DOCSLIB.ORG

Leveraging Genetic Association Data to Investigate the Polygenic Architecture of Human Traits and Diseases

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Leveraging Genetic Association Data to Investigate the Polygenic Architecture of Human Traits and Diseases Leveraging genetic association data to investigate the polygenic architecture of human traits and diseases The Harvard community has made this article openly available. Please share how this access benefits you. Your story matters Citation Chan, Ying Leong. 2014. Leveraging genetic association data to investigate the polygenic architecture of human traits and diseases. Doctoral dissertation, Harvard University. Citable link http://nrs.harvard.edu/urn-3:HUL.InstRepos:12274191 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 Leveraging genetic association data to investigate the polygenic architecture of human traits and diseases A dissertation presented by Ying Leong Chan to The Division of Medical Sciences in partial fulfillment of the requirements for the degree of Doctor of Philosophy in the subject of Genetics and Genomics Harvard University Cambridge, Massachusetts April 2014 © 2014 Ying Leong Chan All rights reserved. Dissertation Advisor: Professor Joel N. Hirschhorn Ying Leong Chan Leveraging genetic association data to investigate the polygenic architecture of human traits and diseases ABSTRACT Many human traits and diseases have a polygenic architecture, where phenotype is partially determined by variation in many genes. These complex traits or diseases can be highly heritable and genome-wide association studies (GWAS) have been relatively successful in the identification of associated variants. However, these variants typically do not account for most of the heritability and thus, the genetic architecture remains uncertain. This dissertation describes analytical approaches to look for evidence of models of genetic architecture that could explain the remaining heritability. We develop methods to make predictions under various models, and compare the expected results from these predictions against the observed data for several traits and diseases. First, in studies of height (a classical polygenic trait), we modeled the expected cumulative effect of common variants identified from GWAS and compared the model with empirical data in individuals from the tails of the height distribution. We found that these common variants are predictive of stature, but have less than expected effects specifically at the short end of the height distribution. This result is consistent with models where rare variants with moderate effect, influence stature only in the shortest individuals. Second, we showed that under genetic models where low frequency variants make iii polygenic contributions to disease, there will be an excess of low frequency risk-increasing variants detected in GWAS. As such, by comparing the number of detected risk-increasing to risk-decreasing variants, one can detect a signal of the contribution to polygenic inheritance from low frequency variants. Finally, we examine the genetic architecture of sitting height ratio (SHR), a measure of body proportion that varies dramatically between individuals of African and European ancestry. We find that the SHR difference between populations is largely due to polygenic architecture; there is no evidence for any major locus accounting for most of this difference. These results show that, with the appropriate computational and genetic models, one can use empirical results of genetics studies to make inferences regarding genetic architecture of human traits and diseases. Doing so can help investigators prioritize strategies for uncovering the remaining unexplained heritability. iv TABLE OF CONTENTS Abstract iii Dedication vi Acknowledgements vii Attributions xi Chapter 1: Introduction 1 A Preamble 2 Heritability of complex traits 7 Methods for studying the genetics of complex traits 12 Complex phenotypes 21 Heritability of human traits 18 Accumulating evidence from multiple studies 25 Summary 29 Chapter 2: Common variants show predicted polygenic effects on height in the tails of 38 the distribution, except in extremely short individuals Chapter 3: An excess of risk-increasing low frequency variants can be a signal of 85 polygenic inheritance in complex diseases Chapter 4: Genome wide association in European and African Americans discover novel 136 loci associated with sitting height ratio Chapter 5: Concluding remarks 169 Overview 170 Major findings and implications 170 Future Directions 174 A Postscript 178 v DEDICATION I dedicate this thesis to my loving wife, Teng Ting (Elaine) Lim. You are the person that has always been there during difficult and trying times. We share and do almost everything together. I would not be the person I am today without your love and support. Elaine, I dedicate this thesis to you. vi ACKNOWLEGEMENTS When I first arrived on the Harvard Medical School campus, I was captivated by the breath and majesty of just being there. Besides the Victorian building design of the Quad buildings, the place was surrounded by many hospitals and people plus that the sound of sirens wailing from ambulances made the whole area a really busy place. It was recruitment weekend for new students and part of the program was to have a couple of faculty members give talks to the potential incoming students and there was where I met my eventual dissertation advisor, Joel Hirschhorn. It was March of 2009. I rotated in the Hirschhorn lab for the summer of that year and eventually joined the lab as a student the next year. Joel was extremely helpful and encouraging mentor throughout graduate school. We (members of the lab) meet regularly with him, at least once in two weeks (30 to 60 minutes) despite his extremely busy schedule and we will always get his full undivided attention during each session. Also, during lab meetings, he will always be there to give critical feedback and suggestions when you present your work and ideas, whether it is about possible experiments to perform or just feedback on giving the presentation itself. Furthermore, he sometimes performs his own analysis, contrary to the long held belief that “PIs don’t do experiments themselves”. To me, it is a privilege to have the opportunity to be his student as well as a member of his lab. I remember when I first joined the lab, I was given the task of “coming up with 10 ideas” by Joel. I only did 8 and after long discussions about each of the aims, 1 of them eventually became one of my thesis aims with the help of another member of the lab, Andrew Dauber. My time in graduate school would not be the same without Andrew. Andrew started as a fellow in the lab the same time when I started my rotation so he has been in the lab for about a year when I vii eventually joined. It was during one of the lab meetings that Andrew presented some genotyping data on height extremes (very short and tall individuals) that could answer one of the 8 ideas that I had initially. Andrew was very kind and helpful and we worked together to answer the question. I will always be grateful to Andrew for getting me started as well as his mentorship throughout my time in the lab. Rany Salem, a post doctoral fellow in the lab, is someone I would also like to specially mention. Rany started as a post doctoral fellow the same day I started my rotation in the lab. Although, he is based at the Broad Institute, he comes to Children’s (Boston Children’s Hospital) frequently and we would always get coffee and exchange ideas. His work on diabetic nephropathy allowed me to develop my next idea, using the summary statistics generated from that project. Besides that, his efforts to obtain genotype data from many different cohorts allowed me and others to explore other ideas with regards to complex traits. In general, members of the Hirschhorn lab are a very sociable bunch which is odd, considering that most of us are ‘computational’ people that do not have a reputation for being sociable. To illustrate this, a former student of the lab, who is now a post doctoral fellow, Charleston Chiang, frequently organizes “games-night” at his place (about once a month) where he invites fellow members of the lab to hang out and play board games. One of our favorite games is called “Betrayal”, which is a game about a group of adventurers exploring a haunted house and one of the members will become the “traitor” midway in the game. That was fun and I will always remember those good times. Another example worth mentioning is our regular sashimi buffets. Somehow, there is a sizeable number of people in our lab that just love gorging on raw-fish, me included. We started out at a reasonably priced restaurant called Yamato somewhere in Allston where they have an all-you-can-eat buffet for just thirty dollars. However, viii when Tonu Esko joined us midway during my time in the lab, he “discovered” a new place, called Takusan where it is cheaper and serves oysters as well. Takusan is now our regular hangout until we find a new place. Therefore, I would like to take the opportunity to thank other members of the lab, past and present for creating the wonderful lab environment that is conducive for sharing ideas and establishing collaboration. Thanks to Tune Pers for providing opportunity to work together on DEPICT. Thanks to Sophie Wang for the help in performing the forward simulations for our 2nd project as well as for free ice-cream. Thanks to Sailaja Vedamtam for pointing me to necessary files when I need them as well as for the wonderful vegetarian dinner that you make. Thanks to Tonu Esko for having the opportunity to work with the Estonian data as well as introducing Takusan Sushi. Thanks to Michael Guo for coffee in exchange for performing LD calculations. Thanks to Yan Meng for discussions about finance and investments.
Load more

Recommended publications
  • K-Model – an Evolutionary Algorithm with New Schema of Representation
    K-MODEL – AN EVOLUTIONARY ALGORITHM WITH NEW SCHEMA OF REPRESENTATION Halina Kwasnicka Department of Computer Science, Wroclaw University of Technology Wyspianskiego 27, 50-370 Wroclaw, Poland tel. (48 71) 3202397, fax: (48 71)211018, e-mail: [email protected], http://www.ci.pwr.wroc.pl/~kwasnick ABSTRACT The paper presents an evolutionary model with pleiotropy and polygene effects, named K-Model. Genes are integer numbers and the linear dependence between phenes and genes are assumed. The redundant genes, i.e., not active ones during some period of evolution, are also included. Such representation allows the evolutionary algorithm to escape from local optima (evolutionary traps) and evolve quicker especially for multi-modal and multi-variable fitness functions. INTRODUCTION From centuries people learn observing natural processes. Usually natural processes are perceived as very skilful, and researchers willingly imitate them to obtain effective techniques. Optimisation techniques called Genetic Algorithms (GAs), or more generally, Evolutionary Algorithms (EAs), base on biological evolution. They imitate the Darwinian paradigm survival the fittest, and use a vocabulary borrowed from genetics. GAs search large spaces efficiently without complete knowledge of an objective function. They require only a limited number of values of objective function to guide their search process. Operators analogical to genetic ones as crossover and mutation are used for achieving a new area in the search space (new solutions). GAs act on the set of coded potential solutions (called chromosomes or individuals) and use the strategy of natural selection to achieve their aims. Proposed by J. Holland genetic algorithms, which we call here as Classic Genetic Algorithm (CGA) uses a binary alphabet for defining chromosomes.
    [Show full text]
  • (Mather, 1949), and Otherwise Appear to Be Inherited in the Classical Mendelian Fashion
    430 NOTES AND COMMENTS ADEFINITION AND STANDARD NOMENCLATURE FOR "POLYGENIC LOCI" JAMES N. THOMPSON, Jr. and J. M. THODAY Department of Genetics, University of Cambridge, Milton Road, Cambridge CM I Xl-! Received3.vi.74 SUMMARY Studies of qualitative and quantitative or continuous genetic variation are, in part, historically and methodologically separate. But the traditional distinction between major genes and polygenes has unfortunately caused some confusion in discussions of the location of polygenes and the nature of the loci which contribute to quantitative variation. Consequently, this paper proposes that the term "polygeniclocus" replace the term "polygene" when reference is made to the effects of individual loci. A polygenic locus is defined as a genetic locus composed of one or more closely linked genes at which allelic substitutions contribute to the variance in a specified quantitative character. A standard nomenclature is described. 1. INTRODUCTION A CHARACTER shows quantitative or continuous variation when the pheno- type is affected by a number of factors, each of which makes a contribution which is small relative to other sources of variation. When these factors include allelic variation at one or more genetic loci, the other sources of variation, which may be environmental, genetic, or both, make it difficult to classify individual genotypes. Thus, segregation at individual loei cannot be studied directly. On the other hand, a character shows qualitative, discontinuous, or Mendelian variation when genetic variation at one or a very small number of loci makes large individual contributions to pheno- typic variance relative to other genetic and environmental factors. In such a situation, variation in the character allows individuals to be classified into distinct phenotypic classes with the result that the segregation of individual alleles can be easily detected.
    [Show full text]
  • Chapter 15 the Chromosomal Basis of Inheritance
    CHAPTER 15 THE CHROMOSOMAL BASIS OF INHERITANCE OUTLINE I. Relating Mendelism to Chromosomes A. Mendelian inheritance has its physical basis in the behavior of chromosomes during sexual life cycles B. Morgan traced a gene to a specific chromosome: science as a process C. Linked genes tend to be inherited together because they are located on the same chromosome D. Independent assortment of chromosomes and crossing over produce genetic recombinants E. Geneticists can use recombination data to map a chromosome’s genetic loci II. Sex Chromosomes A. The chromosomal basis of sex varies with the organism B. Sex-linked genes have unique patterns of inheritance III. Errors and Exceptions to Chromosomal Inheritance A. Alterations of chromosome number or structure cause some genetic disorders B. The phenotypic effects of some genes depend on whether they were inherited from the mother or father C. Extranuclear genes exhibit a non-Mendelian pattern of inheritance OBJECTIVES After reading this chapter and attending lecture, the student should be able to: 1. Explain how the observations of cytologists and geneticists provided the basis for the chromosome theory of inheritance. 2. Describe the contributions that Thomas Hunt Morgan, Walter Sutton, and A.H. Sturtevant made to current understanding of chromosomal inheritance. 3. Explain why Drosophila melanogaster is a good experimental organism. 4. Define linkage and explain why linkage interferes with independent assortment. 5. Distinguish between parental and recombinant phenotypes. 6. Explain how crossing over can unlink genes. 7. Map a linear sequence of genes on a chromosome using given recombination frequencies from experimental crosses. 8. Explain what additional information cytological maps provide over crossover maps.
    [Show full text]
  • Genetics Applied to Forestry an Introduction
    Genetics Applied to Forestry An Introduction Gösta Eriksson Inger Ekberg David Clapham Genetics Applied to Forestry An Introduction Third edition Gösta Eriksson Inger Ekberg David Clapham ISBN 978-91-576-9187-3 1 © 2013 Gösta Eriksson Inger Ekberg David Clapham ISBN 978-91-576-9187-3 Cover photos. Above: A Pinus sylvestris stand in Distribution Central Sweden, photograph Britt Ekberg-Eriks- Department of Plant Biology and Forest VRQ%HORZ$SURIXVHO\ÀRZHULQJTilia cordata Genetics, SLU, Box 7080, 750 07 Uppsala, Sweden tree in Central Sweden, photograph Inger Ekberg. Contact: [email protected] Printing: Elanders Sverige AB 2 Preface This book is a follow-up of An Introduction to Forest Genetics. It is somewhat expanded compared to the book printed in 2007. We were encouraged to ”publish” the revised version of the textbook on the internet. Undergraduate students are the target group as well as graduate students with limited experience of forest genetics. Without the advice and help from Kjell Lännerholm, Björn Nicander, Johan Samuelsson and Hartmut Weichelt the editing would have been more troublesome. We express our sincere thanks to them. A generous grant from Föreningen Skogsträdsförädling, The Tree Breeding Association in Sweden, made this printing grant from Föreningen Skogsträdsförädling, The Tree Breeding possible. A web version of this book may be found under http://vaxt2.vbsg.slu.se/forgen/Forestry_Genetics.pdf Uppsala December 2013 Gösta Eriksson Inger Ekberg David Clapham 3 Content Chapter 1 Chromosome cytology ................................7
    [Show full text]
  • Genetic Analysis of Resistance of Wild Melon to Podosphaera Xanthii Race 2F
    African Journal of Agricultural Research Vol. 6(14), pp. 3340-3345, 18 July, 2011 Available online at http://www.academicjournals.org/AJAR DOI: 10.5897/AJAR11.487 ISSN 1991-637X ©2011 Academic Journals Full Length Research Paper Genetic analysis of resistance of wild melon to Podosphaera xanthii race 2F Feng Xian, Yong Zhang, Jianxiang Ma, Jianqiang Yang and Xian Zhang* College of Horticulture, Northwest A and F University, Yangling 712100, Shaanxi, China. Accepted 15 June, 2011 Yuntian-930 is a wild melon that is highly resistant to powdery mildew (PM). To determine the inheritance of resistance to PM, Yuntian-930 was crossed with another cultivated melon, Hualaishi, which is susceptible to PM. Inheritance of resistance to Podosphaera xanthii race 2F. in six plant generations (P 1, P 2, F 1, B 1, B 2, and F 2) was studied using the mixed major-gene plus polygene inheritance mode with joint analysis method. Inheritance of resistance in wild melons fitted the model of two pairs of additive-dominance-epistasis major genes plus additive-dominant-epistasis polygene. The additive, dominant and epistatic effects between the two major genes played an important role in inheritance. The estimated values of heritability of major genes in B1, B 2 and F 2 were 62.98, 58.58 and 90.89% and those of polygene heritability were 28.93, 31.47 and 3.22%, respectively. The ratios of the environmental variance to phenotype variance were 5.89 to 9.94%. Thus, resistance of Yuntian-930 to PM was controlled not just by two major genes, but was also affected by polygene and the environment.
    [Show full text]
  • Genetics Update: Monogenetics, Polygene Disorders and the Quest for Modifying Genes
    Symonds, J. D. and Zuberi, S. M. (2018) Genetics update: monogenetics, polygene disorders and the quest for modifying genes. Neuropharmacology, 132, pp. 3-19. (doi:10.1016/j.neuropharm.2017.10.013) This is the author’s final accepted version. There may be differences between this version and the published version. You are advised to consult the publisher’s version if you wish to cite from it. http://eprints.gla.ac.uk/145819/ Deposited on: 05 September 2017 Enlighten – Research publications by members of the University of Glasgow http://eprints.gla.ac.uk Accepted Manuscript Genetics update: Monogenetics, polygene disorders and the quest for modifying genes Joseph D. Symonds, Sameer M. Zuberi PII: S0028-3908(17)30480-X DOI: 10.1016/j.neuropharm.2017.10.013 Reference: NP 6898 To appear in: Neuropharmacology Received Date: 31 January 2017 Revised Date: 9 October 2017 Accepted Date: 11 October 2017 Please cite this article as: Symonds, J.D., Zuberi, S.M., Genetics update: Monogenetics, polygene disorders and the quest for modifying genes, Neuropharmacology (2017), doi: 10.1016/ j.neuropharm.2017.10.013. This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. ACCEPTED MANUSCRIPT For channelopathy special issue Genetics update: monogenetics, polygene disorders and the quest for modifying genes Joseph D Symonds 1,2 and Sameer M Zuberi 1,2 * 1.
    [Show full text]
  • Glossary of Biotechnology and Genetic Engineering 1
    FAO Glossary of RESEARCH AND biotechnology TECHNOLOGY and PAPER genetic engineering 7 A. Zaid H.G. Hughes E. Porceddu F. Nicholas Food and Agriculture Organization of the United Nations Rome, 1999 – ii – The designations employed and the presentation of the material in this document do not imply the expression of any opinion whatsoever on the part of the United Nations or the Food and Agriculture Organization of the United Nations concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries. ISBN: 92-5-104369-8 ISSN: 1020-0541 All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, electronic, mechanical, photocopying or otherwise, without the prior permission of the copyright owner. Applications for such permission, with a statement of the purpose and extent of the reproduction, should be addressed to the Director, Information Division, Food and Agriculture Organization of the United Nations, Viale delle Terme di Caracalla, 00100 Rome, Italy. © FAO 1999 – iii – PREFACE Biotechnology is a general term used about a very broad field of study. According to the Convention on Biological Diversity, biotechnology means: “any technological application that uses biological systems, living organisms, or derivatives thereof, to make or modify products or processes for specific use.” Interpreted in this broad sense, the definition covers many of the tools and techniques that are commonplace today in agriculture and food production. If interpreted in a narrow sense to consider only the “new” DNA, molecular biology and reproductive technology, the definition covers a range of different technologies, including gene manipulation, gene transfer, DNA typing and cloning of mammals.
    [Show full text]
  • Bayesian Mapping of Quantitative Trait Loci Under the Identity-By-Descent-Based Variance Component Model
    Copyright 2000 by the Genetics Society of America Bayesian Mapping of Quantitative Trait Loci Under the Identity-by-Descent-Based Variance Component Model Nengjun Yi and Shizhong Xu Department of Botany and Plant Sciences, University of California, Riverside, California, 92521-0124 Manuscript received December 6, 1999 Accepted for publication May 19, 2000 ABSTRACT Variance component analysis of quantitative trait loci (QTL) is an important strategy of genetic mapping for complex traits in humans. The method is robust because it can handle an arbitrary number of alleles with arbitrary modes of gene actions. The variance component method is usually implemented using the proportion of alleles with identity-by-descent (IBD) shared by relatives. As a result, information about marker linkage phases in the parents is not required. The method has been studied extensively under either the maximum-likelihood framework or the sib-pair regression paradigm. However, virtually all investigations are limited to normally distributed traits under a single QTL model. In this study, we develop a Bayes method to map multiple QTL. We also extend the Bayesian mapping procedure to identify QTL responsible for the variation of complex binary diseases in humans under a threshold model. The method can also treat the number of QTL as a parameter and infer its posterior distribution. We use the reversible jump Markov chain Monte Carlo method to infer the posterior distributions of parameters of interest. The Bayesian mapping procedure ends with an estimation of the joint posterior distribution of the number of QTL and the locations and variances of the identi®ed QTL. Utilities of the method are demonstrated using a simulated population consisting of multiple full-sib families.
    [Show full text]
  • Progress and Prospects Andrew H
    Downloaded from genome.cshlp.org on October 5, 2021 - Published by Cold Spring Harbor Laboratory Press REVIEW Molecular Dissection of Quantitative Traits: Progress and Prospects Andrew H. Paterson1 Department of Soil and Crop Science, Texas A&M University, College Station, Texas 77843-2474 The "molecular dissection" of quantitative traits "complete" genetic maps, encompassing all re- began before the demonstration that DNA is the gions of all chromosomes in a plant or animal hereditary molecule. Opponents of the Mende- species. Complete genetic maps permit compre- lian genetic (particulate) theory argued that hensive analysis of the genome of an organism, many traits showed "blending inheritance," with revealing the locations of QTLs influencing vir- progeny intermediate between parents. This pre- tually any characteristic that can be measured. cipitated one of the great debates in the history of The development of DNA-based genetic genetics, which was eventually resolved by the mapping technology was propelled by the search realization that blending inheritance might be for genes responsible for human genetic diseases accounted for by the independent transmission and other traits (Botstein et al. 1980). QTLs influ- of many different Mendelian factors, together encing medically important phenotypes such as with the modifying effects of environment. high blood pressure (Rapp et al. 1989) and hyper- By early in the twentieth century, associa- tension (Jacob et al. 1991) are being identified in tions of genetic markers with differences in quan- mammalian models. Recent results have illus- titative phenotypes had already been noted. For trated the usefulness of genome mapping in dis- example, Sax (1923) noted association of differ- secting complex behavioral characteristics (Plo- ences in bean seed weight with seed-coat pig- min et al.
    [Show full text]
  • Bayesian Quantitative Trait Locus Mapping Based on Reconstruction of Recent Genetic Histories
    Copyright Ó 2009 by the Genetics Society of America DOI: 10.1534/genetics.109.104190 Bayesian Quantitative Trait Locus Mapping Based on Reconstruction of Recent Genetic Histories Dario Gasbarra,*,1 Matti Pirinen,* Mikko J. Sillanpa¨a¨*,† and Elja Arjas*,‡ *Department of Mathematics and Statistics and †Department of Animal Science, University of Helsinki, FIN-00014 Helsinki, Finland and ‡National Institute for Health and Welfare, FI-00271 Helsinki, Finland Manuscript received April 20, 2009 Accepted for publication July 15, 2009 ABSTRACT We assume that quantitative measurements on a considered trait and unphased genotype data at certain marker loci are available on a sample of individuals from a background population. Our goal is to map quantitative trait loci by using a Bayesian model that performs, and makes use of, probabilistic reconstructions of the recent unobserved genealogical history (a pedigree and a gene flow at the marker loci) of the sampled individuals. This work extends variance component-based linkage analysis to settings where the unobserved pedigrees are considered as latent variables. In addition to the measured trait values and unphased genotype data at the marker loci, the method requires as an input estimates of the population allele frequencies and of a marker map, as well as some parameters related to the population size and the mating behavior. Given such data, the posterior distribution of the trait parameters (the number, the locations, and the relative variance contributions of the trait loci) is studied by using the reversible-jump Markov chain Monte Carlo methodology. We also introduce two shortcuts related to the trait parameters that allow us to do analytic integration, instead of stochastic sampling, in some parts of the algorithm.
    [Show full text]
  • Behavioral Genetics
    1 Behavioral genetics The Cambridge Handbook of Evolutionary Perspectives on Sexual Psychology Severi Luoto, University of Auckland [email protected] Michael A. Woodley of Menie, Center Leo Apostel for Interdisciplinary Studies, Vrije Universiteit Brussel, Brussels, Belgium Cite this chapter as: Luoto, S. & Woodley of Menie, M. A. (in press). Behavioral genetics. In T. Shackelford (Ed.), The Cambridge Handbook of Evolutionary Perspectives on Sexual Psychology. Cambridge University Press. Word count: 12 019 (body text only), 15 352 (abstract, body text, references) Keywords: behavioral genetics, evolutionary psychology, evolution, natural selection, sexual selection 2 Abstract In this introductory chapter, we discuss the nexus between evolutionary theory and behavioral genetics, using it to elucidate the biological origins of human behavior and motivational predispositions. We introduce relevant behavioral genetics methods and evolutionary theoretical background to provide readers with the necessary conceptual tools to deepen their engagement with evolutionary behavioral genetics—as well as to help them take on the challenge of building a scientifically and evolutionarily more consilient account of human behavior. To demonstrate the utility of behavioral genetics in evolutionary behavioral science, our analytical examples range from personality, cognition, and sexual orientation to pair-bonding. We conclude by presenting a few recent landmark studies in behavioral genetics research with a particular focus on two aspects of sexual behavior: assortative mating and same-sex sexual behavior. This chapter considers behavioral genetics methods and their connection with evolutionary science more broadly while providing a succinct overview of recent advances in understanding the evolutionary genetic underpinnings of human sexual behavior, mate choice, and basic motivational processes.
    [Show full text]
  • Estimating Genetic Components of Variance for Quantitative Traits in Family Studies Using the MULTIC Routines
    Estimating Genetic Components of Variance for Quantitative Traits in Family Studies using the MULTIC routines Mariza de Andrade Elizabeth J. Atkinson Eric Lunde Christopher I. Amos * Jianfang Chen * May 16, 2006 Technical Report #78 Copywrite 2006 Mayo Foundation * Department of Epidemiology, U.T. MD Anderson Cancer Center Houston, Texas 1 Contents 1 Introduction 4 2 Software 6 2.1 Overview of primary functions . 6 2.1.1 Converting Solar IBDs . 7 2.1.2 Converting Simwalk IBDs . 7 2.2 Example: Data preparation . 8 3 Polygenic and Sporadic Models 9 3.1 Theory . 9 3.2 Estimation Methods . 9 3.3 Example: The polygene function . 11 4 Major Gene or QTL Analysis 13 4.1 Theory . 13 4.2 Statistical Tests . 13 4.3 Example: Whole Chromosome . 14 4.4 Example: Subset marker region or subjects . 16 5 Multivariate Traits 17 5.1 Theory . 17 5.2 Example: look at three traits . 18 5.3 Example: usage of Principal Components and Factor Analysis to combine traits . 20 6 Longitudinal Data 22 6.1 Theory . 22 6.2 Longitudinal Heritability Measure . 23 6.3 Longitudinal Statistical Tests . 23 6.4 Example: look at three time-points . 24 7 Diagnostics 25 7.1 Theory . 25 7.1.1 Testing for Normality . 25 7.1.2 Empirical Normal Quantile Transformation . 26 7.1.3 Influence of outliers . 26 7.2 Example . 28 7.2.1 Normality and Outliers . 28 7.2.2 Influential Families . 32 8 Validation of Results 33 8.1 Jackknife . 33 8.2 Bootstrap . 34 9 Time tests 38 10 Future Directions 40 2 11 Function helpfiles 40 multic .
    [Show full text]