possible unaccounted-for population stratification44. Probes with a P value less than 110–7, a commonly-used threshold in EWAS1 that we pre-specified in the analysis plan, were considered epigenome-wide significant 4 associations.

Epigenetic clock analyses

To construct our epigenetic clock variables (Supplementary Note 3), the cohort-level raw beta- value data was entered into the online Horvath calculator23, as per our pre-registered analysis plan. The “normalize data” and “advanced analysis for Blood Data” options were selected. The following variables were selected from the calculator’s output for subsequent analysis: • Clock 1. Horvath age acceleration residuals, which are the residuals from the regression of chronological age on Horvath age. • Clock 2. White blood cell count adjusted Horvath age acceleration, which is the residual from Clock 1 after additional covariate adjustment for imputed white blood cell counts. • Clock 3. White blood cell count adjusted Hannum age acceleration, which is the same as Clock 2 but with the Hannum age prediction in place of the Horvath prediction. • Clock 4. Cell-count enriched Hannum age acceleration, which is the basic Hannum predictor plus a weighted average of aging-associated cell counts. This index has been found to have the strongest association with mortality45. These Clock measures are annotated in the Horvath software as follows: ‘AgeAccelerationResidual’, ‘AAHOAdjCellCounts’, ‘AAHAAdjCellCounts’, and ‘BioAge4HAAdjAge’. We analyzed two regression models for each clock variable, both with EA as an independent variable and a clock variable as the dependent variable. In the basic age acceleration model, we control for chronological age, and in the adjusted age acceleration model, we additionally control for BMI and smoker status (current, previous, or never smoker). In total, in each adult cohort, we estimated eight regressions: each of the two models with each of the four clock variables as a dependent variable. For each of the eight regressions, we performed a sample-size-weighted meta-analysis of the cohort-level results.

Polygenic predictions with polygenic methylation score

We performed a prediction analysis with polygenic methylation scores (PGMS), analogous to polygenic-score prediction in the GWAS literature (Supplementary Note 6). We tested the predictive power in three independent adult cohort studies: Lothian Birth Cohort 1936 (LBC1936, n = 918), RS-BIOS (Rotterdam Study – BIOS, n = 671), and RS3 (Rotterdam Study 3, n = 728). We re-ran the EWAS meta-analysis for each prediction cohort to obtain the weights

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Chapter 4 for the PGMS, while holding out the prediction cohort to avoid overfitting. We constructed the PGMS for each individual as a weighed sum of the individual’s methylation markers’ beta values and the EWAS effect-sizes, using the Z statistics from the EWAS as weights. (The Z statistics were used instead of the EWAS coefficients because CpG methylation is the dependent variable in the EWAS regression.) We constructed PGMS using two different thresholds for probe inclusion, P < 110–5 and P < 110–7, with weights from the basic and adjusted EWAS models, for a total of four PGMS in each prediction cohort. To shed light on the direction of causation of epigenetic associations, we used a fourth prediction cohort study, a sample of children in the ALSPAC ARIES cohort46. We constructed the PGMS using the same approach as described above, in this case using data from cord-blood- based DNA methylation at birth. The outcome variables in this cohort were average educational achievement test scores (Key Stages 1–447) from age 7 up to age 16 years. To examine the relationship between epigenetic and genetic associations, we also constructed a single-nucleotide polymorphism polygenic score (SNP PGS) for EA. We used SNP genotype data available in the three adult prediction cohort studies (LBC1936, RS-BIOS, and RS3). We constructed the SNP PGS in each cohort as a weighted sum of the individual genotypes from all available genotyped SNPs, with GWAS meta-analysis coefficients as weights. We obtained the coefficients by re-running the largest GWAS meta-analysis to date of EA34 after excluding our prediction cohorts (LBC1936, RS-BIOS, and RS3). We evaluated the predictive power of the PGMS by examining the incremental coefficient of determination (incremental R2) for predicting EA (or test scores in ALSPAC ARIES). The incremental R2 is the difference in R2 between the regression model with only covariates, and the same regression model that additionally includes the PGMS as a predictor. The covariate- only models in the LBC1936, RS-BIOS, and RS3 cohorts controlled for age, sex, and the SNP PGS. In the ALSPAC ARIES cohort we controlled for age at assessment and sex. In the ALSPAC ARIES cohort, when we investigate maternal smoking as a potential confound for our EA associations, we add maternal smoking to the set of covariates. We finally restricted the ALSPAC ARIES cohort to children with non-smoking mothers. To investigate a possible interaction effect between the PGMS and SNP PGS, we re-estimated the regression model after adding an interaction term between the PGMS and the SNP PGS, and the incremental R2 was calculated as the difference in R2 relative to the model that included the PGMS and the SNP PGS as additive main effects. Results

Descriptive statistics

Summary statistics from the 27 independent cohort studies from the 15 contributing cohorts are shown in Supplementary Table S1.1. The mean age at reporting ranges from 26.6 to 79.1 years, and the sample size ranges from 48 to 1,658, with a mean of 399 individuals per cohort. The mean cohort EA ranges from 8.6 to 18.3 years of education, and the sample-size-weighted mean is 13.6 (SD = 3.62). The meta-analysis sample is 54.1% female.

EWAS

The QC filtering is reported in Supplementary Table S1.5. We inspected the quantile-quantile (QQ) plot of the filtered EWAS results from each contributing cohort as part of the QC procedure before meta-analysis. The genomic inflation factor ( ), defined as the ratio of the median of the empirically observed chi-square test statistics to the expected median under the

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Discovering the genetic architecture of the mind null, had a mean across the cohorts of 1.02 for the adjusted model (SD = 0.18). We report the cohort-level genomic inflation factor after probe filtering in Supplementary Table S1.5. The variation in   across cohorts was comparable to that from EWAS performed in cohorts of similar sample size12. We applied genomic control at the cohort level, which is a conservative method of controlling for residual population stratification that may remain even despite the 44 regression controls for principal components . The meta-analysis   was 1.19 for the basic model and 1.06 for the adjusted model. Figure 4.1 shows the Manhattan plot for the meta-analysis results of the adjusted model. The Manhattan plot for the basic model is reported in the Supplementary Note, together with the QQ plots for the basic and adjusted model. In the basic model there were 37 CpG probes associated with EA at our preregistered epigenome-wide P value threshold (P < 10–7); these results are reported in Supplementary Table S1.6a. In the adjusted model there were 9 associated probes, listed in Table 4.1 (with additional details in Supplementary Table S1.7a), all of which were also associated in the basic model. We hereafter refer to the adjusted model’s 4 9 associated probes as the “lead probes.” In Supplementary Note 2.4.1 we present the association results with false discovery rate (FDR) less than 0.05, but since this threshold was not pre-specified in the analysis plan we do not present these results as main findings. To investigate how the EWAS results look at a regional level, we analyzed the distribution of the EWAS associations across the genome by performing enrichment tests for methylation density regions40 (the so-called “HIL” categories; Supplementary Note 5.2). We found that the number of probes with P < 10–7 is more or less proportional to the total number of probes in every region and that there is enrichment for association in all four methylation density categories: high-density CpG islands (HC), intermediate-density CpG islands (IC), intermediate-density CpG islands bordering HCs (ICshore), and non-islands (LC). The effect sizes of the associations for the nine lead probes are shown in Table 4.1. The coefficients of determination (R2’s) range from 0.3% to 0.7%. To put these effect sizes in perspective, Figure 4.2 and Supplementary Table S1.8 compare the R2’s for the top 50 probes in our adjusted model with the top 50 probes from recent large-scale EWAS of smoking13, maternal smoking12, alcohol consumption15, and BMI17, as well as the top 50 GWAS SNP associations with EA34. The EA EWAS associations of our study are an order of magnitude larger than the largest EA SNP effect sizes. However, our EWAS associations are small in magnitude relative to the EWAS associations reported for more biologically proximal environmental factors. BMI is the most similar to EA, with R2’s of associated probes approximately 20–50% larger than those for EA. Relative to the largest R2 for an EA-associated probe, the largest effect for probes associated with smoking and maternal smoking are greater by factors of roughly three and 17, respectively.

Lookup of lead probes in published EWAS of smoking

Since our smoker-status control variable is coarse and discrete (current, former, or never smoker), we were concerned that the adjusted EWAS model might not have adequately controlled for exposure to smoking (i.e., amount and duration of smoking and exposure to second-hand smoke). Therefore, we performed a lookup of our lead probes in the published EWAS on smoking (Supplementary Note 4 and Supplementary Table S1.10). We found that all nine lead probes have previously been associated with smoking. The results of this lookup motivated our analysis of the never-smoker subsample, discussed next.

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Robustness of EWAS results in the never-smoker subsample

To minimize the possible confounding effect of smoking on the association between EA and CpG methylation, we conducted a set of analyzes that we did not anticipate when we preregistered our analysis plan. Specifically, we went back to the cohorts and asked them to re- conduct their EWAS, this time restricting the analysis to individuals that self-reported as never smokers. After following the same QC steps as above, we performed a new meta-analysis of these results (n = 5,175). In this subsample, the effect-size estimates were smaller by at least 60% for seven of the nine lead probes (see Table 4.1 and panel A in Figure 4.3), whereas two probes (cg12803068 and cg22132788) had similar effect-size estimates as in the full sample (statistically distinguishable from zero with P = 1.4810–4 and P = 4.3510–4, respectively). These two probes, however— both in proximity to the gene MYO1G—have been found to be associated with maternal smoking during pregnancy, and the effects on the methylation of this gene are persistent when measured at age 17 in the offspring12,48. This influence has been shown to continue through to middle age49. We cannot distinguish between the hypothesis that these probes have some true association with EA and the hypothesis that their apparent association with EA is entirely driven by more maternal smoking during pregnancy among lower-EA individuals. We also cannot rule out that the probes’ association with EA is driven by second-hand smoke exposure, which could also be correlated with EA. To assess the how widely such confounding may affect the EA results, in Panel B of Figure 4.3 we compare the effect sizes of all the probes associated with EA at P < 10–5 in the adjusted EWAS model to the effect sizes found for the same probes in EWAS meta-analyses of smoking13 and maternal smoking12 (see also Supplementary Table S.1.11). Many of the EA- associated probes are also associated with smoking or maternal smoking, strongly suggesting that residual smoking exposure (i.e., the misclassification of amount and duration of smoking, and second-hand smoke that is not captured by the smoking covariate) and maternal smoking remain potential confounding factors for the probe associations with EA, even in the subsample of individuals who are self-reported never-smokers.

Epigenetic clock associations with EA

Two cohorts, FINRISK and MCCS, did not contribute to the epigenetic clock analyses. Therefore, the sample sizes for these analyses were smaller than for the EWAS meta-analysis: 8,173 for the basic age acceleration model and 7,691 for the adjusted age acceleration model (the difference being due to the lack of covariates for some individuals). The effect-size estimates are presented in Figure 4.4 and Supplementary Table S1.9. There was no evidence for an association between EA and Clocks 1, 2, or 3, but the association between EA and Clock 4 was strong (P = 3.5110–6 and P = 4.5110–4 in the basic and adjusted age acceleration model, respectively). The point estimates were small, however: using Clock 4, each year of EA was associated with a 0.071-year (i.e., 26-day) reduction in age acceleration in the basic model and a 0.055-year (i.e., 20-day) reduction in the adjusted model. Overall then, higher educational attainment was associated with slightly younger biological age when compared with chronological age. We note that the epigenetic clock that was found to be associated with EA, Clock 4, has previously been found to be the most predictive epigenetic-clock measure of mortality45.

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Prediction using polygenic methylation scores

The incremental R2’s from the prediction of EA with polygenic methylation scores (PGMS) in our adult prediction cohort studies, the LBC1936, RS-BIOS, and RS3, are reported in Supplementary Table S1.13a and Figure 4.5. Across the four PGMS constructed with weights from the basic and adjusted model, and with the two probe-inclusion thresholds (P < 10–5 and P < 10–7), the incremental R2’s ranged from 1.4% to 2.0% (P = 3.2810–8 and lower). There was also weak evidence for an interaction between the PGMS and the SNP PGS in predicting EA, with the R2’s for the interaction term ranging from 0.1% to 0.3% (P values ranged from 0.01 to 0.12). In the subsample of never-smokers the PGMS (constructed with weights derived from the full EWAS sample), the PGMS is far less predictive, with incremental R2’s ranging from 0.3% to 0.9% (Figure 4.5 and Supplementary Table S1.13b). The two PGMS constructed from probes with P < 110–5 in the EWAS were associated with EA at P < 0.05, while the two PGMS 4 constructed only from the lead probes with P < 110–7 were not (P > 0.05). No interaction effect was found between the PGMS and the SNP PGS in the never-smoker subsample. To further investigate confounding by smoking in the prediction analysis, we examined the correlations between our PGMS constructed from the lead probes (i.e., those associated with EA at significance threshold P < 110–7) in either our basic or adjusted model and a PGMS for smoking (see Supplementary Note 6.2.2 for details). For the smoking PGMS, we use the 187 probes that were identified at epigenome-wide significance (P < 110–7) and then successfully replicated in a recent EWAS of smoking50. We examine the PGMS correlations in our full prediction samples, not restricted to never-smokers. For the EA PGMS from our basic model, we find a correlation with the smoking PGMS of –0.96 in RS3, –0.94 in RS-BIOS, and –0.93 in LBC1936. For the EA PGMS from our adjusted model, the correlations are –0.90, –0.89, and –0.91, respectively. In all cases, the nearly perfect correlation between the smoking and EA methylation scores strongly suggests that smoking status confounds the EWAS associations with EA. Turning to the child sample in the ALSPAC ARIES cohort46,48, we examined whether a PGMS constructed from methylation assessed in cord blood samples at birth was predictive of four prospective measures of educational achievement test scores (Key Stage 1–447), collected between ages 7 and 16 (Supplementary Note 6.1.1). The results are reported in Supplementary Table S1.13c. The largest incremental R2 was 0.73% (P = 0.0094), and it was attained in the model predicting school performance at age 14–16 (i.e., the Key Stage 4 test scores). However, once maternal smoking status was added as a control variable, the predictive power of the PGMS became essentially zero (incremental R2 = 0.05%, P = 0.234). This suggests that the confounding effects of maternal smoking strongly influenced the predictive power of the PGMS for EA. We draw two conclusions from these results from the child sample. First, they reinforce the concern that maternal smoking was a major confound for any probe associations with EA. Second, they suggest that any true methylation-EA associations were unlikely to be driven by a causal effect of methylation on EA.

Overlap between EWAS probes and published GWAS associations

To supplement our polygenic-score analyses of the overlap between epigenetic and genetic associations, we next investigated whether our lead probes are located at loci that contain SNPs previously identified in GWAS of EA and smoking (Supplementary Note 5). Considering jointly the 141 approximately independent EWAS probes with P < 10–4, we did not find evidence of enrichment for either EA-linked SNPs (P = 0.206) or smoking-linked SNPs (P =

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0.504). Considering the probes individually, one probe (cg17939805) was found to be in the same genomic region as a SNP (rs9956387) associated with EA (with a genomic distance of 607 bp), whereas no probes were close to SNPs previously identified as linked to smoking.

Correlation of EWAS results with tissue-specific methylation

To answer the question of whether our EWAS associations are correlated with any tissue- specific DNA methylation, we utilized the tissue-specific methylation data made available by the Epigenomic Roadmap Consortium51. That data was used to calculate tissue-specific deviations from average cross-tissue methylation at the loci corresponding to the EWAS CpG probes associated with EA at a P value less than 110–4 (Supplementary Note 7). We examined the correlation between these tissue-specific deviations and the EWAS association test statistics (Z statistics) of the probes, using the results from the adjusted EWAS model. We report the results in Figure 4.6 and in Supplementary Table S1.14. The strongest correlations were found for primary haematopoietic stem cells G-CSF-mobilized female and IMR90 fetal lung fibroblast. Intermediate-strength correlations were found across multiple, seemingly unrelated tissues, while no correlations of relevant magnitudes were found with the brain tissues available in the Roadmap. We interpret the lack of correlation with tissues plausibly related to EA (such as brain tissues) as supporting the conclusion that the EWAS results are driven by confounding factors rather than by a true association with EA.

Pathway analysis with gene-expression data

Using the GTEx52 expression data and the webtool ‘Functional mapping and annotation of genetic associations’ (FUMA)53 we performed a pathway analysis. The analysis used the GTEx gene-expression levels to cluster the 29 genes physically closest to the EA-associated (at P < 110–5) CpG probes of the adjusted model (Supplementary Note 8). The results of the expression analysis are displayed in Supplementary Figure 4. We find that the genes closest to the EA-associated probes are expressed across multiple tissues that have no clear relationship to EA (such as blood tissues, among many other); for further discussion, see Supplementary Note. Overall, these results are consistent with the hypothesis that the EWAS results are driven by confounding factors. Discussion

This study provides one of the first large-scale investigations in humans of epigenetic changes linked to a biologically distal environmental factor. In our EWAS meta-analysis—one of the largest EWAS conducted to date—we found nine CpG probes associated with EA. Each of these probes explains 0.3% to 0.7% of the variance in EA—effect sizes somewhat smaller than the largest EWAS effects that have been observed for BMI and many times smaller than those observed for alcohol consumption, smoking, and especially maternal smoking during pregnancy. When we restrict our analysis to the subsample of never-smokers, the effect sizes of seven out of the nine lead probes are substantially attenuated. Moreover, the other two lead probes have been found in previous work to be strongly associated with maternal smoking during pregnancy12. More generally, comparing our own results to those from previous EWAS highlights a variety of factors correlated with EA, including not only maternal smoking but also alcohol consumption and BMI, as potentially major confounding factors for the EA associations we detect. We also cannot rule out that other factors correlated with EA, such as exposure to second-hand smoke, could confound the EA associations. This should be taken into account in future endeavors of associating methylation with biologically distal factors that are known to

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Discovering the genetic architecture of the mind correlate with environmental factors that have a fairly direct biological impact, such as smoking. Convincingly establishing a causal effect of EA would require analyzing a sample with quasi- random variation in EA, such as a sample in which some individuals were educated after an increase in the number of years of compulsory schooling and other individuals were educated before the law change54. We are not aware of large EWAS samples with quasi-random variation at present, but we anticipate that such samples will become available as methylation becomes more widely measured. Although the EWAS we report here is among the largest conducted to date, our sample size of 10,767 individuals is only large enough to identify nine probes associated with EA at the conventional epigenome-wide significance threshold. Subsequent EWAS conducted in larger samples that have sufficient statistical power to identify a much larger number of EA-associated probes will enable more extensive investigations of overlap with probes associated with other 4 phenotypes than were possible from our results, as well as analyses of the biological functions of the probes. Besides limited statistical power, other limitations of our study, common to EWAS research designs, are that we study methylation cross-sectionally and not longitudinally and that we only investigate CpG methylation and no other types of epigenetic modifications. Also, our study focuses on single CpG sites; future studies could consider additional analytical approaches to assess regions of differential methylation (e.g., genes). Once suitable methods have been developed, it would also be of interest to estimate the overall proportion of variance in EA that can be attributed to individual differences in DNA methylation patterns.

Conclusion

One plausible hypothesis is that environmental influences on the epigenome—even those due to everyday, social environmental factors—are pervasive and profound3. According to the logic of this view, a major life experience that occurs over many years, such as EA, should leave a powerful imprint on the epigenome. Motivated by this view and by the evidence of large EWAS effects in studies of lifestyle factors, when we embarked on this project we entertained the hypothesis that we might find large associations between EA and methylation. We also entertained the alternative hypothesis that EA, because it is so biologically distal, may exhibit much weaker associations with methylation. While our results do not allow us to distinguish how much of the effects we find are due to true associations with EA and how much are due to confounding factors, they strongly suggest that the effect sizes we estimate are an upper bound on the effect sizes of any true methylation associations with EA. These upper-bound effect sizes are far smaller than associations with more biologically proximal environmental factors that have been studied. If our results can be generalized beyond EA to other biologically distal environmental factors, then they cast doubt on the hypothesis that such factors have large effects on the epigenome.

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Authors – Chapter 4

Richard Karlsson Linnér, Riccardo E Marioni, Cornelius A Rietveld, Andrew J Simpkin, Neil M Davies, Kyoko Watanabe, Nicola J Armstrong, Kirsi Auro, Clemens Baumbach, Marc Jan Bonder, Jadwiga Buchwald, Giovanni Fiorito, Khadeeja Ismail, Stella Iurato, Anni Joensuu, Pauliina Karell, Silva Kasela, Jari Lahti, Allan F McRae, Pooja R Mandaviya, Ilkka Seppälä, Yunzhang Wang, Laura Baglietto, Elisabeth B Binder, Sarah E Harris, Allison M Hodge, Steve Horvath, Mikko Hurme, Magnus Johannesson, Antti Latvala, Karen A Mather, Sarah E Medland, Andres Metspalu, Lili Milani, Roger L Milne, Alison Pattie, Nancy L Pedersen, Annette Peters, Silvia Polidoro, Katri Räikkönen, Gianluca Severi, John M Starr, Lisette Stolk, Melanie Waldenberger, Johan G Eriksson, Tõnu Esko, Lude Franke, Christian Gieger, Graham G Giles, Sara Hägg, Pekka Jousilahti, Jaakko Kaprio, Mika Kähönen, Terho Lehtimäki, Nicholas G Martin, Joyce B C van Meurs, Miina Ollikainen, Markus Perola, Danielle Posthuma, Olli T Raitakari, Perminder S Sachdev, Erdogan Taskesen, André G Uitterlinden, Paolo Vineis, Cisca Wijmenga, Margaret J Wright, Caroline Relton, George Davey Smith, Ian J Deary, Philipp D Koellinger, Daniel J Benjamin

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References – Chapter 4

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34. Okbay, A. et al. Genome-wide association study identifies 74 loci associated with educational attainment. Nature 533, 539–542 (2016). 35. Rietveld, C. et al. GWAS of 126,559 individuals identifies genetic variants associated with educational attainment. Science 340, 1467–71 (2013). 36. Triche, T. J., Weisenberger, D. J., Van Den Berg, D., Laird, P. W. & Siegmund, K. D. Low-level processing of Illumina Infinium DNA Methylation BeadArrays. Nucleic Acids Res. 41, 1–11 (2013). 37. Fortin, J.-P. et al. Functional normalization of 450k methylation array data improves replication in large cancer studies. Genome Biol. 15, 503 (2014). 38. Johnson, W. et al. Does education confer a culture of healthy behavior? Smoking and drinking patterns in Danish twins. Am. J. Epidemiol. 173, 55–63 (2011). 39. Hermann, S. et al. The Association of Education with Body Mass Index and Waist 4 Circumference in the EPIC-PANACEA Study. BMC Public Health 11, 169 (2011). 40. Price, M. E. et al. Additional annotation enhances potential for biologically-relevant analysis of the Illumina Infinium HumanMethylation450 BeadChip array. Epigenetics Chromatin 6, 4 (2013). 41. Chen, Y. et al. Discovery of cross-reactive probes and polymorphic CpGs in the Illumina Infinium HumanMethylation450 microarray. Epigenetics 8, 203–9 (2013). 42. Willer, C. J., Li, Y. & Abecasis, G. R. METAL: fast and efficient meta-analysis of genomewide association scans. Bioinformatics 26, 2190–2191 (2010). 43. Devlin, B. & Roeder, K. Genomic control for association studies. Biometrics 55, 997– 1004 (1999). 44. van Iterson, M., van Zwet, E., the BIOS Consortium & Heijmans, B. T. Controlling bias and inflation in epigenome- and transcriptome-wide association studies using the empirical null distribution. Genome Biol. 18, 1–13 (2017). 45. Chen, B. H. et al. DNA methylation-based measures of biological age: Meta-analysis predicting time to death. Aging (Albany. NY). 8, 1844–1865 (2016). 46. Relton, C. L. et al. Data resource profile: Accessible resource for integrated epigenomic studies (ARIES). Int. J. Epidemiol. 44, 1181–1190 (2015). 47. GOV.UK. National Curriculum. GOV.UK (2016). 48. Richmond, R. C. et al. Prenatal exposure to maternal smoking and offspring DNA methylation across the lifecourse: Findings from the Avon Longitudinal Study of Parents and Children (ALSPAC). Hum. Mol. Genet. 24, 2201–2217 (2015). 49. Richmond, R. C., Suderman, M., Langdon, R., Relton, C. L. & Davey Smith, G. DNA methylation as a marker for prenatal smoke exposure in adults. bioRxiv (2017). 50. Zeilinger, S. et al. Tobacco Smoking Leads to Extensive Genome-Wide Changes in DNA Methylation. PLoS One 8, (2013). 51. Kundaje, A. et al. Integrative analysis of 111 reference human . Nature 518, 317–330 (2015). 52. The GTEx Consortium. The Genotype-Tissue Expression (GTEx) project. Nat. Genet. 45, 580–5 (2013). 53. Watanabe, K., Taskesen, E., van Bochoven, A. & Posthuma, D. FUMA: Functional

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mapping and annotation of genetic associations. Manuscr. Submitt. Publ. (2017). 54. Lager, A. C. J. & Torssander, J. Causal effect of education on mortality in a quasi- experiment on 1.2 million Swedes. Proc. Natl. Acad. Sci. 109, 8461–8466 (2012).

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Figures and tables – Chapter 4

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Figure 4.1. Manhattan plot of the adjusted EWAS model. The figure displays the Manhattan plot of the meta-analysis of the adjusted EWAS model (the Manhattan plot of the basic model is reported in Supplementary Note). The x-axis is chromosomal position, and the y-axis is the significance on a log10 scale. The dashed lines mark the threshold for epigenome-wide significance (P = 110–7) and for suggestive significance (P = 110–5). Each epigenome-wide associated probe is marked with a red , and the symbol of the closest gene based on physical position.

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Figure 4.2. EWAS effect sizes (in terms of variance explained) across traits and with GWAS. The figure displays the effect size estimates in terms of R2, in descending order, for the 50 top probes of the adjusted EWAS model. For comparison we present the 50 top probes from recent EWAS on alcohol consumption (n = 9,643, Liu et al., 2016), BMI (n = 7,798, Mendelson et al., 2017), smoking (n = 9,389, Joehanes et al., 2016), and maternal smoking (n = 6,685, Joubert et al., 2016). For comparison with GWAS effect sizes we contrast the EWAS probes with the effect sizes of the 50 top approximately independent SNPs from a recent GWAS on educational attainment (n = 405,073, Okbay et al., 2016). Panel (a) and (b) display the same results but with a different scaling of the y-axis in order for the smaller effect sizes to be visible.

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Figure 4.3. Comparison of EA EWAS effect sizes with the effect sizes in the never-smoker subsample and in smoking EWAS results. Panel (a) displays the effect-size estimates in terms of R2 for the 9 lead probes, in descending order, and the lead probe’s corresponding effect size when re-estimated in the subsample of never smokers. Panel (b) displays the same information for the probes of the adjusted model with P < 110–5 (including the 9 lead probes), as well as the same probes’ effect-size estimates from two recent EWAS of smoking (n = 9,389, Joehanes et al., 2016), and maternal smoking (n = 6,685, Joubert et al., 2016). The smoking and maternal smoking estimates are only publicly available for probes associated at FDR < 0.05 in the respective EWAS.

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Figure 4.4. Effect size estimates (in days) of the epigenetic clock analyses with 95% confidence intervals. Panel (a) displays the effect size estimates from the basic age acceleration model, and panel (b) displays the effect size estimates from the adjusted age acceleration model. The effect size is denoted in days of age acceleration per year of EA, and error bars represent 95% confidence intervals.

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Figure 4.5. Methylation score prediction of educational attainment in independent holdout samples. Panel (a) displays the prediction in all individuals, and panel (b) displays the prediction in the subsample of never smokers. Four methylation scores were constructed: using coefficient estimates from the basic model versus adjusted model, crossed with a P-value threshold of 10–5 and 10–7. The sample sizes of the LBC1936, the RS3, and the RS-BIOS cohorts are 918, 728, and 671 individuals, respectively. We performed sample-size-weighted meta-analysis across the cohorts for each of the four methylation-score prediction analyses. From left to right, the respective P-values testing the null hypothesis of zero predictive power –11 –11 –11 –8 are 4.4210 , 7.7610 , 2.0210 , and 3.2810 for the full sample and 0.0183, 0.0898, 0.0051, and 0.1818 for the never-smokers, respectively. The full prediction results are presented in Supplementary Table S1.9a and S1.9b.

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Figure 4.6. Correlations between tissue-specific methylation and the EWAS association results (adjusted model). Panel (a) displays the correlation estimates based on the whole- genome bisulfite sequencing (WGBS) methylation measurement, and (b) displays results based on the mCRF methylation measurement. (The mCRF measurement combines sequencing data from the MeDIP-seq and MRE-seq methods.) The method is described in Supplementary Note 7. Correlations that are significant after Bonferroni correction are marked with two asterisks (**), and marginal significance (P < 0.05) is marked with one asterisk (*). The tissue-specific methylation data is from the Roadmap Epigenomics Consortium, and we used their categorization and color code for simplicity of comparison51.

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2 R 0.04% 0.04% 0.07% 0.17% 0.00% 0.09% 0.28% 0.28% 0.02% 0.07% (never- smokers)

–1 –2 –3 –1 –2 –4 –4 –1 –2 10 10 10 10 10 10 10 10 10          . smokers) smokers) value (never- value P- 4 (never- n smokers) smokers) ) –7 10  Supplementary Table S1.7a value < 1 = 5,175) 5,175) = n P- ( Expected power power Expected in never-smokers in never-smokers 760 15.2% 5,172 6.55 5,172 15.2% 760 6,067 10.6% 5,174 1.48 4.35 5,174 10.6% 6,067 4,334 9.2% 6,500 -7,753 8.2% 5,174 2.98 5,174 8.2% -7,753 13,383 12.3% 5,175 3.37 5,175 12.3% 13,383 13,110 33.3% 4,627 6.36 4,627 33.3% 13,110 3.10 5,174 22.2% 12,851 closest closest -21,674 6.4% 5,170 6.59 6.4% -21,674 5,170 -47,656 75.1% 5,174 1.46 75.1% -47,656 5,174 Distance gene TSS TSS gene F2RL3 F2RL3 ALPPL2 MYO1G MYO1G IER3 AHRR AHRR AHRR ALPPL2 ALPPL2 Closest Closest gene

2 R 0.70% 0.70% 0.46% 0.40% 0.36% 0.36% 0.34% 0.32% 0.31% 0.30% 0.28% –9 –9 –9 –8 –8 –8 –17 –11 –10 10 10 10 10 10 10 10 10 10          value value EWAS association results - adjusted model (9 probes with with probes (9 model adjusted - results association EWAS n P- : "Distance closest gene TSS" measured in base pairs. An extended version of this table is available in in available is table this of version extended An pairs. base in measured TSS" gene closest : "Distance Table 4.1 | EWAS association results - adjusted model model adjusted - results association EWAS | 4.1 Table ChrPosID Probe 2.03 10,315 5:373379 cg05575921 9,633 2:233284662 cg21566642 2.26 cg05951221 2:233284403 10,313 10,313 1.12 2:233284403 cg05951221 10,313 1.24 19:17000586 cg03636183 10,316 3.84 2:233284935 cg01940273 8.09 10,316 7:45002920 cg12803068 9,531 7:45002487 5.52 cg22132788 9,718 6:30720081 6.63 cg06126421 7.39 10,309 5:399361 cg21161138 Note

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Chapter 4 Supplementary methods

Study overview

This meta-analysis study of educational attainment (EA) and cytosine-phosphate-guanine (CpG) methylation in human DNA was performed according to a pre-specified analysis plan. The analysis plan was publicly archived on Open Science Framework (OSF) in September 2015 (available at https://osf.io/9v3nk/), and it specifies two main analyses to be conducted at the cohort level – an epigenome-wide association study (EWAS)1, and an epigenetic clock analysis2. The EWAS is considered hypothesis-free as it is performed genome-wide without an expected direction of effect for individual CpG loci (often referred to as CpG probes), while the epigenetic clock analysis is hypothesis-driven since we a priori expect lower EA to be associated with a faster running epigenetic clock (a higher DNA methylation age). When we designed the study, we aimed to achieve a sample size of at least 10,000 individuals, which would lead to 80% power to detect an effect as small as R2 = 0.38%. This effect size is much smaller than those found in EWAS of other traits3–5, but smaller effect sizes could possibly be expected as EA is a biologically distal environmental factor. To achieve the desired sample size, we were required to pool data across cohort studies and perform a meta-analysis. Due to privacy and data sharing restrictions the EWAS and epigenetic clock analysis could only be performed locally, and subsequently meta-analyzed. Cohorts could join this study by providing a signed collaboration agreement and sample descriptives during the fall of 2015. The Principal Investigator (PI) of each cohort affirmed that the results contributed to the study were based on analyses approved by the local Research Ethics Committee and/or Institutional Review Board responsible for overseeing research. All participants provided written informed consent. An overview of the 27 independent cohort studies from the 15 participating cohorts is reported in Supplementary Table S1.1. In summary, the EWAS was performed with a basic model with covariates for age, sex and white blood-cell counts, and an adjusted model with additional covariates to correct for the two lifestyle factors body mass index (BMI) and smoking, which were available in all cohorts. For a full description of the control variables, see below. Since lifestyle factors such as BMI and smoking are known to be strongly associated with both methylation and EA6–8, we focus the presentation and follow-up analyses on the results of the adjusted model as we consider this to be more conservative.

Sample inclusion criteria

The analysis plan specified the individual inclusion criteria, limiting the analysis to individuals of European ancestry, who were at least 25 years of age at the time of assessment of EA. Individuals were to be excluded if they were cases from cohorts with a case-control study design; if they did not have successfully measured methylation or did not pass other cohort- specific standard quality control (QC) filters. Cohort-level individual and CpG probe filtering is explained below. A second quality control was implemented after meta-analysis, and this procedure is described below. The total meta-analysis sample sizes of the basic and adjusted EWAS models are respectively 10,767, and 10,317. Some individuals were excluded from the adjusted model due to missing covariates, which caused the difference in sample size.

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Cohorts performed the analyses with code pre-specified in the analysis plan9, and this code can be accessed at Open Science Frameworkq. For the EWAS the cohorts provided the following summary statistics for each probe to the meta-level analysis - CpG probe ID (“markername”), estimated regression coefficient (beta), standard error of the coefficient estimate, P value of the coefficient estimate, and sample size. For the epigenetic clock analyses the cohorts provided the parameter estimates for the four different age acceleration measures, standard error of the parameter estimates, P value of the parameter estimate, and sample size.

Phenotype definition and sample descriptive statistics

Educational attainment (EA) was harmonized across cohorts in accordance with earlier work of the Social Science Genetics Association Consortium (SSGAC)10,11, i.e., in accordance with the ISCED 1997 classification (UNESCO)12. The classification consists of seven internationally comparable categories, which were subsequently translated into US years-of- 4 schooling equivalents, which have a quantitative interpretation (Supplementary Table S1.2). The translated measure maintains a high level of variance in the phenotype and the cohort- specific translation is reported in Supplementary Table S1.3, together with the within-cohort means and standard deviations. A summary of the 27 independent cohort studies from the 15 contributing cohorts is reported in Supplementary Table S1.1. The within-cohort mean age at reporting ranges from 26.6 to 79.1 years, and the minimum and maximum age is respectively 18r and 94 years. The sample size ranges from 48 to 1,658, with an average of 399 individuals. The average EA within the cohorts ranges from 8.6 to 18.3 years of education, and the sample-size weighted average is 13.6 (SD = 3.62). Females comprise 54.1% of the meta-analysis sample.

DNA methylation measurement and cohort-level quality control (QC)

Genome-wide CpG methylation was measured in whole blood with the Illumina 450k Human Methylation chip. Since background correction and normalization of methylation data is time- consuming and dependent on sample-specific properties, and as no method is considered overall superior, we encouraged the cohorts to perform background correction13 and normalization14 according to their standard QC protocols to prepare the chip data for analyses. We report the cohort-specific technical details in Supplementary Table S1.4. Cohorts were recommended to implement exclusions of CpG probes and samples of individual participants using the following thresholds: probes should be excluded if the probe-detection P value was greater than 0.01 in more than 5% of the individuals, and individuals should be excluded if the detection P value was greater than 0.01 for more than 5% of the probes within an individual. White blood cell counts, which were included as covariates to avoid confounding due to differential leukocyte cell composition across individuals, could either be measured directly or imputed based on the Houseman algorithm15. To summarize, we standardized the analysis protocol as much as possible, while ensuring some degree of flexibility to keep the implementation feasible for all samples. q To access the code for follow-up analyses please contact the corresponding author. r The FTC and LLD cohorts were allowed to additionally include individuals slightly below 25 years of age because the FTC cohort includes on-going education in the EA measurement, and the cohorts has validated the phenotype with later cohort waves (Cohen’s  = 0.82). The LLD cohort measures EA with high enough precision to capture most of the variation even for individuals that had not yet begun higher education.

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Epigenome-wide association study (EWAS)

The dependent variable in the epigenome-wide association study (EWAS) was the methylation beta-value, i.e., the proportion of methylation at a CpG locus across the measured cells within an individual. The beta-value hence lies in the interval [0, 1], and has a biologically meaningful interpretation. An alternative approach is to regress the methylation as the so-called M-value, 16 which is the log2 transformed beta-value . For comparability, we used the beta-values following the methodology of the largest EWAS studies to our knowledge3,5,6,17. The relation between CpG methylation and the technical covariates motivates having the methylation beta- value as the dependent variable since the technical covariates reduce the error variance leading to greater statistical power to find associations with the phenotype of interest. Each cohort study estimated the following regression for each CpG probe passing cohort-level quality control: #)$ (H \ .8 Z .9H Z 2146 Z 345 Z 147 Z 0H where (H is the methylation beta-value for individual i, H is the harmonized continuous measure of EA, 214 is a vector of the first four principal components of the genetic relatedness matrix, and 34 is a vector of control variables further explained below. 14 is a vector containing study-specific controls and technical covariates (such as dummy variables for plates, hybridization date, and batches) that were encouraged in the analysis plan, which is discussed below. In accordance with the pre-specified analysis plan, probes with P value less than 110–7 (the commonly-used threshold in epigenome-wide association studies1) were considered as epigenome-wide significant associations. We report two-tailed P values throughout the paper unless otherwise specified.

Control variables in the adjusted model

Two epigenome-wide association analyses were performed in each cohort, a basic model and an adjusted model. The two models differ in the included covariates (34) in accordance with the pre-specified analysis plan, where the basic model controls for age, sex, and white blood- cell counts. The adjusted model additionally includes BMI (kg/m2), and smoking measured as a categorical variable (measured either as Ever/Never smoker or Current/Former/Never smoker, depending on data availability), together with a squared age term to account for non- linear age effects, and an interaction term between age and sex. Since lifestyle factors such as BMI and smoking are known to be strongly associated with both methylation and EA6–8, we focus the article on the results of the adjusted model as we consider this to be more conservative. The quadratic age term and the interaction between age and sex were added to the adjusted model, because of the known non-linear relationship between age and CpG methylation (see Bollati et al., 2009; Langevin et al., 2011; Horvath et al., 2012; Bell et al., 2013; Florath et al., 2014)18–22, as well as the relationship between sex and CpG methylation (e.g. Boks et al., 2009; Liu et al., 2010; Zhang et al. 2011)23–25. Moreover, levels of educational attainment (our main explanatory variable) vary by age (birthyear) and sex (see e.g. Barro and Lee, 2001)26. Hence, the inclusion of these control variables is both biologically and statistically warranted.

Cohort-specific control variables

After discussion with the meta-analysts, a few additional cohort-specific control variables were encouraged if they were deemed necessary to control for confounding relationships between

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EA and CpG methylation, and these cohort-specific control variables were included in 14. The EPIC and MCCS cohorts did not have genetic data available to allow inclusion of genetic PCs, so the first principal component of the methylation matrix was included instead as a stringent alternative to control for possible inflation of the test statistic due to subtle population stratification. The HBCS cohort included a dummy for childhood separation exposure, KORA F4 a dummy for World War 2, and MCSS included country of birth control variables, all of which could plausibly be correlated with both EA and methylation. Moreover, in ALSPAC, EPIC-Breast Cancer, MCCS-Breast Cancer, and MCCS-Prostate Cancer, sex was not included because these samples consist of solely females or males. In EGCUT1 smoking was not included because all participants were non-smokers. The EPIC and MCSS samples did not control for genetic PCs because of unavailability of these variables but controlled instead for the first PC of the methylation data as a stringent alternative. The age2 term was not included in the LBC models due to the very narrow age-range in these birth cohort samples. 4 Description of major steps in meta-level quality-control (QC) analyses

A stringent quality-control (QC) protocol was performed to ensure that only high-quality CpG probes were meta-analyzed. All cohorts were asked to supply descriptive statistics and phenotype definitions according to the pre-specified analysis plan, and the completeness of these documents was assessed as the first step of quality control, together with examination of the uploaded EWAS summary statistics. Thereafter we applied the following probe filters. Filters were applied to remove (a) probes with missing P value, standard error or coefficient estimate; (b) probes not available in the probe annotation reference by Price et al. (2013)27; (c) CpH probes (H = A/C/T); (d) probes on the sex chromosomes; (e) cross-reactive probes highlighted in a recent paper by Chen et al.28; and (f) probes with a cohort-level call rate less than 95%. Probes were annotated if they were so-called SNP-probes, i.e., if the probe is located on a single-nucleotide polymorphism (SNP). This was done so that any positive results could be interpreted with caution if the associated probe would be located on a SNP, as this is known to affect the methylation status of the CpG probe27. We however chose to keep all SNP-probes in the results, rather than removing all of them. The output from the quality control was examined to see if any filters removed an unusual or unexpected number of probes, and two analysts independently performed and crosschecked the QC. After probe filtering, the distributions of the coefficient estimates (betas) were compared across the cohorts to identify possible outliers and birth-year effects.

Meta-analysis

Due to the differences in the mean and standard deviation of CpG methylation across cohorts we decided to perform sample-size weighted fixed-effect meta-analysis of the cohort-level EWAS summary statistics using the METAL29 software, as fixed effect meta-analysis is robust to differences in units of measurement. Due to the variability of />= across cohorts we applied cohort-level genomic control to deflate the association test statistic prior to meta-analysis, equivalent to its GWAS analogue30, to stringently control for possible population stratification that could remain even after controlling for genetic principal components in regression. In the final meta-analysis results, only probes with a meta-level sample size greater than 1,000 were considered.

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Investigation of possible confounding with tobacco smoking

The adjusted EWAS model included a categorical control variable for tobacco smoking (categorized as current, former or never smoker). Due to the discrete nature of the variable and the measurement error that follows by not measuring tobacco smoking as a continuous variable, we believe that the control variable might not have completely controlled for the exact smoking exposure. Unfortunately, controlling for smoking with a categorical variable is the common approach due to the lack of a more precise smoking measure in most cohort studies. As a result, smoking could potentially bias the regression coefficient of EA because of the known negative correlation between smoking and EA36, and the strong association between smoking and methylation found in a large number of studies4,6. Therefore, we performed a literature review using PubMed to see if any of the lead probes from the adjusted model have been associated with smoking in previous studies. The literature review was performed February 24, 2016 by searching for the term “smoking” together with each of the adjusted model’s nine lead probes separately. The search resulted in 30 eligible studies or smaller meta-analyses (n < 1,800) on CpG methylation and different forms of smoking exposure, one systematic review by Gao et al. (2015)37, and two larger meta- analyses by Joehanes et al. (2016, n = 9,389)6 and Joubert et al. (2016, n = 6,685)4 that include many of the individual studies. The EWAS meta-analysis by Joehanes et al., 20166, comprised of several of the 30 individual studies, was published after the literature review had been performed and we added this study to the literature review post-hoc due to its relevance and large sample size. The result of the literature review is presented in Supplementary Table S.1.10. The studies could largely be categorized as an investigation of either the relationship between a person’s own smoking and methylation, or the association between newborn’s methylation and maternal smoking, both at birth and at later stages in the life of the offspring. Most of the individual studies have relatively small sample sizes (smallest sample size consists of only 21 monozygotic twin pairs, and the average sample size is 471), and we note that many of the individual studies on personal smoking are meta-analyzed in Joehanes et al. (2016), and most of the individual studies on maternal smoking are meta-analyzed in Joubert et al. (2016). We found that all of the adjusted model’s nine lead probes have been associated with smoking in at least one previous study, and in Supplementary Note 4.3 we contrast the EWAS effect sizes estimates for EA with the effect size estimates from the two meta-analyses studies. However, not all of these previous studies controlled for EA, and the associations between smoking and methylation could therefore be biased due to the omission of EA as a control variable, which makes it complicated to draw definite conclusions of the magnitude of confounding. Also, the sample sizes of many of the individual studies are small and likely to contain a higher number of false positives than the meta-analyses. In light of this finding we interpret our EWAS findings carefully, and the literature review motivated the post-hoc sensitivity analyses in the subsample of people that never smoked presented in the next section.

Robustness of EWAS results in the never-smoker subsample

To further investigate the possible confounding effect of smoking on the association between EA and CpG methylation, all cohorts reran the EWAS in the subsample of individuals that reported as ‘never smoker’. We refer to this subsample as the never smokers. The full EWAS sample consists of the ever smokers (i.e., the individuals who answered that they were ever, current, or former smokers) and the never smokers. This approach would give us confidence in

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Joint test of no association in the never-smoker subsample

Next, we performed a joint test of the null hypothesis that the regression coefficients of the lead probes are all equal to zero, with the sample-size weighted Z statistic estimated in the never smokers. Since we have no a priori hypothesis of a direction of effect for different CpG probes, and to avoid that positive and negative Z-statistics cancel out, we performed this test on the absolute value of the Z-statistic with the following weighting J H<9 ,HH =NLCHMFE \ J : #'$ `H<9 ,H

where

,H \ _H The right-tailed P value of the joint test of no association was 2.1810–11. As a robustness check, we also performed this test while pruning the lead probes so that only the strongest association within 250kb was kept. In that case, we observed a right-tailed P value of 4.9110– 6, based on five probes. We interpret this as evidence that at least one probe has an estimated effect different from zero in the meta-analysis of the never smokers (however see Supplementary Note 4.4 for more discussion of maternal smoking as a confounding factor).

Effect size comparison ever versus never smokers

Next, we investigated whether the effect-size estimates changed between estimation performed in the ever and never smokers. To be able to compare the differences in effect sizes and the precision of the estimates (SE), we used inverse-variance weighted meta-analysis to retain meta-analytical betas and standard errors for the nine lead probes. We thereafter derived the effect sizes and standard errors of the ever smokers by assuming that the effect size estimates in the full sample is a weighted average of the effect size estimates in the ever smokers and

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Chapter 4 never smokers, as the two groups are mutually exclusive and the union between the two is the full EWAS sample. The result of the effect size comparison is presented in Supplementary Figure 4.3. We aligned the effect sizes to the first quadrant by taking their absolute values so that all effect sizes were positive for the comparison. If the effect sizes would be similar across the subsamples we would expect them to align along the 45-degree line, and if confounding would be driving the effect- size estimates then we would expect a cluster above the 45-degree line as the effect-size estimates in the ever smokers would be greater than those in the never smokers. By visual inspection we observed a cluster of probes with larger effect size estimates in the ever smokers than in the never smokers (i.e., those above the 45-degree line). We also observed a second cluster of effect size estimates with similar estimates in both subsamples (i.e., those along the 45-degree line). The two lead probes that we found suggestively associated in the never smokers i.e., cg12803068 and cg22132788, have slightly larger effect size estimates in the ever smokers than in the never smokers, however these larger estimates lie within the confidence intervals of the estimates in the ever smokers. The effects of the other seven probes were estimated to be more than 50% smaller in the never smokers than in the ever smokers, and all of these effect size estimates are outside the confidence intervals of the estimates among ever smokers.

Lookup of probes in EWAS meta-analysis on smoking and maternal smoking during pregnancy

In Figure 4.3 and Supplementary Table S1.11 we present the effect-size estimates, in terms of R2, of the 44 probes of the adjusted model with P value less than 110–5 (including the nine lead probes). The probes are listed in descending order based on their effects, for comparison, we display their effects when re-estimated in the never-smoker subsample, as well as their effects as reported in the recent EWAS meta-analyses on smoking by Joehanes et al. (2016)6 and maternal smoking by Joubert et al. (2016)4. We report the effect sizes for smoking and maternal smoking if a probe was significantly associated in these studies at FDR < 0.05, as those results were publicly available. Notably, the effect sizes of smoking and maternal smoking are many times larger for most probes compared to the effect of EA. Further, the suggestively associated probes cg12803068 and cg22132788, and the closest gene; MYO1G, have all been reported as affected by maternal smoking in newborn’s, and persistently later in life4,38. The effect of maternal smoking on these two probes is extreme; their R2 are 8.34% and 6.33%, respectively. In our study, we do not have access to individual-level information on maternal smoking. Thus, we cannot distinguish between the hypothesis that these probes are truly associated with EA and the hypothesis that the observed association with EA is entirely driven by a greater exposure to maternal smoking during pregnancy among lower-EA individuals. The extreme effects of smoking and maternal smoking substantially weaken the support of the premise that these probes would be directly affected by EA, rather than indirectly via smoking. Also, we cannot rule out other potential confounders, such as exposure to second-hand smoke. The lookup strongly suggests that smoking and maternal smoking are worrying confounding factors for the probe associations with EA, even in the subsample of individuals who self-report as never smokers.

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Enrichment analyses

Enrichment analysis of GWAS of smoking and EA

We performed an enrichment analysis to investigate whether the genetic loci, in proximity with the probes found to be associated in the adjusted EWAS model (at P < 110–4), also contain SNPs enriched for EA and smoking. For EA, we used the GWAS summary-statistics from a large (n = 405,073), recent meta-analysis by Okbay et al. (2016)34. For smoking, we used a meta-analysis combining GWAS results from the UK Biobank together with the publicly available summary statistics from the Tobacco, Alcohol, and Genetics Consortium (TAG, total n = 186,102)39. The latter meta-analysis is described in detail in Karlsson Linnér et al. (2017)40. The enrichment analysis was performed with the 179 EWAS probes from the adjusted model with P value less than 110–4. We pruned the probes so that the probe with the lowest P value in a locus was selected as the lead probe, and all probes within 250kb from the lead probe was 4 clumped with the strongest association. This resulted in 141 “approximately independent” lead probes (k). For each of the phenotypes, we extracted the closest SNP available in the GWAS summary statistics based on the physical position of the k lead probes. Using the reference panel 1000 Genomes phase 3 (October 2014 haplotype release version 5), we extracted the SNPs in strong LD (r2 >= 0.8) with the k SNPs. Next, we averaged the absolute value of their GWWAS Z statistics, as well as the minor allele frequency (MAF) of the SNPs in strong LD. This leads to a distribution of k average Z statistics and average MAF. From the summary statistics we extracted a set of 1,000 random SNPs for each of the k SNPs, matched on the average MAF (+/- 1pp), and for the matched SNPs we also averaged the absolute value of the Z statistics across the SNPs in strong LD, just as for the 141 “first-stage” SNPs. This leads to a distribution of k1,000 average absolute Z statistics. Next, we ordered the Z statistics and performed a test of joint enrichment with the non-parametric Mann-Whitney test of the null hypothesis that the association test statistic of the k SNPs are drawn from the same distribution as those of the 141,000 MAF-matched SNPs.

Prediction of EA with polygenic methylation scores

To test the out-of-sample predictive power of our EWAS findings, we constructed polygenic methylation scores (PGMS) to perform prediction of EA. The prediction was performed in three independent cohort studies, the Lothian Birth Cohort 1936 (LBC1936, n = 917), Rotterdam Study BIOS (RS-BIOS, n = 671), and Rotterdam Study 3 (RS3, n = 729). For each of the prediction cohorts we created a new EWAS meta-analysis withholding the respective prediction cohort study to avoid overfitting42. The effect sizes (in terms of Z statistics) from the respective holdout meta-analysis were used as weights when constructing the PGMS. The Z statistic were used instead of the EWAS coefficients because CpG methylation is the dependent variable in the EWAS regression. For each prediction sample we created four PGMS using two P-value thresholds P < 10–5 and P < 10–7, and the estimates from the basic and adjusted models. The PGMS was defined as the weighted sum of the Q CpG probes: P b \ a c   #($ H I HI I<9 where bH denotes the methylation score of individual i, cI is the estimated Z-statistic for CpG probe j, and HI is the methylation beta-value for individual i at CpG probe j.

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A recent study showed that the phenotypic prediction can be improved if the PGMS is combined with a single-nucleotide polymorphism polygenic score (SNP PGS)43. We therefore included a SNP PGS constructed with the effect size estimates from a recent large-scale GWAS on EA (n = 405,073)11, and the prediction cohorts were excluded from the GWAS meta-analysis to avoid overfitting. The SNP PGS was defined as the weighted sum of the T directly genotyped SNPs: S c \ a .c !  #)$ H I HI I<9 where cH denotes the polygenic score of individual i, .cI is the estimated additive effect size of the effect-coded allele at SNP #, and !HIis the genotype of individual "at SNP j (coded as having 0, 1 or 2 instances of the effect-coded allele)44. For each prediction cohort study we performed an OLS regression with EA as the dependent variable, and age and sex as control variables. The predictive power of the PGMS was evaluated as the incremental R2 of adding the PGMS to the regression model, i.e., the difference in R2 between the regression model with only the SNP PGS and the covariates, and the regression model with the PGMS together with the SNP PGS and the covariates. The incremental R2 of the interaction term (between the PGMS and SNP PGS) was evaluated as the difference in R2 between the regression model with the PGMS and the SNP PGS as additive main effects, together with the covariates, and the same regression model with the interaction term added. To estimate 95% confidence intervals for the incremental R2 we performed bootstraps with 1,000 samples with the percentile interval method, and this was done with the ‘boot’ package in R9,45,46. Finally, the incremental R2 was meta-analysed across the three prediction cohorts (LBC, RS-BIOS, and RS3) as the sample-size weighted incremental R2.

ALSPAC prediction of educational achievement

A further prediction analysis, using the same PGMS prediction method as described in Supplementary Note 6, was carried out in the ARIES substudy (n = 678) of the ALSPAC cohort47,38. Cord-blood methylation was assessed in newborn children and EA was measured using four sets of standardised Key Stage48 school grades from primary level through to high school (educational achievement was evaluated at ages 7-16 years), and the Key Stage educational achievement test scores are part of the state education system in the UK48. The cord blood signatures were collected prior to any educational exposure and may help identify if methylation differences might be a cause or consequence of EA. For each of the Key Stages 1 to 4, an average score across all school subjects was derived for each child. The predictive accuracy of the PGMS, built using the association Z-statistic from a meta-analysis excluding the ALSPAC cohort, was assessed as the incremental adjusted R2 of adding the score to the OLS regression model, with sex and age at assessment as control variables. Secondly, we tested if these associations were robust to the inclusion of maternal smoking as an additional control variable. Finally, we performed the prediction in a restricted sample using data from children of non-smokers only.

Correlation of EWAS associations with tissue-specific methylation

The NIH Roadmap Epigenomics Consortium recently made an impressive analysis and categorization of a multitude of different epigenetic marks, across 111 different tissues51. The data is publicly archived, and we harnessed their tissue-specific methylation data to answer the question of whether our EWAS associations are correlated with any tissue-specific DNA methylation. We hypothesize that EWAS associations for a given phenotype would be more

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Supplementary methods likely to be located at loci that are differentially methylated in tissues relevant for the phenotype or endophenotypes. E.g., if our EWAS associations would be correlated with the tissue-specific methylation of brain tissues, that would increase the credibility of the associations, and improve the biological interpretation. Genome-wide methylation data was available for three kinds of CpG methylation measurements: whole-genome bisulfite sequencing (WGBS), reduced representation bisulfite sequencing (RRBS), and mCRF (a method combining sequencing data from the MeDIP-seq and MRE-seq methods). Methylation was measured as the beta value, ranging from 0 to 1, and it was available for 37 tissues measured with WGBS, 49 tissues with RRBS, and for 16 tissues with mCRF. Only eight tissues were available for two or more methylation measurements. We pruned the probes of the adjusted EWAS model with P-value less than 110–4 using a window of 250kb, as described in 5.1 Enrichment analysis of GWAS of smoking and EA, which resulted in 141 “approximately independent” probes. Based on the physical location of 4 the 141 pruned probes we extracted the beta-value for WGBS, RRBS, and mCRF. The beta- values were first converted to M-valuess, and thereafter we calculated standardised tissue- specific deviations (=O>S) from the cross-tissue average methylation (+%YYYYYYY=O>e for each of the 141 CpG loci as:

(7) +%=O>S [ +%YYYYYYY=O> =O>S \ * d+%=O>e where =O>S is the tissue-specific deviation for tissue t, at locus CpG. This procedure was performed within each methylation measurement. Hence, in the case that a tissue had no missing CpG loci, there were 141 tissue-specific deviations; each corresponding to one of the loci identified in the association results of the adjusted EWAS model. For RRBS, there were many missing values across all 49 tissues, and the maximum number of loci available was 38 out of 141. We therefore excluded the RRBS measurement from further analysis, while both WGBS and mCRF had close to 141 overlapping loci for all tissues (Supplementary Table 1.14). For WGBS and mCRF we calculated correlations with the tissue-specific Z-statistics and the EWAS association Z-statistics of the pruned probes (from the adjusted EWAS model). Bonferroni correction for the number of tissues within each methylation measurement was performed. methQTL analysis

It has been shown that genetic variants also explain variation in CpG levels1 in addition to environmental influences on DNA methylation. EWAS probes under genetic influence may help us understand the direction of association between CpGs and outcomes, but it can also be a confounding factor. SNPs affecting the level of methylation, usually referred to as methylation quantitative trait loci (methQTL)1, sometimes have effect sizes that can be found in samples of less than a thousand individuals54,55, which is much less than what is necessary for most GWAS of e.g. behavioral phenotypes34. Therefore, as is customary in the EWAS literature, we performed a GWAS for each of the 9 lead probes to investigate if any SNPs were associated with the level of methylation. The genome-wide association analysis was performed in the

s As the consortium methylation data was only available with two decimals precision, we imputed 0’s and 1’s with 0.005 and 0.995 to avoid infinite M-values.

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LBC1936 (n = 918) and RS3 (n = 731) cohorts that estimated the following GWAS regression equation for each autosomal bi-allelic SNP and lead probe: #+$ H \ .8 Z .9H Z 2146 Z 345 Z 147 Z 0H where H is the methylation beta-value for individual ", H is the number of reference alleles of the SNP, 214 is a vector of the first four principal components of the genetic relatedness matrix, and 34 is a vector of the control variables age and sex, as well as an interaction term between age and sex. 14 is a vector containing study-specific controls and technical covariates, such as dummy variables to control for genotyping array and batch.

Quality control of methQTL analyses

The GWAS results of the methQTL analyses were quality controlled according to a stringent protocol by the GIANT consortium56, and the protocol was implemented with the EasyQC software. In summary the following major filters were applied; removal of monomorphic and multi-allelic SNPs, and structural variants such as INDELs; removal of SNPs with an IMPUTE imputation quality < 0.7; and of SNPs with a minor allele frequency (MAF) < 0.05. The quality control procedure ensures that all SNPs have alleles aligned to the 1000G phase 3, version 5 (October 2014 haplotype release)57, and SNPs that could not be aligned with the reference were removed.

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References – Chapter 4 Supplementary methods

1. Rakyan VK, Down TA, Balding DJ, Beck S. Epigenome-wide association studies for common human diseases. Nat. Rev. Genet. 2012; 12, 529–541. 2. Horvath S. DNA methylation age of human tissues and cell types. Genome Biol. 2013; 14, 1-19 (2013). 3. Mendelson MM, Marioni RE, Joehanes R, Liu C, Hedman ÅK, Aslibekyan et al. Association of Body Mass Index with DNA Methylation and Gene Expression in Blood Cells and Relations to Cardiometabolic Disease: A Mendelian Randomization Approach. PLoS Med. 2017; 14: 1–30. 4. Joubert BR, Felix JF, Yousefi P, Bakulski KM, Just AC, Breton C et al. DNA Methylation in Newborns and Maternal Smoking in Pregnancy: Genome-wide Consortium Meta-analysis. Am. J. Hum. Genet. 2016; 98: 680–696. 4 5. Ligthart S, Marzi C, Aslibekyan S, Mendelson MM, Conneely KN, Tanaka T et al. DNA methylation signatures of chronic low-grade inflammation are associated with complex diseases. Genome Biol. 2016; 17: 1–15. 6. Joehanes R, Just AC, Marioni RE, Pilling LC, Reynolds LM, Mandaviya PR et al. Epigenetic Signatures of Cigarette Smoking. Circ. Cardiovasc. Genet. 2016; 9: 436- 447. 7. Demerath EW, Guan W, Grove ML, Aslibekyan S, Mendelson MM, Zhou Y-H et al. Epigenome-wide association study (EWAS) of BMI, BMI change and waist circumference in African American adults identifies multiple replicated loci. Hum. Mol. Genet. 2015; 24: 4464–4479. 8. Johnson W, Kyvik KO, Mortensen EL, Batty GD, Deary IJ. Does education confer a culture of healthy behavior? Smoking and drinking patterns in Danish twins. Am. J. Epidemiol. 2011; 173: 55–63. 9. Canty A, Ripley B. boot: Bootstrap R (S-Plus) Functions. [Internet]. 2015 [cited 4 April 2016]. Available from: 10. Rietveld CA, Medland SE, Derringer J, Yang J, Esko T, Martin NW et al. GWAS of 126,559 individuals identifies genetic variants associated with educational attainment. Science 2013; 340: 1467–1471. 11. Okbay A, Beauchamp JP, Fontana MA, Lee JJ, Pers TH, Rietveld CA et al. Genome- wide association study identifies 74 loci associated with educational attainment. Nature 2016; 533: 539–542. 12. United Nations Educational, Scientific and Cultural Organization. International Standard Classification of Education [Internet]. 2006 [cited 18 September 2015]. Available from: 13. Triche TJ, Weisenberger DJ, Berg D, Laird PW, Siegmund KD. Low-level processing of Illumina Infinium DNA Methylation BeadArrays. Nucleic Acids Res. 2013; 41: 1– 11. 14. Fortin J-P, Labbe A, Lemire M, Zanke BW, Hudson TJ, Fertig EJ et al. Functional normalization of 450k methylation array data improves replication in large cancer studies. Genome Biol. 2014; 15: 503-520. 15. Houseman EA, Accomando WP, Koestler DC, Christensen BC, Marsit CJ, Nelson HH

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et al. DNA methylation arrays as surrogate measures of cell mixture distribution. BMC Bioinformatics 2012; 13: 86-102. 16. Du P, Zhang X, Huang C-C, Jafari N, Kibbe WA, Lifang H et al. Comparison of Beta- value and M-value methods for quantifying methylation levels by microarray analysis. BMC Bioinformatics 2010; 11: 587-596. 17. Liu C, Marioni RE, Hedman ÅK, Pfeiffer L, Tsai P-C, Reynolds LM et al. A DNA methylation biomarker of alcohol consumption. Mol. Psychiatry 2016; e-pub ahead of print 15 November 2016; doi:10.1038/mp.2016.192 18. Bollati V, Schwartz J, Wright R, Litonjua A, Tarantini L, Suh H et al. Decline in genomic DNA methylation through aging in a cohort of elderly subjects. Mech. Ageing Dev. 2009; 130: 234–239. 19. Langevin SM, Houseman EA, Christensen BC, Wiencke JK, Nelson HH, Karagas MR et al. The influence of aging, environmental exposures and local sequence features on the variation of DNA methylation in blood. Epigenetics 2011; 6: 908–919. 20. Horvath S, Zhang Y, Langfelder P, Kahn RS, Boks MPM, Eijk, K et al. Aging effects on DNA methylation modules in human brain and blood tissue. Genome Biol. 2012; 13: R97. 21. Bell JT, Yang TP-C, Pidsley R, Nisbet J, Glass D, Mangino M et al. Epigenome-wide scans identify differentially methylated regions for age and age-related phenotypes in a healthy ageing population. PLoS Genet. 2012; 8: e1002629. 22. Florath I, Butterbach K, Müller H, Bewerunge-Hudler M, Brenner H. Cross-sectional and longitudinal changes in DNA methylation with age: An epigenome-wide analysis revealing over 60 novel age-associated CpG sites. Hum. Mol. Genet. 2012; 23: 1186– 1201. 23. Boks MP, Derks EM, Weisenberger, DJ, Strengman E, Janson E, Sommer IE et al. The relationship of DNA methylation with age, gender and genotype in twins and healthy controls. PLoS One 2009; 4: 21–23. 24. Liu J, Morgan M, Hutchison K, Calhoun VD. A study of the influence of sex on genome wide methylation. PLoS One 2010; 5: e10028. 25. Zhang FF, Cardarelli R, Carroll J, Fulda KG, Kaur M, Gonzalez K et al. Significant differences in global genomic DNA methylation by gender and race/ethnicity in peripheral blood. Epigenetics 2011; 6: 623–629. 26. Barro RJ, Lee J-W. International data on educational attainment: updates and implications. Oxf. Econ. Pap. 2001; 53: 541–563. 27. Price ME, Cotton AM, Lam LL, Farré P, Emberly E, Brown CJ et al. Additional annotation enhances potential for biologically-relevant analysis of the Illumina Infinium HumanMethylation450 BeadChip array. Epigenetics Chromatin 2013; 6: 4- 19. 28. Chen Y-A, Lemire M, Choufani S, Butcher DT, Grafodatskaya D, Zanke BW et al. Discovery of cross-reactive probes and polymorphic CpGs in the Illumina Infinium HumanMethylation450 microarray. Epigenetics 2013; 8: 203–209. 29. Willer CJ, Li Y, Abecasis GR. METAL: Fast and efficient meta-analysis of genomewide association scans. Bioinformatics 2010; 26: 2190–2191. 30. Yang J, Weedon MN, Purcell S, Lettre G, Estrada K, Willer CJ et al. Genomic

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inflation factors under polygenic inheritance. Eur. J. Hum. Genet. 2011; 19: 807–812. 31. Devlin B, Roeder K. Genomic control for association studies. Biometrics 1999; 55: 997–1004. 32. Iterson M, Zwet E, the BIOS Consortium, Heijmans BT. Controlling bias and inflation in epigenome- and transcriptome-wide association studies using the empirical null distribution. Genome Biol. 2017; 18: 1–13. 33. Goeman JJ, Solari A. Multiple hypothesis testing in genomics. Stat. Med. 2014; 33: 1946–1978. 34. Okbay A, Beauchamp JP, Fontana MA, Lee JJ, Pers TH, Rietveld CA et al. Genome- wide association study identifies 74 loci associated with educational attainment. Nature 2016; 533: 539–542. 35. Chen BH, Marioni RE, Colicino E, Peters MJ, Ward-Caviness CK, Tsai P-C et al. 4 DNA methylation-based measures of biological age: Meta-analysis predicting time to death. Aging 2016; 8: 1844–1865. 36. Johnson W, Kyvik KO, Mortensen EL, Batty GD, Deary IJ. Does education confer a culture of healthy behavior? Smoking and drinking patterns in Danish twins. Am. J. Epidemiol. 2011; 173: 55–63. 37. Gao X, Jia M, Zhang Y, Breitling LP, Brenner, H. DNA methylation changes of whole blood cells in response to active smoking exposure in adults: a systematic review of DNA methylation studies. Clin. Epigenetics 2015; 7, 113-123. 38. Richmond RC, Simpkin AJ, Woodward G, Gaunt TR, Lyttleton O, McArdle WL et al. Prenatal exposure to maternal smoking and offspring DNA methylation across the lifecourse: Findings from the Avon Longitudinal Study of Parents and Children (ALSPAC). Hum. Mol. Genet. 2015; 24: 2201–2217. 39. The Tobacco and Genetics Consortium. Genome-wide meta-analyses identify multiple loci associated with smoking behavior. Nat. Genet. 2010; 42: 441–447. 40. Karlsson Linnér R, Beauchamp JP. Large-scale genetic study of risk tolerance and risky behaviors identifies new loci and reveals shared genetic influences. Manuscript in preparation for publication 2017. 41. Rietveld CA, Esko T, Davies G, Pers TH, Turley P, Benyamin B et al. Common genetic variants associated with cognitive performance identified using the proxy- phenotype method. Proc. Natl. Acad. Sci. 2014; 111: 13790–13794. 42. Wray NR, Yang J, Hayes BJ, Price AL, Goddard ME, Visscher PM. Pitfalls of predicting complex traits from SNPs. Nat. Rev. Genet. 2013; 14: 507–515. 43. Shah S, Bonder MJ, Marioni, RE, Zhu Z, McRae AF, Zhernakova A et al. Improving Phenotypic Prediction by Combining Genetic and Epigenetic Associations. Am. J. Hum. Genet. 2015; 97: 75–85. 44. Dudbridge F. Power and predictive accuracy of polygenic risk scores. PLoS Genet. 2013; 9: e1003348. 45. Davison AC, Hinkley DV. Bootstrap Methods and Their Application. Cambridge University Press: Cambridge, UK, 1997. 46. The R Core Team. R: A language and environment for statistical computing [Internet]. 2013 [cited 4 April 2016]. Available from:

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release/fullrefman.pdf>. 47. Relton CL, Gaunt T, McArdle W, Ho K, Duggirala A, Shihab H et al. Data Resource Profile: Accessible Resource for Integrated Epigenomic Studies (ARIES). Int. J. Epidemiol. 2015; 44: 1181–1190. 48. GOV.UK. National Curriculum [Internet]. 2016 [cited 14 January 2017]. Available from: 49. Zeilinger S, Kühnel B, Klopp N, Baurecht H, Kleinschmidt A, Gieger C et al. Tobacco Smoking Leads to Extensive Genome-Wide Changes in DNA Methylation. PLoS One 2013; 8: e63812.50. Fraser, A. et al. Cohort profile: The avon longitudinal study of parents and children: ALSPAC mothers cohort. Int. J. Epidemiol. 42, 97–110 (2012). 51. Roadmap Epigenomics Consortium, Kundaje A, Meuleman W, Erst J, Bilenky M, Yen A et al. Integrative analysis of 111 reference human epigenomes. Nature 2015; 518: 317–330. 52. The GTEx Consortium. The Genotype-Tissue Expression (GTEx) project. Nat. Genet. 2013; 45: 580–585. 53. Watanabe K, Taskesen E, Bochoven A, Posthuma D. FUMA: Functional mapping and annotation of genetic associations. Manuscript submitted for publication 2017. 54. Quon, G., Lippert, C., Heckerman, D. & Listgarten, J. Patterns of methylation heritability in a genome-wide analysis of four brain regions. Nucleic Acids Res. 41, 2095–2104 (2013). 55. Lemire M, Zaidi SHE, Ban M, Ge B, Aïssi D, Germain M et al. Long-range epigenetic regulation is conferred by genetic variation located at thousands of independent loci. Nat. Commun. 2015; 6: 6326. 56. Winkler TW, Day FR, Croteau-Chonka DC, Wood AR, Locke AE, Mägi R. et al. Quality control and conduct of genome-wide association meta-analyses. Nat. Protoc. 2014; 9: 1192–1212. 57. The 1000 Genomes Project Consortium. An integrated map of genetic variation from 1,092 human genomes. Nature 2012; 491: 56–65. 58. Gibbs JR, Brug MP, Hernandez DG, Traynor BJ, Nalls MA, Lai S-L et al. Abundant quantitative trait loci exist for DNA methylation and gene expression in Human Brain. PLoS Genet. 2010; 6: e1000952.

118 Meta-analysis of the serotonin transporter promoter variant (5-HTTLPR) in relation to adverse environment and antisocial behavior

I don’t think aggression works like thirst or . I think aggression is more elicit- ed by particular situations. I think it can be mitigated. Steven Pinker

It’s been proven by quite a few studies that plants are good for our psychological development. If you green an area, the rate of crime goes down. Torture victims begin to recover when they spend time outside in a garden with flowers. So we need them, in some deep psychological sense, which I don’t suppose anybody really understands yet. Jane Goodall

Based on Tielbeek, Karlsson Linnér, Beers, Posthuma, Popma, & Polderman (2016). American Journal of Medical Genetics Part B: Neuropsychiatric Genetics. 5

Chapter 5

Abstract

Several studies have suggested an association between antisocial, aggressive and delinquent behavior and the short variant of the serotonin transporter gene polymorphism (5-HTTLPR). Yet, genome-wide and candidate-gene studies in humans have not convincingly shown an association between these behaviors and 5-HTTLPR. Moreover, individual studies examining the effect of 5-HTTLPR in the presence or absence of adverse environmental factors revealed inconsistent results. We therefore performed a meta-analysis to test for the robustness of the potential interaction effect of the “long-short” variant of the 5-HTTLPR genotype and environmental adversities, on antisocial behavior. Eight studies, comprising of 12 reasonably independent samples, totaling 7,680 subjects with an effective sample size of 6,724, were included in the meta-analysis. Although our extensive meta-analysis resulted in a significant interaction effect between the 5-HTTLPR genotype and environmental adversities on antisocial behavior, the methodological constraints of the included studies hampered a confident interpretation of our results, and firm conclusions regarding the direction of effect. Future studies that aim to examine biosocial mechanisms that influence the etiology of antisocial behavior should make use of larger samples, extend to genome-wide genetic risk scores and properly control for covariate interaction terms, ensuring valid and well-powered research designs.

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Introduction

Antisocial behaviors, such as aggression, have a destructive effect on the lives of victims, can disrupt communities and cause societal instability and fear. Next to these physical and psychological costs, antisocial behavior inevitably brings enormous monetary costs. The average costs per murder in the United States was estimated between $17.25 and $24 million and the costs of criminal careers of the most violent and prolific offenders was estimated to be $150–160 million1,2. Given its tremendous societal impact, the study of antisocial behavior has been an important focus of research throughout history. Scientifically informed early intervention strategies could potentially interfere with risk factors for antisocial development, thereby effectively reducing the burden of antisocial behavior for society. However, despite substantial research efforts, the complex etiology of antisocial behavior remains only partly understood.

Environmental Adversities

Criminological research has revealed extensive and invaluable information regarding the environmental adversities influencing antisocial behavior. An important environmental 5 adversity that often arises from these studies is the negative effect of exposure to stressful or traumatic events early in life. These include physical, emotional or sexual abuse, neglect, and a range of household dysfunctions, such as witnessing violence against mothers or incarceration of a family member. Exposure to environmental adversities can have an immense impact on the cognitive and emotional development of children and is associated with developing serious mental disorders later in life and health problems throughout the lifespan, such as alcohol- seeking behavior in early and mid-adolescence3, early initiation of illicit drugs and sexual risk behaviors4. Environmental adversities also impose an increased risk of developing antisocial outcomes, such as conduct disorder and adult criminality5,6. In addition to childhood environmental adversities, other environmental variables such as a lower socioeconomic status (SES) and delinquent peer affiliation are also associated with antisocial development. A recent meta-analysis, summarizing the data of 133 studies, demonstrated that lower family SES was associated with higher levels of antisocial behavior in children and adolescents*. Another study showed that socialization effects of peer influences on antisocial behavior were particularly important at the age of 16 to 20 years, after which the impact of peers seems to disappear8. This work illustrates the importance of taking developmental periods into account when studying the relationship between environmental adversities and antisocial behavior. Even though environmental adversities have negative overall effects, the degree to which they affect individual development differs.

5-HTTLPR and antisocial behavior

In addition to the influence of environmental risk factors, twin and adoption studies have shown an important contribution of genetic factors to antisocial behavior with about 50% of the individual differences in antisocial behavior being explained by genetic variation9–11. Together with the MAOA and COMT gene, the serotonin transporter gene (SERT or 5-HTTLPR) is one of the most widely studied genes in relation to the development of antisocial behavior. 5- HTTLPR is a polymorphism of the serotonin transporter gene that has a long (L) or a short (S) variant. The S variant has been found to affect the efficiency of the transcription rate of the gene, thereby regulating the serotonergic availability in the brain12. Previous studies in monkeys have shown that S-carriage of 5-HTTLPR leads to an altered neural stress and thread circuitry as well as an increased hypothalamic–pituitary–adrenal (HPA) axis response to stress13,14. A study in mice demonstrated increased vulnerability to psychosocial stress in heterozygous 5-

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HTT knockout mice15. Also human studies have reported the involvement of 5-HTTLPR in mood and emotion regulation16. Despite the mixed findings in (generally) small samples, 5- HTTLPR has been suggested as an important candidate gene influencing the development of antisocial behavior. Recently however, Vassos et al. (2014)17 demonstrated the limitations of candidate-gene studies with a systematic review of all published genetic association studies of aggression and violence. Their meta-analysis did not find significant associations between any candidate gene and aggression outcomes17. Regarding 5-HTTLPR, 19 studies were meta- analyzed, yielding a non-significant odds ratio of 0.90. Nevertheless, their meta-analysis did not include gene-environment interactions, so it might be that moderation effects are missed out since the interaction effects of these genes with the environment were not modeled.

Gene-environment interaction (GE) studies

Animal studies have reported individual differences in behavioral and biological responses to environmental challenges. Suomi and others14 examined individual differences in resilience of rhesus monkeys that were reared in different environments during infancy: with peers only (PO) and with both mother and peers (MP). Their study revealed that rhesus monkeys carrying the S variant of 5-HTTLPR only showed excessive aggression and other neurodevelopmental impairments when they had been PO-reared, but not when they were MP-reared. Similarly, another study in monkeys demonstrated that the S allele was associated with increased reactivity to repeated, chronic stress, inclining a higher risk of developing affective psychopathology18. In humans, a twin study found that the effect of maltreatment on conduct problems was more severe in children at high genetic risk (i.e., having a co-twin with conduct disorder), increasing the probability of conduct disorder with 24% compared to 2% at low genetic risk19. Similarly, a twin study on antisocial behavior found higher estimates of heritability in adolescents in socioeconomically more advantaged environments, compared to adolescents in socioeconomically less advantaged environments, indicating a gene- environment interaction20. Scholars have suggested two distinct, but not mutually exclusive, theoretical frameworks when interpreting these GE effects. The diathesis-stress model proposes that the impact of negative life experiences on psychopathology depends on the individual’s pre-dispositional vulnerability or diathesis21–23. An alternative explanation branching from the diathesis-stress model is the differential susceptibility theory (DST) which advocates that, along with variation in vulnerability to negative experiences, individuals also differ in their susceptibility to positive environmental conditions24. A landmark study by Caspi et al. (2003)25 reported that individuals possessing two S alleles of 5-HTTLPR, were most adversely affected by stressful life events, but in the absence of these events, they showed the lowest scores on depression and suicidality. Even though these findings fit within the DST framework, individual studies examining this interaction have shown inconsistent effects. Moreover, meta-analyses have been inconclusive with two studies26,27 failing to support the original interaction, and one more inclusive meta-analysis finding support for an interaction effect28. The latter study however, yielded liberal inclusion criteria incorporating many indirect replications resulting in a higher risk of publication bias29. Several studies in humans have examined the relationship between 5-HTTLPR, environmental adversities and their effect on “antisocial behaviors”. Again, the findings of these candidate gene-environment interaction studies have generally been inconclusive and are typically characterized by underpowered samples29–31. Hence, the aim of the current study is to meta- analyze all reported studies in humans that investigated a 5-HTTLPRenvironmental adversities interaction effect on antisocial behavior, to test for an overall effect. We defined

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Discovering the genetic architecture of the mind environmental adversity as exposure to psychosocial risk factors associated with antisocial conduct32, such as childhood maltreatment, lower family SES and delinquent peer affiliation. Antisocial behavior was broadly defined and encompassed both reactive aggression as well as instrumental forms of aggression, such as psychopathy. To assess the methodological quality of the studies included in our meta-analysis, we performed a systematic review to guide the interpretation of results in terms of potential for biases and sources of heterogeneity. Materials and Methods

Literature search and selection strategy

A systematic literature search was performed to collect relevant studies. Two independent researchers (JT, KB) conducted the search using four online databases (Google Scholar, PubMed, Web of Science, and Psychinfo). We performed a search on the gene-environment interaction between the serotonin transporter gene (“5-HTTLPR”, “5-HTT”, “serotonin transporter gene”, SERT) and environmental adversity (“abuse”, “adverse childhood events” (or “ACE”), “maltreatment”, “SES”, “socioeconomic status”, “environmental adversity”, “parenting quality”, “peer behavior” and “peer network), on antisocial outcome measures 5 (“antisocial”, “aggression”, “conduct”, “criminal”, “delinquency”, “crime”). Studies were first evaluated on their relevance based on title and abstract. Secondly, we checked for the presence of an antisocial outcome measure, environmental adversity measure, and 5-HTTLPR measure (for criteria, see Textbox I). In addition, we searched the reference lists of relevant papers to find additional studies. Figure 5.1 reflects the flowchart of our search. Of the 17 potentially suitable studies, nine did not meet our inclusion criteria and were excluded after full text screening. No additional papers, based on reference lists, were identified. Thus, the search resulted in the inclusion of eight suitable studies.

Inclusion  • Antisocial outcome measure  • Environmental adversity measure  • 5-HTTLPR measure  •  Association test of interaction between ‘short-long’ 5-HTTLPR variant and  environmental adversity on antisocial outcome measure 

 Exclusion • Subjects with severe handicaps  • Medical intervention studies  • Focus on comorbidity with a particular disorder (e.g., ADHD, anxiety)   Textbox I. Inclusion and exclusion criteria for meta-analysis

Quality assessment

To evaluate the methodological quality of the included studies we used a short version of the quality assessment as proposed by Hayden et al. (2006)33 including the items as shown in Textbox II. Every item was rated positive (+), average (+ -), or negative (-), by two independent researchers (JJT, KB). In case of disagreement, an additional third researcher (TJCP) rated that particular study. Studies were ranked based on the total sum score of all items, with “+” being one point, “+-” being a half point, and “-” being zero points. In line with previous studies, we did not weigh studies in the meta-analysis by their quality scores. However, to test whether low scoring studies affected our results, we repeated the overall meta-analysis utilizing a descending and ascending step-wise removal of the studies based on their quality score34.

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Study Participation (1) Clear description of the key characteristics of the study population (distribution by age, gender and ethnicity). (2) The sampling frame and recruitment are described, including characteristics of the place of recruitment and response rate. (3) Inclusion and exclusion criteria are described.

Measures (4) A clear definition of the measure of antisocial behavior and environmental adversity is provided. (5) The measure of antisocial behavior and environmental adversity is adequately valid to limit misclassification (Includes when applicable: report of the reliability of the assessment).

Covariates (6) Age and gender are accounted for in the analysis. (7) IQ or SES or Race are accounted for in the analysis. (8) Covariate interaction terms are accounted for in the analysis.

Textbox II. Items of Quality Assessment, as derived from Hayden et al. (2006)

Descriptives

Data from the eight studies that met criteria for inclusion were extracted and summarized in Table 5.1, based on the design of Munafo et al. (2009)26. Briefly, we provide the characteristics of all samples, as well as study designs, measurements, interaction and statistical models.

Meta-analysis method

The literature search for this study resulted in eight eligible studies, comprising of 12 reasonably independent samples. The low number of studies found in the literature search motivated the combination of the non-independent samples; and this concerns seven of the included samples from three of the studies. In these cases, the extracted analyses were non-independent analyses divided per-sex or per-ethnicity from the same population. The present meta-analysis includes studies with a variety of designs (e.g. some being case-control studies, others being extreme- group sampling), statistical models (e.g. count data, logistic regression and factor analysis) and subtypes of analyses (e.g. within-sex and pooled-sex analysis). In addition, the degree of scaling regarding both the phenotype and the genotype varies across studies. These different sources of between-study variability raise an important issue regarding the assumption of heterogeneity of the effect sizes available for meta-analysis35. Study heterogeneity introduces difficulties of drawing a directional conclusion based on the overall meta-analysis result and this is further discussed in the Supplementary Material. Furthermore, only five out of the 12 samples included sufficient information on the effect size and within-study error variance for the parameter estimate to be converted into a common effect-size metric and perform a random- effects meta-analysis36. Based on these issues we performed a meta-analysis on the level of significance, i.e. the P values, rather than the effect sizes. The motivation for this approach and potential issues when trying to draw a directional conclusion are further discussed in the Supplementary Material.

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P value extraction

All of the included samples employed two-tailed, and hence non-directional tests of parameter significance; this is displayed in Table 5.3. The Supplementary Material explains in depth how we extracted and converted P values from the different non-directional tests of parameter significance into harmonized Z statistics. In summary, relevant P values were first independently extracted from the samples by three investigators (JT, KB & RKL), and any inconsistencies were settled by discussion. Secondly, the estimated direction of effect was harmonized over the studies to make sure that the variable coding of the effect allele, the coding of the environmental variable and the coding of the outcome measure had the same direction of effect in all studies. This is explained in depth in the Supplementary Material, and the result of this harmonization is shown in Table 5.3, column “Harmonized direction of GE” and column “Harmonized Z statistic”. Further, the meta-analysis was performed following the often employed methodology of combining significance levels referred to here as the non-weighted Stouffer’s method and the weighted Liptak-Stouffer’s method described in e.g., refs. 37–39. In summary, all extracted two-tailed P values were harmonized into Z statistics that all have the same direction of effect in terms of the effect allele, the environmental adversity, and the outcome measure of antisocial behavior. 5 Stouffer’s method and the weighted Liptak-Stouffer’s method

The meta-analysis was first performed on the Z-transformed P values employing the non- weighted Stouffer’s method implemented in the following way J H<9 H ASNTGGFQ \ $ where k is the number of included Z statistics. This combined metric tests the null hypothesis of no effect in all samples and each sample is assigned the same weight (Borenstein, 2009, p. 328; Becker, 1994, p. 218-219)35,40. Positive and negative z-statistics cancel out, so that the combined metric can be seen as a reasonable approximation of an average effect size under the assumption that all transformed P values are derived from the same parameter estimate, as discussed in the Supplementary Material. A clear caveat of the method is that no difference in weight is given to the studies based on their measure of precision. At the same time, the P value for a given effect size is a function of sample size, and hence the metric doesincorporate information on the precision of a study (Becker, 1994, p. 227)35. Even though the non-weighted Stouffer’s method includes information of per-sample precision via the P value, we also performed a meta-analysis with the weighted Liptak-Stouffer’s method. This was carried out on the Z-transformed P values in the following way J H<9 ,HH ?HOSBJ;ASNTGGFQ \ J : `H<9 ,H where

,H \ _FGGH where FGGH is the effective sample-size of study " which is the full sample size except in the case of a sample being of case-control design. In these cases, the effective sample-size was calculated according to Willer et al. (2010)39 and rounded to closest integer.

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 \ FGGH f Z g DBRFR DNMSQNKR

If the effective sample-size would be the same in all samples, then ?HOSBJ;ASNTGGFQ converges 38 into ASNTGGFQ (Whitlock, 2005, p. 1369) . We chose not to perform the meta-analysis with the Fisher’s combined probability test, often referred to as “the sum of logs method”, since there is a bias towards low compared to high P values with this method, as explained in Whitlock (2005)38. Two-tailed P values of the overall Z statistic from the non-weighted Stouffer’s method and the weighted Liptak-Stouffer’s method were calculated as  \ d[e, as in Stock and Watson (2012)41.

Environmental adversity and sex specific meta-analysis

To perform stratified analyses, we divided the samples into two subsets: adverse childhood environment (n = 8) as well as SES and peer rejection (n = 4). Furthermore, we conducted both pooled-sex and within-sex analyses. Since all stratified analyses include a low number of samples, i.e. ^ , possible outliers might drive these subset analyses.

Robustness checks

As a robustness check we tested the impact of each study on the overall significance by removing them individually from the analysis. Additionally, we analyzed two subsets where only studies with an effective sample-size above the median or the average were included. All robustness checks were performed using the ?HOSBJ;ASNTGGFQ meta-analytic procedure, in order to control for extreme values from small samples given the low number of available studies. There is a possibility that the overall meta-analysis result is driven by false-positive findings in samples with small sample size, this is referred to as the small-study effect (Borenstein et al., 2009, p. 291)42. We therefore reran the meta-analysis while excluding samples with an effective sample-size lower than the average effective sample-size (+ )! FGG \ ) or lower than the median effective sample-size (& "'FGG \ ) and observed the effect on the overall P value. Moreover, through step-wise removal of studies with the smallest sample size, we tested the number of studies that could be removed before the overall P value would change to non-significant (i.e., two-tailed P value > 0.05 for the weighted Liptak-Stouffer method). We also performed both descending and ascending step-wise removal of the studies according to their rated quality score, as explained in section Quality Assessment.

P-curve

As an additional robustness check we utilized the meta-analytical procedure, the P-curve as recently introduced by Simonsohn, Nelson, & Simmons (2014)43 (see Supplementary Material for a detailed description of this method). We employed the P-curve in comparison with the non-weighted Stouffer’s method and the weighted Liptak-Stouffer’s since these methods rely on the availability of null results in the published body of literature. However, if null results are missing from the published body of literature because of publication bias then the results of the Stouffer’s and Liptak-Stouffer’s methods are biased towards showing a larger average effect than if the non-published null results would have been included44. We utilized two P-curves, the first testing the hypothesis of a positive interaction effect between the S variant and environmental adversity on antisocial behavior by including the subset of the two- tailed statistically P values from significant estimates with a positive interaction effect, which were then divided by two. The second P-curve tests the two-sided hypothesis of an interaction

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Discovering the genetic architecture of the mind effect that can take both positive and negative values in the terms of the S variant and this was performed on all of the statistically significant two-tailed P values in our sample. Since the publication of this study, the reliability of the P-curve for analyses of non- experimental data has been criticized (Bruns & Ioannidis, 2016)67.

Rosenthal’s failsafe N

The potential for publication bias was further assessed through calculation of a failsafe number @V : : (GR) using the formula GR \ W d88 [ D e, where 8 is the total number of samples in the UX meta-analysis, D is the two-tailed critical value of  with a type I error rate, - \  , and 8 45 is either the ASNTGGFQ or the ?HOSBJ;ASNTGGFQ obtained for the total sample . The fail-safe N, first described by46, is the number of additional “negative” statistical analyses (yielding an average Z-statistic of zero) that would need to be added to our meta-analysis to make the combined effect size statistically non-significant. Noteworthy, this method of publication-bias testing is heavily criticized in the recent publication bias literature regarding issues of interpretability under heterogeneity of included samples, the lack of underlying statistical model and therefore varying results depending on which version of the test is employed. For 5 further discussion, we refer the interested reader to Becker (2005)47 and Borenstein et al., (2009, p. 284-285)42. Based on their discussion, we interpret the results from this publication-bias testing moderately. Nonetheless, we chose to test for publication bias with this method because most other common publication-bias testing procedures require effect sizes and are thus beyond the scope of this paper (Rothstein et al., 2005)48 because most of the included studies do not report these metrics. Results

Quality assessment

Table 5.2 displays the methodological quality of the studies included in our meta-analysis, listed in descending order. Importantly, none of the studies attained the highest possible quality score and they all failed to control for the mandatory covariategene and covariateenvironment interaction terms that are required for unambiguous interpretation of the estimated interaction effect49.

Overall meta-analysis

The meta-analysis was performed over 12 samples, most of which were independent, adding up to an effective sample-size of 6,724 subjects (Table 5.3). Analyses were conducted using the non-weighted Stouffer as well as the weighted Liptak-Stouffer method, however we generally restrict the report to the weighted Liptak-Stouffer method unless there is discordance between the methods. We found support for an interaction effect of the 5-HTTLPR genotype and environmental adversities on antisocial outcomes, with a P value of the non-weighted Stouffer method equal to 0.0029 and the weighted Liptak-Stouffer P value of 0.0006 (Figure 5.2 and Table 5.4). In the Supplementary Material we discuss the restrictions regarding directional inference based on our meta-analytical approach. Since directional conclusions are to be avoided, our results point toward a significant, non-zero interaction effect in at least one of the included samples. Thus, the meta-analysis supports the alternative hypothesis that there is an interaction effect between the 5-HTTPLR gene region and environmental adversities on antisocial

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Chapter 5 behavior, however the interpretation concerning which allele does actually increases sensitivity remains ambiguous. This is further illustrated by the analysis in ref. 50 which estimated a u- shaped relationship with regards to the homozygotes.

Environmental adversity and sex specific meta-analysis

The weighted Liptak-Stouffer P value became less significant when focusing exclusively on the subset of eight samples that used adverse childhood environment as more specific parameter (P value = 0.020), and the choice of meta-analytic procedure gave noticeably different results when compared to the non-weighted Stouffer P value (p-value = 0.0017). However, both methods still resulted in statistical significance and the number of samples in the subset was relatively low. Outliers with below-average sample size such as the female African American sample of Douglas et al., 201151 (Z statistic = 5.00, n = 112), could therefore explain the difference in results between the methods. When focusing on the samples with SES and peer affiliation as the environmental adversity, the two-tailed Liptak-Stouffer P value became non- significant (P value = 0.055) and the two-tailed non-weighted Stouffer P value became very non-significant (P value = 0.47). In this case the weights given to the two large samples with positive effect, i.e. Aslund et al. (2013)52, and Kretschmer et al. (2014)53 clearly affects the results towards statistical significance, even though both methods gave non-significant results.  In the meta-analyses stratified by sex, we analyzed the combined male-female samples and the exclusive male and female samples separately. The pooled sex meta-analysis included six samples and the Liptak-Stouffer P value was significant (P value = 0.0085), however the non- weighted Stouffer P value was non-significant (P value = 0.063). The sex-specific analyses both comprised of three samples and the analysis revealed a significant overall effect for females (Liptak-Stouffer P value = 0.032, Stouffer P value = 0.0019) but a non-significant interaction effect for males (Liptak-Stouffer P value = 0.27, Stouffer P value = 0.45).

Robustness checks

The robustness check showed that the results were robust against the removal of each sample individually, as the meta-analytic two-tailed Liptak-Stouffer P value did not change to being non-significant (i.e. P value > 0.05). The result of this robustness check is displayed in Table IV. However, when removing Aslund et al. (2013)52, then the two-tailed Liptak-Stouffer P value changed to be marginally significant, i.e. in the range of 0.01–0.05. This shows that the overall result is sensitive to the exclusion of either large samples or samples with large effects such as Aslund et al. (2013) dH \   FGG \  e. After exclusion of the seven samples with lower than average effective sample-size the overall Liptak-Stouffer P value remained significant (P value < 0.0001). Likewise, after exclusion of the six samples with lower than median effective sample-size the overall Liptak-Stouffer P value remained significant (P value ] 0.0001). Similarly, a step-wise removal of the studies with the smallest sample size revealed that the 10 smallest of the 12 samples could be removed before the overall two-tailed Liptak-Stouffer P value would change to non-significance (P value > 0.05). Step-wise removal of the samples based on the quality assessment score showed that the results might be driven by the studies with the lowest quality. When we first removed the samples with lowest quality the overall weighted Liptak-Stouffer P value remained significant until only the two highest rated studies remained, i.e. Sadeh et al. (2012)54 and Cicchetti et al. (2012)55 with a tied quality score of seven. When only these two studies remained the Liptak-Stouffer P value equals 0.1499. When reversing the step-wise removal, starting with the highest quality studies

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Discovering the genetic architecture of the mind first, the Liptak-Stouffer P value stayed significant until only the three studies with the lowest quality remained. We however emphasize that the low overall number of samples in our study makes all analyses based on exclusions very sensitive to outliers in our study.

P-curve

The result from the P-curve method regarding the P values with a positive direction of effect in terms of the S variant supported the conclusions of the other meta-analytical methods above. The right-skew P value (i.e. test for evidential value) for this subset of P values was <0.0001, see Figure 5.3 P-curve – positive direction of effect. When testing the two-tailed P values the right-skew P value was <0.0001, see Figure 5.4 P-curve – positive and negative direction of effect. The conclusion from the P-curve method is therefore in support of the above meta- analysis results, while at the same time avoiding assumptions regarding missing studies in our sample. Since both P-curves showed statistically significant signs of evidential value, we did not test if the observed distributions were flatter than what would be expected at a power of 33%. The results from the left-skew test of the P-curve (i.e. testing for signs of possible P- hacking) showed that neither of the P-curves were evident of possible P-hacking (both left- skew P values > 0.99). 5

Rosenthal’s failsafe N

The Rosenthal’s failsafe GR of 424.7 for the Liptak-Stouffer method indicated that a ratio of 35 (] 424.7 / 12) unpublished or undiscovered statistical tests with an average effect of zero (two-tailed P value = 1) and average effective sample-size (FGG = 560) would be needed per included test in our analysis to make the Liptak-Stouffer P value non-significant. The GR for the Stouffer method resulted in an approximate ratio of 27 (] 321.4 / 12) and was hence in concordance with the GR for the Liptak-Stouffer method. Discussion

To circumvent the issue of statistical power typical for single GE studies, we conducted the first meta-analysis summarizing the results of individual studies that examined the potential interaction effect between 5-HTTLPR and environmental adversity on the level of antisocial behavior. Despite the overall significant interaction effect found in this study, the results should be interpreted in the light of some important limitations. First, the use of the meta-analytical methods based on pooled P values limits the interpretation in terms of direction of the effect. Therefore, our analyses cannot demonstrate whether the significant interaction effect is driven by the S or L allele or a combination of both. Scholars have argued that significance of the Liptak-Stouffer test merely indicates that the GE interaction is statistically different from zero in at least one of the included studies37,56. Therefore, once well-powered direct replication studies, employing hierarchical regression designs and reporting effect sizes, become available, more informative meta-analyses utilizing common effect-size metrics could further examine these results. Secondly, and most importantly, the moderate quality of the individual studies, and specifically the heterogeneity of their different tests and statistics undermines the reliability of our meta-analytical findings. An important methodological shortcoming in most GE studies is insufficient inclusion of potential confounding effects of covariates on the GE interaction term. Even though most of the studies in our meta-analysis included confounding variables such as ethnicity, gender, age

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Chapter 5 in the regression equation to control for these covariates on the main effects of genotype and environment, they did not control for the effects of the covariates on the GE interaction. Controlling for covariate interaction terms in the full model is preferred and yields more power than creating subsets of data based on the covariate and then examining the GE term. Therefore, it is essential to include all relevant covariategene and covariateenvironment interactions in the same model that tests the GE term in order to properly control for the confounding effects of covariates49. Thus, GE studies that improperly model covariates do not rule out alternative explanations for their findings. Next, most studies included in our meta-analysis utilize ethnically heterogeneous samples (Table 5.1) and since allele frequencies of the 5-HTTLPR polymorphism differ across ethnicities, stratification could be a potential confounding variable as an ethnicity-by- environment interaction offers an alternative explanation for the reported GE effect49,57. None of the studies corrected for population structure at the genetic level, as genome-wide data were not available in these samples at the time of analysis. Besides cross-ethnic differences in allele frequencies, a study by Willams et al. (2003)58 also demonstrated differential effects of 5- HTTLPR on central nervous system serotonin function, with the short allele being associated with higher CSF 5-HIAA levels in African Americans, but lower levels in Caucasians. These differential effects across ethnicity of 5-HTTLPR on central nervous system serotonin function, could explain why the study of Douglas et al. (2011)51 found a significant interaction effect in the African Americans but not in the European Americans (see Table 5.3). Hence, the use of admixed samples in the present study confines the validity and generalizability of our results and emphasizes the need of future GE studies to employ ethnically homogenous samples or correct for population structure by using genome-wide data. The above concerns are supported by our systematic review and quality assessment revealing that all of the studies included had methodological shortcomings, such as absence or improper controlling for covariates. Lastly, given the heterogeneity in the type of measures, design, sex and age range across studies, the studies cannot be considered as direct replication attempts testing the same underlying hypothesis, necessitating further caution in the interpretation and generalization of the results59. Although the interpretation of our findings requires carefulness in the light of the aforementioned limitations, they are in line with previous studies showing a differentiated reactivity to environmental stressors between S and L carriers of 5-HTTLPR14,60. The interaction effect on antisocial behavior found in this study offers several possible routes of explanation. An explanation is that L carriers of 5-HTTLPR are somehow more resilient to environmental adversity than S carriers. This hypothesis is particularly interesting in view of research suggesting that the L allele might be a potential risk factor for psychopathy61. Psychopathy is associated with the more instrumental type of aggression and may have a different underlying etiology than reactive aggression62. Therefore, the L allele of 5-HTTLPR, which is linked with hypo-responsivity to environmental factors, may thus predispose to instrumental aggression, whereas the S allele, associated with hyper-responsivity to the environment, may predispose to reactive aggression61. The study of Sadeh et al. (2010)63, included in our meta-analysis, supports this line of reasoning. They reported a significant interaction between the L allele and callous-unemotional and narcissistic features of psychopathy in the presence of environmental adversity. This might explain why we did not find evidence for a directional effect in our meta-analysis, since we employed a broadly conceptualized outcome measure that included psychopathy. Moreover, due to the liberal inclusion criteria because of the small number of studies identified in the literature, the meta-analysis comprises both population-based and clinical samples, which differ in severity and may thus have distinct neurobiological underpinnings. An alternative explanation for the interaction effect would be that a positive environment (i.e. secure

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Discovering the genetic architecture of the mind attachment relationships), somehow protects S or L carriers for their increased risk on developmental problems. Thus far, GE research has predominantly focused on the genetic vulnerability towards negative environmental factors, such as maltreatment or harsh parenting. However, in potential, those individuals who are more malleable through their increased genetic plasticity, could also be more receptive to treatment and may benefit more from positive environments64. To what extent significant GE effects fit into the framework of differential susceptibility or diathesis-stress remains to be understood as it cannot be inferred from the current meta-analysis whether the 5-HTTLPR polymorphism resembles the characteristics of a differential susceptibility or risk gene. Further research in the exact etiological mechanisms underlying these differences in susceptibility or genetic risk is warranted, as more insights into the gene-environment interplay could improve prevention programs and could potentially lead to more tailored-based treatment65. Cohen & Piquero (2009)65 estimated that preventing a high- risk youth from developing a criminal career could save society $2.6 million to $4.4 million. By increasing our knowledge on the etiological risk factors contributing to antisocial development, we could reduce the societal and financial burden of antisocial behavior. Concerning the external validity, since our meta-analysis result is mainly based on studies that used adverse childhood environment as a source of environmental adversity (five out of eight studies), it is too early to conclude that the impact of other environmental factors, such as SES 5 or peer affiliation is dependent on the 5-HTTLPR genotype. It is thus important to further examine the relative contribution of specific environmental stressors in the GE interaction model on antisocial behavior. A recent study that made use of genome-wide meta-analysis results from the Psychiatric Genomics Consortium showed that the effect of polygenic risk scores on depression was increased in the presence of childhood trauma66. Using the same approach, future research should examine whether individuals with high polygenic vulnerability exposed to childhood environmental adversity are particularly at risk for developing antisocial behavior. To conclude, taking advantage of the rapid technological advancements in the field of genomics, including the rise of genome-wide data and polygenic risk score analysis, future studies should focus on how a broader polygenic profile interacts with environmental adversity factors to achieve a more sophisticated understanding of how biosocial interactions impact on antisocial development. Naturally, these polygenic studies should acknowledge the statistical issues accompanying GE research, and properly control for potential confounders using well- powered study designs.

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References – Chapter 5

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rejection and acceptance in preadolescence and early adolescence, serotonin transporter gene, and antisocial behavior in late adolescence: The TRAILS Study. Merrill. Palmer. Q. 60, 193–216 (2014). 54. Sadeh, N., Javdani, S. & Verona, E. Analysis of monoaminergic genes, childhood abuse, and dimensions of psychopathy. J. Abnorm. Psychol. 122, 167–179 (2012). 55. Cicchetti, D., Rogosch, F. a. & Thibodeau, E. L. The effects of child maltreatment on early signs of antisocial behavior: Genetic moderation by tryptophan hydroxylase, serotonin transporter, and monoamine oxidase A genes. Dev. Psychopathol. 24, 907– 928 (2012). 56. Laoutidis, Z. G. & Luckhaus, C. The Liptak-Stouffer Test for Meta-Analyses. Biol. Psychiatry 77, e1–e2 (2015). 57. Ioannidis, J. P. A., Ntzani, E. E. & Trikalinos, T. A. ‘Racial’ differences in genetic effects for complex diseases. Nat. Genet. 36, 1312–1318 (2004). 58. Williams, R. B. et al. Serotonin-related gene polymorphisms and central nervous system serotonin function. Neuropsychopharmacol. Off. Publ. Am. Coll. Neuropsychopharmacol. 28, 533–541 (2003). 5 59. Manuck, S. B. & McCaffery, J. M. Gene-environment interaction. Annu. Rev. Psychol. 65, 41–70 (2014). 60. Champoux, M. et al. Serotonin transporter gene polymorphism, differential early rearing, and behavior in rhesus monkey neonates. Mol Psychiatr 7, 1058–1063 (2002). 61. Glenn, A. L. The other allele: exploring the long allele of the serotonin transporter gene as a potential risk factor for psychopathy: a review of the parallels in findings. Neurosci. Biobehav. Rev. 35, 612–620 (2011). 62. Glenn, A. L. & Raine, A. Psychopathy and instrumental aggression: Evolutionary, neurobiological, and legal perspectives. Int. J. Law Psychiatry 32, 253–258 (2009). 63. Sadeh, N. et al. Serotonin transporter gene associations with psychopathic traits in youth vary as a function of socioeconomic resources. J. Abnorm. Psychol. 119, 604– 609 (2010). 64. Belsky, J. et al. Vulnerability genes or plasticity genes? Mol. Psychiatry 14, 746–754 (2009). 65. Cohen, M. A. & Piquero, A. R. New evidence on the monetary value of saving a high risk youth. J. Quant. Criminol. 25, 25–49 (2009). 66. Peyrot, W. J. et al. Effect of polygenic risk scores on depression in childhood trauma. Br. J. Psychiatry J. Ment. Sci. 205, 113–119 (2014). 67. Bruns, S. B. & Ioannidis, J. P. A. P-curve and p-hacking in observational research. PLoS One 11, 1–13 (2016).

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Figures and tables – Chapter 5

    

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Figure 5.1. Flowchart of study selection.

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5

Figure 5.2. Forest plot – Harmonized z-statistics and meta-analysis estimates. Forest plot of the harmonized z-statistics, as well as the overall Stouffer and Liptak-Stouffer z-statistics.

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Figure 5.3. P-curve – positive direction of effect. P-curve testing the one-tailed hypothesis of a positive effect in terms of the S allele by including the subset of two-tailed statistically significant P-values from estimates with a positive interaction effect divided by two.

Figure 5.4. P-curve - positive and negative direction of effect. P-curve testing the two-tailed hypothesis of a positive or negative effect in terms of the S allele by including all of the statistically significant two-tailed P-values in our sample.

138 Discovering the genetic architecture of the mind Statistical Model models Regression Hierarchical linear linear Hierarchical regression General linear model ANCOVA Logistic generalized estimating equations regression Latent class multinomial regression logistic logistic Stepwise regression Grouping: Genotype vs vs LS SS LL LL vs. SL vs. SS SS vs vs vs LS SS LL vs vs LS SS LL vs vs LS SS LL SS vs SL vs LL S/S and S/L vs. L/L

5 Environmental (EA) adversity nomination acceptance; assessment Family income level and categories) (three 1-7) (coded occupation SES, 7-point Likert Likert 7-point SES, low, in divided scale; SES and high medium on DHS based MCS abuse, (physical records emotional neglect, maltreatment) ACE (violent crime, abuse, sexual exposure, abuse) physical 0-3 score Ordinal Retrospective In-home interview on (neglect, maltreatment sexual abuse, physical abuse) Adverse Childhood Index- Environmental 0- calculated mean score 2 Design Design Antisocial Process Device- Screening item delinquency scale scale delinquency item self-report; Peer ratings; form report teacher SSADDA, 44-item criteria; ASPD specific score dichotomous 7 overt of frequency to converted items, ASB summed score nonviolent or violent experts by group Ancestry Ancestry & Measure Antisocial American American and African American Total N 1381 European European 1381 184 Caucasian a to assigned Subjects Age Range or SD Age or Range American/African American/African American (37- 38.2/41.0 M= 42) T1: M=11.1 1118 Mixed 31 itemsASBQ of at T1 Peer rejection & ± M=32.1 (N=72, 12.1) group Non-violent ± M=35.4 (112, 11.3) 42.0 42.0 SD=1.5 14.3, M= 118 Mixed self-report 20-item 51.3 51.3 17-18 49.8 M= 1467 11.3 (10-12) Mixed 627 on 15- based score Sum Mixed European 58.9 Pittsburgh Youth Survey Not stated 48.0 (12-20) M=15.7 group 2488 Mixed Violent 100 on interview In-home Youth from from Youth ility or risk Survey of Subjects National Volunteers Children Prospective Prospective sample of youth from US US from youth of sample schools high treatment or legal agencies agencies legal or treatment community and general Cross-sectional - Vestmanland Life Adolescent - Cross-sectional camp summer in participating for programme research low-income school-aged children Cross-sectional - on alcohol, dependent 4 sites at opioids or Longitudinal - adolescents- Dutch cohort Tracking Lives Individual Adolescents’ Survey (TRAILS) Cross-sectional - of Study Longitudinal random - Health Adolescent Cross-sectional - in forensic examination to the Institute of Forensic University the of Psychiatry evaluation for Saarland the of responsib legal of Cross-sectional - assessment Study Sampling % Male Mean Age (M), Age Mean Study Sampling Male Aslund et 2013 al. % et Cicchetti 2012 al. Douglas et 2011 al. Kretschmer 2014 al. et Li et al. 2010 et al. Reif 2007 Sadeh et al. 2010 Table Baseline 5.1 | characteristics of included all studies

139 Chapter 5 y S: social social S: ial personalit regression analyses analyses regression ASPD: antisoc ASPD: SS vs. LL SS Hierarchical Childhood abuse, CTQ CTQ abuse, Childhood scale, Composite continuous mean; MCS: maltreatment classification; SE classification; maltreatment MCS: mean; ial behaviour questionnaire, ial behaviour Callous-Unemotional; Callous-Unemotional; narcissism; impulsivity Screening Version - Version Screening two- of score Composite and factor model three-factor ial behaviour; ASBQ: antisoc ial behaviour; ASBQ: ssment for Drug Dependence and Alcoholism. ssment for Dependence and Alcoholism. Drug DHS: department of human services; M: M: services; human of DHS: department 100 M=30.9 (18-61) 237 Mixed Psychopathy Checklist: Checklist: Mixed Psychopathy 237 (18-61) M=30.9 100 Selected on Selected recruited via agency referrals, referrals, agency via recruited and advertisement newspaper flyers posted high rates of antisocial antisocial of rates high and high behaviour adversity. psychosocial via Recruited agencies, probation/parole and mandated jail, county as well as centers, treatment newspaper via advertisements targeting legal with individuals convictions Cross-sectional - ACE: adverse childhood events; ASB: antisoc ASB: childhood events; adverse ACE: Sadeh et al. 2012 Note: Note: questionnaire; trauma Childhood disorder; CTQ: Asse SSADDA: Semi-structured status; economic

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Table 5.2 | Quality assessment results of included studies

Domain Criterion A B C D E F G H Total Cichetti et al., 2012 + + + + + + + - 7 Sadeh et al., 2012 + + + + + + + - 7 Aslund et al., 2013 +/- + + + + + + - 6.5 Li et al., 2010 + + +/- + +/- + + - 6 Sadeh et al., 2010 + + - + + + + - 6 Douglas et al., 2011 + + - + + + +/- - 5.5 Reif et al., 2007 + + + +/- - + + - 5.5 Kretschmer et al., +/- +/- - + + +/- - - 3.5 2014 Note: A: Clear description of the key characteristics of the study population. B: The sampling frame and recruitment are described. C: Inclusion and exclusion criteria are described. D: Clear definition of the measure of antisocial behavior and EA is provided. E: The measure of antisocial behavior and EA is adequately valid to limit misclassification. F: Age and gender are accounted for in the analysis. G: IQ or SES or Race are accounted for in the analysis. H: Covariate interaction terms are accounted for in the analysis. 5

141 Chapter 5 Table 5.3 continues on next page. Table Environmental adversity measure measure adversity Environmental - p 0.019 Peer rejection 0.019 Peer <0.001 <0.001 events Childhood Adverse <0.001 events Childhood Adverse value value -value -value p 0.16 0.023 Socio-economic status status Socio-economic 0.023 0.16  0.37, SE = 0.76 0.76 = SE 0.37, 0.62 Maltreatment 1.82, SE = 0.82 0.82 = SE 1.82, <0.05 Maltreatment -statistic = 1.02 1.02 = -statistic 0.31 abuse Childhood CTQ   t from correspondence with author] author] with correspondence from OR in Table 6, p. 20] 20] p. 6, Table in OR 20] p. 6, Table in OR Extractedtest statistic of the GxEinteraction Reported Effect allelein statistical test , Sex, , Sex, t Ethnicity Ethnicity Study, Year Year Study, Effective N Aslund et al., 2013 2013 al., et Aslund 2013 al., et Aslund 2012 al., et Cicchetti 2011 al., et Douglas Mixed F, 464, 2011 al., et Douglas Mixed M, 752, Mixed M/F, 627, 2011 al., et Douglas EA NA M/F, 315, NA NA AA F, 112, 2014 al., et Kretschmer NA AA M, 261, 2010 al., et Li Mixed M/F, 2.74 1118, = 661) F(2, S 2010 al., et Li 3.91 6.67 = = F(2,689) 586) F(2, 2007 al., et Reif S S 2010 al., et Sadeh 2 = df 1.4, = statistic Chi-square 2012 al., et Sadeh Mixed F, 1294, from [Calculated 0.3422. = SE 1.8181, = beta Logit Mixed M, 1194, Caucasian M/F, 172, and 4, [Table 0.021 = Mixed beta M/F, Standardized 178, from [Calculated L 0.3083 = SE 1.0296, = L beta Logit Mixed M/F, 237, L L 0.5 L 0.065 0.02 0.001 events Childhood Adverse = Beta 0.65 Log-linear = SE -1.65, = Beta Logit status Socio-economic = Beta Log-linear status = Socio-economic Beta regression Linear Maltreatment regression Linear 0.011 environment adverse Childhood Effective sample-size calculated as reported in methods section. section. methods in reported as calculated sample-size Effective Table 5.3 | Extracted and harmonized values of the meta-analysis sample, in total 12 samples derived from eight studies studies eight from derived samples 12 total in sample, meta-analysis the of values harmonized and Extracted | 5.3 Table t

142 Discovering the genetic architecture of the mind

2 Table 5.3 cont. cont. 5.3 Table -statistic z Harmonized Harmonized - p

v -2.2734 -2.2734 2.3455 2.3455 4 w value Calculated two-tailed two-tailed

statistics, see methods section. section. methods see statistics, z- (S variant) (S d u nal study report. Negative indicates a greater sensitivity among L L among sensitivity greater a indicates Negative report. study nal

direction of 5 G x E Negative 0.0653 -1.8432 -1.8432 0.0653 Negative 3.2047 0.0014 Positive 0.6799 0.4966 Positive 0.4867 0.6265 Positive 0.0230 Negative -value is included in the meta-analysis. the in included is -value p opean Americans;opean F:Available Female; M:Male; Not NA: -value; hence the reported reported the hence -value; p -values wereprovided in all calculations and analyses. p coded [p. 59; Fig. 2, p. 58] 58] p. 2, Fig. 59; [p. coded coded[p. 59; Fig. 2, p. 58] 19] p. 795] 6] [p. coded reverse SES however Study, Year Year Study, 2013 al., et Aslund 2013 al., et Aslund reverse SES however E, with interacted when females in ASB study] decreases to original L [Reference effect of interaction 2012 Direction al., et Cicchetti reverse 2011 al., SES et Douglas however E, with interacted when males in ASB increases L Harmonized 2011 al., 920] et p. 8. Douglas [Fig E with interacted when ASB decreases L 5, [Table E with interacted when EA in ASB decreases (L significant Not 2011 al., et Douglas 20] p. 6, [Table E. with interacted when females AA in ASB decreases L 2014 al., et Kretschmer Positive 20] p. 6, [Table E. with interacted when males Positive AA in ASB increases L 2010 al., et Li 205] p. 4, [Table E with interacted when ASB decreases L Negative 2010 al., et Li <0.0001 2007 al., et Reif 0.0206 2010 al., et Sadeh 0.0010 795] [p. E with interacted when females 5.0064 in ASB decreases L Positive [p. E) with interacted when males in ASB decreases 2012 al., (L et Sadeh significant Not 2.3159 2379] [p. E with interacted when ASB decreases L -3.3016 not, does SS and SL which E, with interacted when ASB decreases LL Positive 0.0190 7] [p. E) with interacted when ASB increases (L significant Not 0.0266 Negative Positive 2.2170 0.3088 0.0120 -1.0178 2.5115 AA: African Americans; ASB: Antisocial ASB: Antisocial Americans; AA: African Behavior; EA: Eur Study does not report the required information in order to calculate an exact exact an calculate to order in information required the report not does Study

Positive indicates greater sensitivity among S variant 5-HTTLPR subjects to environmental adversity, as presented in the origi the in presented as adversity, environmental to subjects 5-HTTLPR S variant among sensitivity greater indicates Positive The values displayed inthe table are rounded; exact Note: u v w variant 5-HTTLPR subjects to environmental adversity. For further discussion of the extraction and calculation of the the harmonize of calculation and extraction of the discussion further For adversity. to environmental subjects 5-HTTLPR variant

143 Chapter 5

Table 5.4 | Results of Stouffer’s z meta-analysis Study, Year Effective Direction Harmonized Two-tailed p-value of * sample-size, of effect z-statistic weighted Liptak-Stouffer sex, z-statistic after Study ethnicity Exclusion Aslund et al., 2013 464 F Negative -1.8432 <0.0001 Aslund et al., 2013 752 M Positive 3.2047 0.0130 Cicchetti et al., 2012 627 M/F Positive 2.3159 0.0045 Douglas et al., 2011 315 M/F, EA Positive 0.6799 0.0008 Douglas et al., 2011 112 F, AA Positive 5.0064 0.0053 Douglas et al., 2011 261 M, AA Negative -3.3016 <0.0001 Kretschmer et al., 2014 1118 M/F Positive 2.3455 0.0071 Li et al., 2010 1294 F Positive 2.2170 0.0066 Li et al., 2010 1194 M Positive 0.4867 0.0004 Reif et al., 2007 172 M/F Positive 2.5115 0.0023 Sadeh et al., 2010 178 M/F Negative -2.2734 0.0001 Sadeh et al., 2012 237 M/F Negative -1.0178 0.0002

Total effective sample 6724 Overall two-tailed p-value 0.0006 size of weighted Liptak- Stouffer Note: F: female; M: male; EA: European Americans; AA: African Americans *Positive indicates greater sensitivity among S variant 5-HTTLPR subjects to environmental adversity, as presented in the original study report. Negative indicates a greater sensitivity among L variant 5- HTTLPR subjects to environmental adversity. For further discussion of the extraction and calculation of the Harmonized z-statistic, see methods section and Supplementary Materials III) Transformation of p-values into a common z-statistic.

144 Discussion and conclusion

“Life should not be a journey to the grave with the intention of arriving safely in a pretty and well preserved body, but rather to skid in broadside in a cloud of smoke, thoroughly used up, totally worn out, and loudly proclaiming “Wow! What a Ride!” Hunter S. Thompson

“My supervisor once told me that the only good thesis is a finished thesis.” Philipp D. Koellinger 6 Chapter 6

In this sixth and final chapter, I first discuss how selected results answer the main research questions and relate to the previous literature. Thereafter, I conclude the thesis and discuss some study limitations. Finally, the thesis is abbreviated in a general summary. 1. a. Do GWAS in hundreds of thousands of individuals identify robustly associated SNPs with the main phenotypes studied in Chapters 2–3? The genome-wide association analyses identified a varying number of SNPs associated with the main phenotypes studied in Chapters 2–3. It was expected that the number of identified associations would depend strongly on sample size. The analyses of subjective well-being, depressive symptoms, and neuroticism are the smallest of the main GWAS, performed in 298,420, 161,460, and 170,911 individuals, respectively. Upon publication, while these were the largest GWAS reported for these traits1 the sample sizes were only sufficient to identify a handful genome-wide significant associations. We found three approximately independent “lead SNPs” associated with subjective well-being, two with depressive symptoms, and 11 with neuroticism (Table 3.1). It is possible that the association signal was attenuated by suboptimal phenotypic measurement and overlap, as well as imperfect genetic correlations, across the 59 study cohorts included in the meta-analysis of subjective well-being. In contrary, the meta-analyses of depressive symptoms and neuroticism included only two to three study cohorts, which may explain the similar number of hits, while those GWAS were much smaller. We estimated the SNP heritability of neuroticism to be about twice that of subjective well-being and depressive symptom, 9.1% versus 4–4.7%, which likely explains why the number of associations is the largest for neuroticism. Nonetheless, the identified lead SNPs are among the first genome-wide significant associations reported for these traits2,3, and the findings could be considered a small first step in the effort to identify the genetic variants responsible for their missing heritability. In Chapter 2, the GWAS identified a much larger number of lead SNPs; 124 with general risk tolerance, 167 with adventurousness, 42 with automobile speeding propensity, 85 with drinks per week, 223 with ever smoker, 117 with number of sexual partners, and 106 with the first PC of the four risky behaviors (Supplementary Materials). A few previously reported associations are corroborated by the findings, while the vast majority of the identified associations are novel. The analysis of general risk tolerance is the largest of the GWAS, which included 939,908 individuals, and the six supplementary GWAS included 315,894 to 557,923 individuals. Compared to Chapter 3, larger sample size is an obvious explanation of the much greater number of identified lead SNPs. However, two additional explanations deserve to be mentioned. First, most of the GWAS in Chapter 2 were either performed in a single large and relatively homogenous cohort, the UKB, or as a meta-analysis of two large and relatively homogenous cohorts (the UKB and the 23andMe cohort). Therefore, it is likely that there was less attenuation because of phenotypic heterogeneity and imperfect genetic correlation. Second, the GWAS in Chapter 2 were performed with a recently developed method and software with which it is possible to perform computationally tractable GWAS with linear mixed models in large genetic datasets4,5. Importantly, GWAS performed with linear mixed models have increased power to detect genetic associations. This method was introduced in the later stages of the study reported in Chapter 3, and was therefore not implemented there. In combination, these factors may explain why the GWAS in Chapter 2 identified a much larger number of genetic associations. We performed various types of replication and robustness analyses, which suggest that the results are not spurious overall. Thus, our findings show that GWAS in hundreds of thousands of participants can identify robust associations with mental and behavioral traits for which it has proven difficult to identify genes and biomarkers6,7. Overall, our findings motivate further

146 Discovering the genetic architecture of the mind genetic studies of these traits when larger samples become available in order to discover the remaining genetic variants responsible for their missing heritability, discussed next. b. What do the results reveal about their genetic architectures? In Chapter 2, the lead SNPs account for only a small part of the missing heritability. In sum, the 124 general-risk-tolerance lead SNPs explain about 0.5% of the phenotypic variation. In that chapter, we estimated the SNP heritability of general risk tolerance with high precision to between 5.5 and 8.5%, depending on the method. Thus, the size of the hiding heritability, which is expected to be revealed by increased GWAS sample size, is in the order of 5–8%. The same applies for the supplementary phenotypes. The joint R2 of the lead SNPs, depending on the trait, is about 0.41 to 1.91%, and the hiding heritability is between 5.8 and 15.9%. In Chapter 3, the handful of lead SNPs jointly account for an even smaller, almost minute, part of the variability, roughly 0.03% of the variation in subjective well-being, 0.06% in depressive symptoms, and 0.23% in neuroticism. Thus, the hiding heritability remains almost equal to the SNP heritability. Polygenic prediction performed with a much larger set of SNPs (about 1 million) supports the notion that the hiding heritability will be revealed with increased sample size. A general-risk- tolerance PGS explains up to 1.8% of the phenotypic variation, about 3.6 times more than the 124 lead SNPs. A PGS for subjective well-being explains about 0.9% of the variation in subjective well-being, 0.5% in depressive symptoms, and 0.7% in neuroticism. Thus, the PGS for subjective well-being explains about 30 times more than the three lead SNPs. Unfortunately, GWAS in hundreds of thousands to almost a million individuals did not reveal the bulk of the missing heritability. Nonetheless, the following paragraphs will discuss some interesting 6 features of the trait’s genetic architectures that were revealed by the results. As discussed in Chapter 1, it has been proposed that part of the still-missing heritability8, which is not expected to be revealed by increased GWAS sample size, may be explained by structural variants9,10, such as inversions. Overall, it has not been concluded whether the alleles of structural variants are actually in LD with SNPs, and thus, tagged in GWAS11. Interestingly, we found that neuroticism is strongly associated (P < 2.0110–15) with a previously known inversion located on chromosome 8 (~7.89 to 11.8 Mb), and less strongly (P < 1.2510–9) with another known inversion on chromosome 17 (~43.6 to 44.3 Mb). The estimated effect of the chromosome 8 inversion is stronger than that of any individual lead SNP (R2 ~ 0.06%), but similar to the joint R2 of the six near-independent and significant SNPs that tagged it. The effect of the chromosome 17 inversion was estimated to 0.033%, and thus, is similar to the effect of its only significant tag SNP (R2 ~ 0.028%). Both inversions were estimated to be bi-allelic and common in the British population, with minor allele frequencies of about 0.44 and 0.22, respectively. It is obviously too early to say for sure, but because the effects of these inversions appear to be tagged by common SNPs, and would thus be identified by traditional GWAS, it remains an open question whether such common structural variants actually account for the still-hiding heritability, instead of the missing heritability. At the same time, rare structural variants that cannot be tagged in traditional GWAS because of limited linkage with common or low-frequency SNPs12,13 would instead contribute to the still-hiding heritability. Notably, we annotated many of the lead SNPs identified in Chapter 2 to more than a hundred smaller structural variants of various types identified by Sudmant et al. (2015), as well as to about 40 larger candidate inversions identified by Gonzalez & Esko (2017, unpublished), including the two discussed above. Because of the very large number it was not within the scope of that study to perform the same thorough variant calling as in Chapter 3. Nonetheless, the annotated variants could be considered interesting candidates for future studies on the relation between mental traits and structural variation. A surprising finding was that many of these regions harbor great overlap of association signal across the main phenotypes. In the literature,

147 Chapter 6 there is conflicting evidence with respect to whether structural variants have much influence on complex traits11,14. Our results, and in particular the more thorough variant calling in Chapter 3, support the notion that common SNPs do indeed tag structural variation, and the findings contribute to the somewhat limited evidence that suggests that common structural variants indeed have an influence on complex traits and disorders. Next, the findings align with the previous literature that has found that most GWAS findings are located in non-coding intergenic regions10,15,16. In Chapter 2, about 64% of the identified lead SNPs are intergenic17. These findings suggest that many of the identified SNPs are likely to be involved in the regulation of gene expression rather than the structure of RNA and/or proteins16. Because of the large number of identified associations in Chapter 2, it was not within the scope of that study to closely examine the genomic function of each association. Next, the 16 lead SNPs identified in Chapter 3 tell a slightly different story because almost all are intragenic17, and thus, located within genes. 14 of those are intron variants, i.e., within genes and involved in RNA transcription, but that get spliced away before translation into protein18. The remaining two are intergenic, and none are coding variants. However, a few of the 16 lead SNPs appear to be in linkage with nonsynonymous coding variants, for example the neuroticism lead SNP that tagged the inversion on chromosome 17. That particular SNP is in strong linkage with 11 known missense variants. It is possible that those are the truly causal variants in that region, but more extensive fine-mapping is required to come to a firm conclusion. Overall, fine- mapping of the associations identified in Chapters 2–3 is an interesting avenue for future research19. Finally, the small effects that we identified for most of the main phenotypes, and the difference in R2 between the lead SNPs and the PGS constructed with about a million variants, align with the previous evidence that suggests that mental traits are highly polygenic and typically lack common variants with large effects. An exception is the large effect of the top association with drinks per week, located in the ADH1B gene. It is reassuring that we identified several associations between drinks per week and various members of the ADH gene family because of their well-known function in alcohol metabolism20,21. Yet, it remains to be decided whether these traits confer to a completely infinitesimal “omnigenic” model22. Ultimately, it could be that we may never be able to detect all truly causal SNPs because an infinite sample size would be required23. Identification of the absolutely smallest effects could still be relevant to fully understand trait biology. Yet, I think that the absolutely smallest effects would not really contribute much to the predictive power of a polygenic score because they would not be estimated with high-enough precision in any finite sample. However, an observation in Chapter 2 speaks against the premise that all common SNPs are causal. Namely, the optimal predictive power with LDpred24, a polygenic-score method that includes SNPs in linkage disequilibrium (LD) and adjusts their effects for non-independence, was achieved when the fraction of causal SNPs was assumed to be 30%. However, that evidence is weak due to several limitations, such as the non-asymptotic sample size of the discovery GWAS and possible inaccuracy in the reference panel LD. Overall, it is too early to conclude whether the genetic effects on the studied traits are completely infinitesimal. c. What is the potential to use the results to strengthen inference in empirical research and to perform multi-trait analyses of genetically correlated traits? In Chapter 2, the potential to strengthen inference in empirical research was evaluated through polygenic prediction of alternative measures of risk tolerance, as well as personality dimensions and various risky real-world behaviors, among other traits. We found that a general-risk- tolerance PGS significantly explains variation in many of these traits. However, the predictive power was limited. It is the greatest for general risk tolerance itself, ranging from about 1 to 1.8%, and typically less than 1% for the other traits. Overall, the predictive power could be

148 Discovering the genetic architecture of the mind considered disappointing, for example in comparison with a recent large-scale GWAS of educational attainment where the predictive power reached 13%25, though it should be noted that the SNP heritability of general risk tolerance is less than that of educational attainment. Nonetheless, it is possible that a PGS with the current predictive power could already be beneficial to strengthen inference in empirical research of risk-related outcomes, discussed next. Rietveld et al. (2013) proposed a framework to evaluate the contribution of a PGS to empirical research26. The basic idea is that a PGS can increase the power of a randomized control trial by decreasing residual variation. Thus, the inclusion of a PGS could offset a reduction in experimental sample size, which can be translated into financial terms. A financial break-even point can be approximated as     , where  is the originally number of study participants,  is the number of study participants in the reduced sample,  is the original cost per study participant, and  is the cost to genotype each of the study participants in the reduced sample. Thus, the left-hand side of the equation represents the cost saved, while the right-hand side represents the additional cost to genotype the remaining study participants. Rietveld et al. (2013) give examples of expensive educational reforms that can amount to more than $10,000 per participant and year to evaluate. In such cases, it is possible that even a PGS with modest predictive accuracy can substantially reduce the study cost. But at what P could our general-risk-tolerance PGS, which explains 1.8% of the outcome variation, be motivated? To illustrate, assume that a prospective randomized control trial 2 intends to include 300 participants (i.e., ) to detect a treatment effect of 5% (R ) on some 6 risk-related outcome. In that case, the theoretical study power is 97.5%. Under these assumptions, calculations show that power can be kept constant with about 295 participants (i.e., ), that is five participants less. Today, it could be reasonable to assume that the cost of genotyping () is 50 per study participantx. In this case, P is equal to 2,950. Thus, our general-risk-tolerance PGS could already be beneficial to some expensive randomized control trials. However, there are also many conceivable studies that are not expensive enough to motivate the inclusion of a PGS based only on financial motives. In such cases, our PGS could be used anyways to increase power and/or control for unobserved heterogeneity. This particular example assumed the inclusion of a single PGS, but once the participants have been genotyped it is easy to include multiple polygenic scores at a low marginal cost. In summary, I argue that our results have the potential to strengthen inference in empirical research, and in addition, can be financially beneficial to particularly expensive experiments of risk taking. In Chapter 2, genetic correlations were estimated between general risk tolerance and about 30 other traits. In summary, strong correlations were identified with many risky lifestyle behaviors, and weak to moderate correlations were identified with several socioeconomic and neuropsychiatric traits, and with personality dimensions. In that chapter, multi-trait analyses with the summary statistics already suggest a benefit of multi-trait analysis. Using MTAG, we leveraged information from the supplementary phenotypes and were able to increase the number of general-risk-tolerance leads SNPs to 312, which jointly explain 0.93% of the phenotypic variation. Thus, almost twice that of the 124 lead SNPs identified in the discovery GWAS. It was also the general-risk-tolerance PGS based on the MTAG results that had the greatest predictive power. Future studies should be to able to use our results to boost power in multi-trait analyses of genetically correlated traits. Such decisions can be guided by the genetic correlations we estimated. Similarly, we estimated strong genetic correlations between subjective well-being, depressive symptoms, and neuroticism with anxiety disorders. There are currently few reported genome-wide associations with anxiety disorders27, and thus, it could be x Gencove. Retrieved September 13, 2018, from https://gencove.com/

149 Chapter 6 beneficial to use our results to aid the identification of genetic associations with that condition. Finally, proxy-phenotype analyses in Chapters 2–3 identified plausible candidate associations with several traits, such as ADHD, lifetime cannabis use, and self-employment, in loci that have not been previously reported for these traits. In conclusion, our results can be leveraged for multi-trait analysis of genetically correlated traits. c. What do the results reveal about biological mechanisms and pathways? We performed bioinformatic analyses of general risk tolerance, subjective well-being, depressive symptoms, and neuroticism with the GWAS summary statistics. Importantly, functional partitioning with stratified LD Score regression found the strongest enrichment for all four traits in the functional category central nervous system. That is encouraging because these phenotypes are foremost expected to be related to brain function. It would be worrying if the strongest signal would have been found in seemingly unrelated pathways and tissues. Also, a weaker, but yet significant level of enrichment was found for general risk tolerance in the category immune/hematopoietic (while excluding the MHC region). To the best of our knowledge, this is one of the first times a study finds significant enrichment for a mental trait in the category immune/hematopoietic, though this has previously been implicated for some mental disorders. Next, the functional category adrenal/pancreas was found enriched for subjective well-being and depressive symptoms, which aligns with previously reported associations between depression and the stress hormone cortisol28, which is produced in the adrenal cortex. Interestingly, the lack of enrichment for general risk tolerance in the category adrenal/pancreas contests previously reported associations with cortisol29. Next, Gene Network analysis strongly suggest the involvement of the following brain regions in general risk tolerance: prefrontal cortex, , visual cortex, parietal lobe, and putamen. The biological pathways that are implicated by that analysis include particular brain- related mechanisms, such as dendritic and synaptic processes. That method, together with DEPICT, mutually implicate the involvement of both glutamate and GABA neurotransmitters. However, neither SMR nor competitive gene-set analysis support that finding. For brevity, the interested reader can find a detailed discussion of this conflicting result in the Supplementary Materials. Notably, few to no other large-scale GWAS of mental traits have reported evidence in favor of the involvement of both glutamate and GABA. In Chapter 3, Gene Network analysis of subjective well-being suggest the involvement of particular brain regions, such as putamen, thalamus, and the visual cortex, but in contrast to risk tolerance, that analysis also ranks many seemingly unrelated tissues among the highest. That result is likely explained by the smaller sample size of the GWAS of subjective well-being, and future studies in larger samples should be able to more accurately pinpoint particular brain regions. Overall, these bioinformatic analyses of GWAS summary statistics are limited by the availability of external reference data, and it may be useful to revisit these analyses in the future as more reference data becomes available. d. What do the results suggest about the biological pathways and candidate genes that have previously been hypothesized to influence risk taking? As discussed in Chapter 1, previous studies of biological pathways and candidate genes in relation to risk taking could be considered hampered by several methodological shortcomings, such as small sample size. Because this is by far the largest genetic study of risk tolerance it provides an excellent opportunity to replicate the previously hypothesized pathways and genes. Previously, five pathways have frequently been hypothesized to influence risk tolerance: testosterone and estrogen, dopamine and serotonin, and cortisol. Competitive gene-set analysis did not find significant enrichment for neither of the five, which is further supported by the results of Gene Network analysis and DEPICT. Next, we investigated 15 previously

150 Discovering the genetic architecture of the mind hypothesized candidate genes, and found that none are among the 285 significant genes identified in gene-based analyses with MAGMA. Thus, a large number of other genes appear to be more important than those that have previously been suggested in the literature. Finally, we investigated whether particular SNPs located within, or that are known to tag, those 15 candidate genes are associated with general risk tolerance. Overall, no such SNPs replicate at genome-wide significance. In summary, our results contest the involvement of the biological pathways and candidate genes that have previously been hypothesized to influence risk taking. 2. Is educational attainment associated with CpG methylation and how does the strength of association compare to biologically proximate factors? In Chapter 4, the EWAS, which included 10,767 individuals, identified that educational attainment is associated with 37 CpG probes at epigenome-wide significance. Nine of those probes remain significant in an adjusted model that controls for BMI and smoking. Thus, when we considered two biologically proximate confounders then the number of associations dropped quite drastically. We also observed that there is much less inflation of the overall test statistic in the adjusted model, which is a sign that these covariates are important to avoid inflation, and thus, possible omitted variable bias across most or all probes. We benchmarked the 50 top probes from the adjusted model in comparison to four biologically proximate factors and found that their effect sizes are many times weaker compared to the strongest effects reported for smoking and maternal smoking, a few times weaker compared to alcohol consumption, and about 50% weaker compared to BMI. In addition, most of the 44 probes with association P value less than 110–5, in the adjusted model, have previously been associated 6 with smoking and/or maternal smoking30,31. Disconcertingly, the results of several robustness analyses suggest that most, if not all, of the identified associations are driven by confounding from smoking even though we controlled for smoking with a covariate. It appears as if that covariate is not detailed enough to fully account for the strong and long-lasting effects from smoking on methylation. Typically, the smoking covariate available in most epidemiological cohorts is coarsely measured, which translates into measurement error, and thus, residual confounding32. Unfortunately, alcohol consumption was not available or consistently measured across the included study cohorts. Therefore, we cannot rule out that alcohol consumption could be responsible for the remaining inflation of the overall test statistic;  is about 1.06 after genomic control (adjusted model). Notwithstanding the disappointing findings, our study contributes by being many times larger than most previous studies in behavioral epigenetics, and it can hopefully increase the awareness of the many issues surrounding confounding from lifestyle factors in observational studies of methylation. What do the results suggest about the hypothesis that psychosocial experiences influence disease via methylation? Overall, our results do not support the hypothesis that a major life experience would be strongly associated with methylation, or at least in a direct manner that cannot be attributed to biologically proximate factors. Thus, it is questionable if psychosocial experiences actually influence disease via methylation. To my knowledge, there are no studies that have investigated that hypothesis with a randomized design in humans33,34, which is an important but potentially infeasible avenue for future research. Thus, before causality has been established, a more parsimonious explanation is that the reported associations between psychosocial experiences and methylation are driven by a range of biological confounders. Notably, several review articles on the subject acknowledge that possibility35–38. Nevertheless, I find it alarming that many studies in behavioral epigenetics brush aside such concerns in favor of more sensational claims, which may lead to a legitimization of bad research practices, solidify the trust in weak results, and add to the overall hype of epigenetic epidemiology33. In my opinion, if the goal of

151 Chapter 6 epigenetic epidemiology is indeed to provide biomarkers and guide health interventions and social policies, which is a frequently voiced motivation34,39,40, then carefulness must be the norm for the execution and interpretation in behavioral epigenetics. Otherwise, there is a risk that resources are misallocated to ineffective medical and social interventions, non-replicable studies, and that clinical trials may experience similar failure rates as those observed in industry-conducted trials based on published findings from cancer research41. On the introductory page of Chapter 4, I quote Carl Sagan on the virtue of keeping an open but not too open mind. I chose that particular quote because it relates to my skepticism towards the notion that “biological embedding of psychosocial stress”34,39,42 would be a strong explanatory factor of health inequalities, rather than positive health behaviors, lifestyle factors, and underlying genetic liability towards multiple of these factors. I speculate that this may be a politically appealing explanation because it implies less blame and shame of the health inequalities across socioeconomic strata43,44. However, good intentions do not motivate bad research practices. Importantly, I am not saying that stress, bad working conditions, and poverty are not major negative health factors6,45,46, I merely question the need to hypothesize about a largely unobservable social stress proxied by higher-order factors such as socioeconomic status, which by design becomes prone to being confounded by unobserved heterogeneity. 3. What is the overall quality and evidential value of the previous studies that have tested antisocial behavior for association with an interaction effect between adverse life events and 5-HTTLPR? None of the eight reviewed studies attained a completely satisfying score in the quality assessment. Importantly, all the studies fail to account for all relevant covariategene and covariateenvironment interaction terms47. Apparently, the studies disagree on what covariates are important in this setting because the included covariates vary a lot. Thus, omitted variable bias could have inflated the strength of association in some of the studies. Further, none of the studies control for population stratification with molecular genetic data and several of the studies analyze samples with admixed ancestry. Therefore, none of the studies can rule out the alternative explanation that other genetic and environmental factors could be attributable. Thus, the quality of the eight studies could be considered weak. Evidently, the multiple sources of bias that we identified severely limit the evidential value of the subset of studies that report a significant interaction. Also, more recent GWAS findings with respect to antisocial behavior48, and our GWAS of the genetically correlated trait general risk tolerance (rg ~ 0.35), do not provide support of a main effect of 5-HTTLPR. The absence of a direct effect is a weaker but yet important indicator that it is unlikely that 5-HTTLPR would be a moderator of the effect of adverse life events. In conclusion, the findings contest the hypothesis that antisocial behavior would be associated with an interaction between adverse life events and 5-HTTLPR. Conclusion

The central purpose of this thesis has been to investigate the genetic architecture of the main phenotypes. Chapters 2–3 found that GWAS in hundreds of thousands to almost 1 million individuals have the ability to identify robust genetic associations with general risk tolerance, adventurousness, and the four risky behaviors and their first PC, as well as with subjective well- being, depressive symptoms, and neuroticism. Many of the identified associations were the first reported for these traits. However, the identified variants explain only a small part of the hiding heritability. Nonetheless, the results uncovered several interesting aspects of their genetic architectures, such as genetic overlap in a couple of inversion polymorphisms, and the thesis has shown that the results can be used to strengthen inference in empirical research and to perform multi-trait analyses of genetically correlated traits. An interesting finding is the novel

152 Discovering the genetic architecture of the mind lead about the possible involvement of glutamatergic and GABAergic neurotransmission in relation to general risk tolerance, and the lack of support for the involvement of the previously hypothesized genes and biological pathways. Chapter 4 found that educational attainment is not strongly associated with methylation, and that several biologically proximate confounders are major concerns for the robustness of observational studies that test whether psychosocial experiences are associated with methylation. Finally, Chapter 5 found that the overall quality is low for the previous studies that have tested antisocial behavior for association with a gene- environment interaction between adverse life events and 5-HTTLPR, and that there is little evidential support in favor of that interaction.

Study limitations

The vast majority of the study participants are of European descent. Thus, it is not certain that the results can be generalized to populations of different ancestry. Overall, larger genetic studies in non-Europeans are warranted49,50. Next, it was possible to amass very large samples because relatively simple self-reported measures were studied. That, in combination with phenotypic heterogeneity across the study cohorts, could have attenuated the power to detect genetic associations. Although some of the studies used repeated measurements most analyses were performed cross-sectionally. Out of the four studies, I deem that a longitudinal study design could have benefited the EWAS the most. In that study, a longitudinal design could have led to more accurate inference of whether associations with CpG probes would have appeared before or after initiation of education as well as smoking initiation. Also, because methylation is 6 dynamic and unstable, repeated measurements could have been used to correct for measurement error. Unfortunately, we could not identify any large-enough EWAS sample with quasi-random variation that would allow us to investigate causality. Furthermore, the GWAS reported in this thesis focused on common and low-frequency autosomal SNPs. Based on the previous literature, I deem it unlikely that there would be a very large number of rare variants responsible for a substantial share of the heritability of the main phenotypes, but based on the studies in this thesis, we simply do not know. Also, it could be that allosomal variation (i.e., on the sex chromosomes) may explain part of the phenotypic variation, such as the observed difference in general risk tolerance between men and women. But we did not investigate variation on the sex chromosomes. Overall, the studies reported in this thesis are particularly focused on genetic variation rather than environment factors, while the heritability estimates strongly suggest that much of the variation in the main phenotypes is attributable to the environment. Thus, the lack of large-scale investigation of environmental factors could be considered a limitation of this thesis. Finally, I acknowledge that this thesis has barely scratched the surface of the genetic iceberg, and there are likely many thousands of loci left to discover in relation to the main phenotypes.

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References – Chapter 6

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156 Summary

General summary

Twin and family studies have convincingly established that most, if not all, human traits are heritable to some degree. Yet, few genetic variants have been discovered that can be attributed for the resemblance in psychology and behavior that is observed in families. Until recently, the available technology restricted the sample size of molecular genetic studies, which limited researchers to be able to detect only highly penetrant variants. At the same time, repeated empirical observations suggested that the genetic influences on genetically complex traits, such as psychology and behavior, are orders of magnitude smaller compared to what was previously thought and distributed across a very large set of genetic variants. Now, modern genotyping technologies are drastically changing the investigation of the molecular genetic basis of complex traits by rapidly decreasing the cost of measuring individual genotypes. This advancement has led to a surge of data generation in epidemiological cohorts and large biobank initiatives and we are finally at a point where samples sizes are large enough to discover the very small genetic effects that influence highly polygenic traits. Because of that development, there is now a cascade of scientific efforts, across fields and disciplines, with the objective to discover the genetic architecture of complex traits and disorders, in some aspects comparable to the space race of the 20th century. Many researchers aspire to put a flag on previously uncharted genetic territory. These efforts are considered to be a potential game changer in the investigation of mental traits, not only for the detection, prevention, and treatment of various mental disorders, but also for how empirical research can be conducted in fields like psychology and the social sciences. In that spirit, this thesis investigates the molecular genetic basis of various mental traits, such as behaviors, moods, and preferences, with the intent to contribute to the first steps towards discovering the genetic architecture of the human mind. This thesis consists of four empirical studies that study a few specific measures of psychology and behavior in relation to a particular type of molecular genetic variation. The objective of the first two empirical chapters is to discover novel associations with single-nucleotide polymorphisms (SNPs), the most occurring form of genetic variation, by performing genome- wide association studies in hundreds of thousands and up to a million individuals. The objective of the third empirical chapter is to discover novel associations with CpG methylation, an epigenetic marker, by performing an epigenome-wide association study. The objective of the fourth empirical chapter is to critically review and meta-analyze the literature on a reported gene-environment interaction. In Chapter 2, we study individual differences in the overall willingness to take risks, which we refer to as general risk tolerance, in combination with self-reported adventurousness and four risky behaviors: automobile speeding, alcohol consumption, smoking initiation, and number of lifetime sexual partners. By studying hundreds of thousands to almost a million individuals we discover hundreds of novel genetic loci associated with these traits. Yet, the loci account for only a small part of the missing heritability. Thus, there are many genetic variants left for future studies to discover as larger samples become available. Nonetheless, the findings reveal several interesting aspects of the traits’ genetic architectures, such as the possible involvement of structural variation, in the form of inversions. We find that most of the associations are located outside of genes, and thus, are likely to be involved in the regulation of gene expression. Also, the study estimates great genetic overlap across general risk tolerance, risky behaviors, and many other traits from various research domains. For general risk tolerance, bioinformatic analyses finds strong enrichment of the central nervous system, and weaker enrichment of the category immune/hematopoietic. The study uncovers evidence in favor of biological mechanisms that were previously not thought to be related to risk taking, in the form of

157 Summary glutamatergic and GABAergic neurotransmission, and casts doubt on the pathways and candidate genes that have previously been hypothesized to influence risk taking. In Chapter 3, we investigate three mood phenotypes: subjective well-being, depressive symptoms, and neuroticism. The three traits are strongly correlated phenotypically and there is a shared genetic basis, which motivates a joint study of the three. Importantly, larger genetic studies of depression have been warranted for a long time. By performing genome-wide association studies in roughly 150,000 to 300,000 individuals we discover a handful of associated loci, which are among the first reported for these traits. Yet, their joint effects are so small that the study barely scratches the surface of their genetic architectures. Nonetheless, the findings are a first step in the discovery of the genetic variants responsible for the missing heritability of depression. We indeed find great genetic overlap across the three traits, as well as with anxiety disorders. Associations with neuroticism tag two inversion polymorphisms, which provides evidence in favor of the premise that structural variants are involved in determining individual differences in mental traits. Bioinformatic analyses finds strong enrichment of the central nervous system for all three traits, and weaker enrichment of the category adrenal/pancreas for subjective well-being and depressive symptoms. In Chapter 4, we investigate whether educational attainment, which is a cognitive trait influenced by the social environment but also a major life experience, is associated with CpG methylation across the genome. Methylation is an epigenetic mechanism that is considered to play a major role in aging and a range of medical conditions. We perform an epigenome-wide association study in almost 11,000 individuals, which is by far the largest study on the association between a social factor and methylation. Disappointingly, we identify only a small number of epigenetic associations, and most do not appear to be robustly associated with educational attainment but instead driven by confounding bias attributable to own and/or maternal smoking. Overall, the overwhelming effect of smoking exposure on methylation appears to be a major source of concern for epigenetic studies of the social environment, because many such factors correlate with smoking to a varying degree. Nonetheless, because of the large sample size our estimates provide an approximate upper bound to the strength of association that can be expected for a biologically distal factor. Importantly, the findings suggest that the association with a social environmental influence, which is experienced over many years early in life, appears to be many times weaker than that of risk factors with a direct biological impact, such as smoking, alcohol consumption, and BMI. In Chapter 5, we critically review and meta-analyze the literature that has hypothesized that antisocial behavior is associated with a gene-environment interaction between adverse life events and the gene region 5-HTTLPR. The number of published studies is not great, but the meta-analysis includes about 8,000 individuals. The study identifies a significant interaction effect. However, a range of methodological shortcomings severely restrict the evidential value in support of that interaction and the possibility to draw a firm conclusion. The major sources of concern are differences in study design and statistical procedures, inadequate statistical modeling of all relevant covariategene and covariateenvironment interaction terms, and the high risk of bias from population stratification because of admixed-ancestry samples and lack of genetic principal components. In addition, a more recent genome-wide association study lends little support for a direct effect of 5-HTTLPR on antisocial behavior, which casts further doubt on the hypothesis that 5-HTTLPR would be involved in the moderation of the effect of adverse life events.

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