DOCSLIB.ORG

Comparison of Genotypic and Phenotypic Correlations: Cheverud's Conjecture in Humans

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Comparison of Genotypic and Phenotypic Correlations: Cheverud's Conjecture in Humans Genetics: Early Online, published on May 8, 2018 as 10.1534/genetics.117.300630 1 Comparison of Genotypic and Phenotypic Correlations: 2 Cheverud’s Conjecture in Humans 3 4 Sebastian M. Sodini*, Kathryn E. Kemper*, Naomi R. Wray*†, Maciej Trzaskowski* 5 6 * Institute for Molecular Bioscience, The University of Queensland, St Lucia, Brisbane, 4072, 7 Australia 8 † Queensland Brain Institute, The University of Queensland, St Lucia, Brisbane, 4072, Australia 9 Copyright 2018. 1 10 Running Title: Cheverud’s Conjecture in Humans 11 12 Keywords: genetic correlation, genetic proxy, LD score regression, morphological non-morphological 13 traits, UK Biobank 14 15 Corresponding Author: Dr. Maciej Trzaskowski, Institute for Molecular Bioscience, 306 Carmody 16 Road, The University of Queensland, St Lucia, 4072, Brisbane, Australia. Phone number: 17 +61733466430; e-mail address: [email protected] 18 19 20 21 Abstract 22 Accurate estimation of genetic correlation requires large sample sizes and access to genetically 23 informative data, which are not always available. Accordingly, phenotypic correlations are often 24 assumed to reflect genotypic correlations in evolutionary biology. Cheverud’s conjecture asserts that 25 the use of phenotypic correlations as proxies for genetic correlations is appropriate. Empirical evidence 26 of the conjecture has been found across plant and animal species, with results suggesting that there is 27 indeed a robust relationship between the two. Here, we investigate the conjecture in human 28 populations, an analysis made possible by recent developments in availability of human genomic data 29 and computing resources. A sample of 108,035 British European individuals from the UK Biobank 30 was split equally into discovery and replication datasets. 17 traits were selected based on sample size, 31 distribution and heritability. Genetic correlations were calculated using linkage disequilibrium score 32 regression applied to the genome-wide association summary statistics of pairs of traits, and compared 33 within and across datasets. Strong and significant correlations were found for the between-dataset 34 comparison, suggesting that the genetic correlations from one independent sample were able to predict 35 the phenotypic correlations from another independent sample within the same population. Designating 36 the selected traits as morphological or non-morphological indicated little difference in correlation. The 37 results of this study support the existence of a relationship between genetic and phenotypic correlations 38 in humans. This finding is of specific interest in anthropological studies, which use measured 39 phenotypic correlations to make inferences about the genetics of ancient human populations. 40 41 Introduction 42 Genetic correlations are a measure of genetic factors shared between two traits. When two traits 43 are highly genetically correlated, the genes that contribute to the traits are usually co-inherited (Lynch 44 & Walsh, 1998). While traditionally used in animal breeding (Lynch & Walsh, 1998), in a broader 2 45 research context, genetic correlations contribute to understanding the development and pathways of 46 traits, population level gene flow and the co-occurrences of traits (Via & Hawthorne, 2005). For this 47 reason, genetic correlations play an important role in evolutionary biology, and estimates of genetic 48 correlations are also used in theoretical modelling of human populations. 49 Genetic correlations (푟) are calculated from the additive genetic variance and covariance 50 between traits, as shown for traits X and Y, 푐표푣푔(푋,푌) 푐표푣푔(푋,푌) 51 푟 = , or for standardized traits where the phenotypic variances are one, 푟 = , where √푉푔푋푉푔푌 2 2 √ℎ푋ℎ푌 2 2 52 ℎ푋and where ℎ푌are the heritability estimates of the two traits and 푉푋and 푉푌are the variances of the 53 traits. 54 Traditionally genetic correlations are calculated from pedigree data using statistical methods to 55 partition phenotypic (co)variance into genetic variance and genetic covariance (Henderson 1986). 56 More recent methods make use of genome-wide single nucleotide polymorphism (SNP) data and the 57 very small coefficients of relationship between very large numbers of unrelated individuals in order to 58 calculate these parameters (Lee et al., 2012). Since only common variants are included in the 59 calculations, this approach assumes that the genetic correlation is the same across the allelic frequency 60 spectrum. Accepting this caveat as reasonable, the approach has an advantage over the traditional 61 methods, as unrelated individuals are less likely to have had exposure to similar environmental effects, 62 reducing confounding from shared environment. Additionally, as genotyping becomes cheaper, 63 genome-wide SNP data is becoming more readily and widely available than pedigree data. Moreover, 64 unbiased estimates of genetic correlations are achievable with minimal computing resources from 65 analysis of summary statistics from genome-wide association studies via the LD-score regression 66 method (Bulik-Sullivan et al., 2015a; Ni et al., 2017). 3 67 The sampling variance of a genetic correlation estimate, depends on, and is larger than, the 68 sampling variances of the concurrently estimated heritabilities (Robertson, 1959; Visscher et al., 69 2014). Hence, large sample sizes are needed to estimate genetic correlations with accuracy. James 70 Cheverud proposed in 1988 that phenotypic correlations (푟푝) could be used as a proxy for genetic 71 correlations (Cheverud, 1988). 72 Whilst there has been criticism of the conjecture, most notably by Willis et al. (1991), 73 subsequent studies in various organisms have provided much empirical evidence and theory supporting 74 the conclusion. Roff (1996) considered a variety of traits from previously published datasets. This 75 investigation showed that the relationship between the two correlations was most concordant in 76 morphological traits, as opposed to behavioral or life history traits. In addition, while the average 77 absolute disparity (Dabs = |rp-rg| (Willis et al., 1991)) between the correlations was relatively high (0.24- 78 0.46), this difference could be attributed to the sampling error of rg. In 2008, Kruuk et al. repeated the 79 analysis of Roff’s 1996 paper with more recent data with an increased sample size, reaching similar 80 conclusions. 81 The suitability of using phenotypic correlations as a proxy for genetic ones in various traits has 82 been discussed by Hadfield et al. (2007), concluding that while the conjecture may be true in traits 83 with high heritability, particularly those related to growth, there are still exceptions, and the conjecture 84 most likely does not apply to all traits generally. Since phenotypic correlations depend both on the 85 correlation of additive genetic and on the correlation of environmental effects (with the term 86 environmental representing any effects that are not additive genetic), differences between phenotypic 87 and genetic correlations must be explained by the relationship between genetic and environmental 88 effects. Cheverud (1984) suggests that most environmental effects often act in the same direction and 89 through the same pathways as genetic effects, which leads to a similarity between phenotypic and 90 genetic correlations. Hadfield (2007), on the other hand, suggested that certain traits have 4 91 environmental effects that act in the opposite direction to the genetic effects, which could reflect the 92 conclusion of Roff (1996), who found lower correlation for life history and behavioral traits than 93 morphological ones. 94 Despite the possible deficiencies of the conjecture when applied to non-morphological traits, 95 behavioral researchers often assume that correlations between behaviors can give insight into the 96 genetics behind the behavior. In order to test this assumption, Dochtermann (2011) tested the 97 relationship between published behavioral genetic and phenotypic correlations from animal studies. 98 The author found that while the correlation between the phenotypic and genetic correlations was high 99 (r=0.86), the mean absolute difference between traits was also high (0.27), suggesting that phenotypic 100 correlations were not a good predictor of genetic correlations between behavioral traits. Dochtermann 101 found that while not a good predictor, the phenotypic correlation is able to reliably provide information 102 on the direction of the genetic effect for behavioral research, still allowing to make certain genetic 103 conclusions based on phenotypic data. 104 To date, studies investigating the existence of Cheverud’s conjecture in specific populations 105 have looked at morphological traits in insects (Roff, 1995; Reusch & Blanckenhorn, 1998), tamarins 106 (Ackermann & Cheverud, 2002), and plants (Waitt & Levin, 1998) with results corroborating the 107 findings of Cheverud and Roff. While the conjecture has not been investigated in humans, it has been 108 applied in human modelling. As genetic data are not directly accessible in many ancient human 109 populations, phenotypic traits have been used to make conclusions regarding genetic information 110 (Weaver et al., 2007; Relethford & Blangero, 1990). 111 While the proportionality of phenotypic and genetic correlations has been assumed to be true in 112 human populations, there has yet to be a study to investigate the conjecture in the context of humans. 113 This study aims to fill the gap in understanding how the conjecture
Load more

Recommended publications
  • Cognitive Ability and Education: How Behavioural Genetic Research Has Advanced Our Knowledge and Understanding of Their Association
    Cognitive ability and education: how behavioural genetic research has advanced our knowledge and understanding of their association Margherita Malanchini1,2,3, Kaili Rimfeld2, Andrea G. Allegrini2, Stuart J. Ritchie2, & Robert Plomin2 1 Department of Biological and Experimental Psychology, Queen Mary University of London, United Kingdom. 2 Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, United Kingdom. 3 Population Research Center, The University of Texas at Austin, United States. Correspondence: Margherita Malanchini, Department of Biological and Experimental Psychology, Queen Mary University of London, Office 2.02, G.E. Fogg Building, Mile End Road, London E1 4NS. E-mail: [email protected] Abstract Cognitive ability and educational success predict positive outcomes across the lifespan, from higher earnings to better health and longevity. The shared positive outcomes associated with cognitive ability and education are emblematic of the strong interconnections between them. Part of the observed associations between cognitive ability and education, as well as their links with wealth, morbidity and mortality, are rooted in genetic variation. The current review evaluates the contribution of decades of behavioural genetic research to our knowledge and understanding of the biological and environmental basis of the association between cognitive ability and education. The evidence reviewed points to a strong genetic basis in their association, observed from middle childhood to old age, which is amplified by environmental experiences. In addition, the strong stability and heritability of educational success are not driven entirely by cognitive ability. This highlights the contribution of other educationally relevant noncognitive characteristics. Considering both cognitive and noncognitive skills as well as their biological and environmental underpinnings will be fundamental in moving towards a comprehensive, evidence-based model of education.
    [Show full text]
  • Is There a Genetic Correlation Between General Factors of Intelligence and Personality?
    Twin Research and Human Genetics Volume 18 Number 3 pp. 234–242 C The Author(s) 2015 doi:10.1017/thg.2015.28 Is There a Genetic Correlation Between General Factors of Intelligence and Personality? John C. Loehlin,1 Meike Bartels,2 Dorret I. Boomsma,2 Denis Bratko,3 Nicholas G. Martin,4 Robert C. Nichols,5 and Margaret J. Wright4 1Psychology Department, University of Texas, Austin, Texas, USA 2Department of Biological Psychology, VU University, Amsterdam, the Netherlands 3Department of Psychology, Faculty of Social Sciences and Humanities, University of Zagreb, Zagreb, Croatia 4QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia 5Jamul, CA, USA We tested a hypothesis that there is no genetic correlation between general factors of intelligence and personality, despite both having been selected for in human evolution. This was done using twin samples from Australia, the United States, the Netherlands, Great Britain, and Croatia, comprising altogether 1,748 monozygotic and 1,329 same-sex dizygotic twin pairs. Although parameters in the model-fitting differed among the twin samples, the genetic correlation between the two general factors could be set to zero, with a better fit if the U.S. sample was excepted. Keywords: general factor of intelligence, general factor of personality, heritability, mutation-selection balance, life history theory, twins The concept of hierarchical structure in the domains of in- artifact (Backstr¨ om¨ et al. 2009; Pettersson et al., 2012)or telligence and personality, featuring a single general factor declared it to be psychometrically weak (Revelle & Wilt, at the top, have been controversial ever since general fac- 2013). However, in one study, partialing out self-esteem or tors were advocated early in the 20th century by Spearman social desirability measures did not greatly reduce a GFP (1904)andWebb(1915).
    [Show full text]
  • An Atlas of Genetic Correlations Across Human Diseases and Traits
    An Atlas of Genetic Correlations across Human Diseases and Traits The Harvard community has made this article openly available. Please share how this access benefits you. Your story matters Citation Bulik-Sullivan, B., H. K. Finucane, V. Anttila, A. Gusev, F. R. Day, P. Loh, L. Duncan, et al. 2015. “An Atlas of Genetic Correlations across Human Diseases and Traits.” Nature genetics 47 (11): 1236-1241. doi:10.1038/ng.3406. http://dx.doi.org/10.1038/ng.3406. Published Version doi:10.1038/ng.3406 Citable link http://nrs.harvard.edu/urn-3:HUL.InstRepos:27320469 Terms of Use This article was downloaded from Harvard University’s DASH repository, and is made available under the terms and conditions applicable to Other Posted Material, as set forth at http:// nrs.harvard.edu/urn-3:HUL.InstRepos:dash.current.terms-of- use#LAA HHS Public Access Author manuscript Author Manuscript Author ManuscriptNat Genet Author Manuscript. Author manuscript; Author Manuscript available in PMC 2016 May 01. Published in final edited form as: Nat Genet. 2015 November ; 47(11): 1236–1241. doi:10.1038/ng.3406. An Atlas of Genetic Correlations across Human Diseases and Traits Brendan Bulik-Sullivan1,2,3,*, Hilary K Finucane4,*, Verneri Anttila1,2,3, Alexander Gusev5,6, Felix R. Day7, Po-Ru Loh1,5, ReproGen Consortium8, Psychiatric Genomics Consortium8, Genetic Consortium for Anorexia Nervosa of the Wellcome Trust Case Control Consortium 38, Laramie Duncan1,2,3, John R.B. Perry7, Nick Patterson1, Elise B. Robinson1,2,3, Mark J. Daly1,2,3, Alkes L. Price1,5,6,**, and Benjamin M.
    [Show full text]
  • Heritability Estimates and Genetic Correlation for Growth Traits and LCDV Susceptibility in Gilthead Sea Bream (Sparus Aurata)
    fishes Article Heritability Estimates and Genetic Correlation for Growth Traits and LCDV Susceptibility in Gilthead Sea Bream (Sparus aurata) Carlos Carballo 1,2, Hyun Suk Shin 3, Concepción Berbel 1, Maria Jesús Zamorano 3, Juan Jose Borrego 2 , Eva Armero 4, Juan Manuel Afonso 3 and Manuel Manchado 1,5,* 1 IFAPA Centro El Toruño, Junta de Andalucía, Camino Tiro Pichón s/n, 11500 El Puerto de Santa María, Cádiz, Spain; [email protected] (C.C.); [email protected] (C.B.) 2 Departamento de Microbiología, Campus de teatinos, Universidad de Málaga, 29071 Málaga, Spain; [email protected] 3 Aquaculture Research Group (GIA), IU-ECOAQUA, Universidad de Las Palmas de Gran Canaria, Crta. Taliarte s/n, 35214 Telde, Spain; [email protected] (H.S.S.); [email protected] (M.J.Z.); [email protected] (J.M.A.) 4 Animal Production group, Universidad Politecnica de Cartagena, Paseo Alfonso XIII, 48, 30203 Cartagena, Spain; [email protected] 5 “Crecimiento Azul”, Centro IFAPA El Toruño, Unidad Asociada al CSIC”, 11500 El Puerto de Santa María, Cádiz, Spain * Correspondence: [email protected]; Tel.: +34-671-532-088 Received: 26 October 2019; Accepted: 19 December 2019; Published: 25 December 2019 Abstract: The lymphocystis disease (LCD) is a viral infection with a high economic impact in gilthead sea bream aquaculture. In this study, genetic estimates associated with lymphocystis disease virus (LCDV) disease susceptibility and growth were determined in sea bream juveniles. Two fish batches (named batch 1 and batch 2) were built from mass spawning and reared under industrial conditions until disease outbreak.
    [Show full text]
  • Clinical Use of Current Polygenic Risk Scores May Exacerbate Health Disparities
    PERSPECTIVE https://doi.org/10.1038/s41588-019-0379-x Clinical use of current polygenic risk scores may exacerbate health disparities Alicia R. Martin 1,2,3*, Masahiro Kanai 1,2,3,4,5, Yoichiro Kamatani 5,6, Yukinori Okada 5,7,8, Benjamin M. Neale 1,2,3 and Mark J. Daly 1,2,3,9 Polygenic risk scores (PRS) are poised to improve biomedical outcomes via precision medicine. However, the major ethical and scientific challenge surrounding clinical implementation of PRS is that those available today are several times more accurate in individuals of European ancestry than other ancestries. This disparity is an inescapable consequence of Eurocentric biases in genome-wide association studies, thus highlighting that—unlike clinical biomarkers and prescription drugs, which may indi- vidually work better in some populations but do not ubiquitously perform far better in European populations—clinical uses of PRS today would systematically afford greater improvement for European-descent populations. Early diversifying efforts show promise in leveling this vast imbalance, even when non-European sample sizes are considerably smaller than the largest studies to date. To realize the full and equitable potential of PRS, greater diversity must be prioritized in genetic studies, and summary statistics must be publically disseminated to ensure that health disparities are not increased for those individuals already most underserved. RS, which predict complex traits on the basis of genetic All studies to date using well-powered genome-wide association data, are of burgeoning interest to the clinical community, as studies (GWAS) to assess the predictive value of PRS across a range Presearchers demonstrate their growing power to improve clini- of traits and populations have made a consistent observation: PRS cal care, genetic studies of a wide range of phenotypes increase in predict individual risk far more accurately in Europeans than non- size and power, and genotyping costs plummet to less than US$50.
    [Show full text]
  • Two-Trait Selection and the Genetic Correlation II. * Changes in the Genetic Correlation During Two-Trait Selection
    Aust. J. bioi. Sci., 1974, 27, 89-101 Two-trait Selection and the Genetic Correlation II. * Changes in the Genetic Correlation During Two-trait Selection A. K. SheridanA,B and J. S. F. BarkerA A Department of Animal Husbandry, University of Sydney, Sydney, N.S.W. 2006. B Present address: Poultry Research Station, Seven Hills, N.S.W. 2147. With an Appendix by J. W. James Abstract Although there is little experimental information on the effect of simultaneous selection for two quantitative characters on the magnitude of the genetic correlation between them, it is apparently generally expected that such selection for the two characters in the same direction will cause a negative change in the genetic correlation, and selection in opposite directions a positive change. Selection using independent culling levels was done for each of the four combinations of high or low third coxal bristle number with high or low sternopleural bristle number in Drosophila melanogaster for 22 generations. To estimate changes in the genetic correlation, realized genetic parameters were estimated from single-trait lines started from the base population, and from the two-trait lines after 10 and 22 generations of selection. Changes in the genetic correlation in individual two-trait selection lines were variable and unpredictable. At generation 22 concurrent two-trait selection had resulted in significantly larger realized genetic correlations than divergent two-trait selection, so that results were contrary to the generally accepted expectation. Introduction There is very little experimental information on the effect of simultaneous selection for two quantitative characters on the magnitude of the genetic correlation between them.
    [Show full text]
  • Genetics and Personality Inventories: the Limits of Replication with Twin Data
    Behavior Genetics, Vol. 8, No. 4, 1978 Genetics and Personality Inventories: The Limits of Replication with Twin Data Gregory Carey, 1 H. Hill Goldsmith, x Auke Tellegen, ~ and Irving I. Gottesma# Received 18 Aug. 1977--Fina110 Feb. 1978 The consistency of twin data with personality questionnaires is examined using all reported twin samples which have been administered the California Psychological Inventory. The scale correlations for the M Z twins are fairly consistent across different samples while the correlations for DZ twins fail to show as much consistency. Differences, moreover, between M Z and DZ correlations fail to replicate across samples. Sampling error and sampling bias are proposed as the major reasons for the inconsistency, and when these factors are taken into account the resulting heritabilities suggest that the CP1 scales loading on the extraversion-introversion factor are the most heritable. The implications of sampling error and sampling bias for estimat- ing genetic parameters from correlational twin data, for uncovering dif- ferential heritability of personality traits, and for designing future research are discussed. KEY WORDS: twins; personality; heritability; genetics; California Psychological Inventory; extraversion; introversion; replication. INTRODUCTION A close examination of the twin literature on genetic aspects of personality traits reveals controversial interpretations of the data. Thompson and Wilde (1973) reviewed much of the literature and reported that heritabilities for This work was supported in part by a Mental Health Training Grant in Biological Sciences (MH10679) awarded to Sheldon Reed and Irving I. Gottesman and by a grant from the University Computer Center of the University of Minnesota, Minneapolis. 1 Department of Psychology, University of Minnesota, Minneapolis, Minnesota 55455.
    [Show full text]
  • Supplement(Text/Figures)
    SUPPLEMENTARY INFORMATIONARTICLES https://doi.org/10.1038/s41562-019-0566-x In the format provided by the authors and unedited. Genomic structural equation modelling provides insights into the multivariate genetic architecture of complex traits Andrew D. Grotzinger 1*, Mijke Rhemtulla2, Ronald de Vlaming 3,4, Stuart J. Ritchie5,6, Travis T. Mallard1, W. David Hill5,6, Hill F. Ip 7, Riccardo E. Marioni5,8, Andrew M. McIntosh 5,9, Ian J. Deary5,6, Philipp D. Koellinger3,4, K. Paige Harden1,10, Michel G. Nivard 7,11 and Elliot M. Tucker-Drob1,10,11 1Department of Psychology, University of Texas at Austin, Austin, TX, USA. 2Department of Psychology, University of California, Davis, Davis, CA, USA. 3Department of Economics, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands. 4Erasmus University Rotterdam Institute for Behavior and Biology, Rotterdam, The Netherlands. 5Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, UK. 6Department of Psychology, University of Edinburgh, Edinburgh, UK. 7Department of Biological Psychology, Vrije Universiteit University Amsterdam, Amsterdam, The Netherlands. 8Centre for Genomic and Experimental Medicine, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, UK. 9Division of Psychiatry, University of Edinburgh, Edinburgh, UK. 10Population Research Center, University of Texas at Austin, Austin, TX, USA. 11These authors jointly supervised this work: Michel G. Nivard, Elliot M. Tucker-Drob. *e-mail: [email protected] NatURE HUMAN BehaviOUR | www.nature.com/nathumbehav Genomic SEM 32 December 19, 2018 Online Supplement: Genomic SEM Provides Insights into the Multivariate Genetic Architecture of Complex Traits Supplementary Methods MTAG Moment Conditions. Here we examine the connection between the MTAG model and Genomic SEM.
    [Show full text]
  • Genetic Specificity of Linguistic Heritability
    Genetic Specificity of Linguistic Heritability Karin Stromswold Rutgers University – New Brunswick TWIN STUDIES OF LANGUAGE* The logic of twin studies The most common method used to study the role of genetic factors in development is to determine whether monozygotic (MZ) cotwins are linguistically more similar to one another than dizygotic (DZ) cotwins. Because MZ and DZ cotwins share essentially the same pre- and postnatal environment, whereas MZ cotwins share 100% of their DNA and DZ cotwins share only 50% of their DNA, if MZ cotwins are linguistically more similar than DZ cotwins, this suggests that genetic factors play a role in language. If, on the other hand, MZ cotwins are no more similar to one another than DZ cotwins, this suggests that genetic factors play a negligible role for language. Putting aside the possibility of interactions and correlations between genetic and environmental factors, the variation in linguistic abilities in a population (the phenotypic variance) is due to genetic variance plus environmental variance. Heritability is a measure of the proportion of the phenotypic variance that is due to genetic variance. In twin studies, environmental factors that * Portions of this work were supported by grants from the Bamford-Lahey Children’s Foundation, the Busch Biomedical Research Foundation, the National Science Foundation (BCS-9875168, BCS-0042561, BCS-0124095), and an unrestricted research grant from Rutgers University. I thank participants at the Max Planck Institute in Nijmegen’s Four Corners Psycholinguistic Workshop for their helpful comments and suggestions. 2 KARIN STROMSWOLD may contribute to phenotypic variance are divided into those environmental factors that co-twins do and do not share.
    [Show full text]
  • Do Molecular Markers Inform About Pleiotropy?
    HIGHLIGHTED ARTICLE GENETICS | COMMUNICATIONS Do Molecular Markers Inform About Pleiotropy? Daniel Gianola,*,†,‡,1 Gustavo de los Campos,§ Miguel A. Toro,** Hugo Naya,†† Chris-Carolin Schön,†,‡ and Daniel Sorensen‡‡ *Departments of Animal Sciences, Dairy Science, and Biostatistics and Medical Informatics, University of Wisconsin–Madison, Wisconsin 53706, †Department of Plant Sciences Technical University of Munich, Center for Life and Food Sciences, D-85354 Freising-Weihenstephan, Germany, ‡Institute of Advanced Study, Technical University of Munich, D-85748 Garching, Germany, §Department of Biostatistics, Michigan State University, East Lansing, Michigan 48824, **Escuela Técnica Superior de Ingenieros Agrónomos, Universidad Politécnica de Madrid, 20840 Madrid, Spain, ††Institut Pasteur de Montevideo, Mataojo 2020, Montevideo 11400, Uruguay, and ‡‡Department of Molecular Biology and Genetics, Aarhus University, DK-8000 Aarhus C, Denmark ABSTRACT The availability of dense panels of common single-nucleotide polymorphisms and sequence variants has facilitated the study of statistical features of the genetic architecture of complex traits and diseases via whole-genome regressions (WGRs). At the onset, traits were analyzed trait by trait, but recently, WGRs have been extended for analysis of several traits jointly. The expectation is that such an approach would offer insight into mechanisms that cause trait associations, such as pleiotropy. We demonstrate that correlation parameters inferred using markers can give a distorted picture of the genetic correlation between traits. In the absence of knowledge of linkage disequilibrium relationships between quantitative or disease trait loci and markers, speculating about genetic correlation and its causes (e.g., pleiotropy) using genomic data is conjectural. KEYWORDS genetic correlation; genomic correlation; genomic heritability; linkage disequilibrium; pleiotropy HE interindividual differences for a trait or disease risk 1996; Lynch and Ritland 1999).
    [Show full text]
  • Genomic Structural Equation Modelling
    VU Research Portal Genomic structural equation modelling provides insights into the multivariate genetic architecture of complex traits Grotzinger, Andrew D.; Rhemtulla, Mijke; de Vlaming, Ronald; Ritchie, Stuart J.; Mallard, Travis T.; Hill, W. David; Ip, Hill F.; Marioni, Riccardo E.; McIntosh, Andrew M.; Deary, Ian J.; Koellinger, Philipp D.; Harden, K. Paige; Nivard, Michel G.; Tucker-Drob, Elliot M. published in Nature Human Behaviour 2019 DOI (link to publisher) 10.1038/s41562-019-0566-x document version Publisher's PDF, also known as Version of record document license Article 25fa Dutch Copyright Act Link to publication in VU Research Portal citation for published version (APA) Grotzinger, A. D., Rhemtulla, M., de Vlaming, R., Ritchie, S. J., Mallard, T. T., Hill, W. D., Ip, H. F., Marioni, R. E., McIntosh, A. M., Deary, I. J., Koellinger, P. D., Harden, K. P., Nivard, M. G., & Tucker-Drob, E. M. (2019). Genomic structural equation modelling provides insights into the multivariate genetic architecture of complex traits. Nature Human Behaviour, 3(5), 513-525. https://doi.org/10.1038/s41562-019-0566-x General rights Copyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights. • Users may download and print one copy of any publication from the public portal for the purpose of private study or research. • You may not further distribute the material or use it for any profit-making activity or commercial gain • You may freely distribute the URL identifying the publication in the public portal ? Take down policy If you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediately and investigate your claim.
    [Show full text]
  • Why Are Education, Socioeconomic Position and Intelligence Genetically Correlated?
    bioRxiv preprint doi: https://doi.org/10.1101/630426; this version posted May 7, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license. Why are education, socioeconomic position and intelligence genetically correlated? Tim T Morris ab†, Neil M Davies ab, Gibran Hemani ab, George Davey Smith ab a Medical Research Council Integrative Epidemiology Unit, University of Bristol, BS8 2BN, United Kingdom. b Bristol Medical School, University of Bristol, Oakfield House, Oakfield Grove, Bristol, BS8 2BN, United Kingdom. † Corresponding author: Tim Morris. Email: [email protected] Key words: Genetics, social class, heritability, confounding, ALSPAC. 1 bioRxiv preprint doi: https://doi.org/10.1101/630426; this version posted May 7, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license. Abstract Genetic associations and correlations are perceived as confirmation that genotype influences one or more phenotypes respectively. However, genetic correlations can arise from non-biological or indirect mechanisms including population stratification, dynastic effects, and assortative mating. In this paper, we outline these mechanisms and demonstrate available tools and analytic methods that can be used to assess their presence in estimates of genetic correlations and genetic associations. Using educational attainment and parental socioeconomic position data as an exemplar, we demonstrate that both heritability and genetic correlation estimates may be inflated by these indirect mechanisms.
    [Show full text]