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The recent availability of commercial high-density genotyping technologies, combined with the identification of subsets of single nucleotide polymorphisms (SNPs) that are capable of “tagging” most of the common variants in the human genome from the HapMap project, has now made it feasible to conduct genome-wide association studies (GWAS). There have now been quite a few reviews of the general principles of the design and analysis of GWAS. This chapter focuses on some of the basic issues of multistage sampling design as they have been developed for this purpose, and some of the associated analysis issues.

Environmental modifiers of the effects of genetic variants, or gene-environment interactions, have received increased attention in recent years due to the recognition that genetic variants alone are unlikely to explain most of the recent increases in chronic diseases. Such increases are more likely due to environmental and behavioral changes interacting with a genetic predisposition, suggesting that failing to identify and control environmental modifiers of disease risk could mask important associations with genetic variants or misestimate the magnitude of their effects. Identifying environmental modifiers of these variants may also be essential in mitigating the risk conferred by these variants. Population-based genetic association studies with detailed characterization of environmental exposures are critical and underused resources for identifying potential interacting factors. This chapter explores the substantial and complementary strengths offered by the two main approaches to these studies — case-control and cohort designs — in the search for the genetic and environmental influences on common diseases.

This chapter reports the findings and recommendations from a multidisciplinary workshop, including geneticists, epidemiologists, journal editors, and bioinformatics experts, that was sponsored by the Human Genome Epidemiology Network (HuGENet) and held in Atlanta on January 24-25, 2008. The meeting was convened in order to discuss synthesis and appraisal of cumulative evidence on genetic associations and to develop a strategy for an online encyclopedia on genetic variation and common human diseases.

This chapter examines findings from the newly developed technology of genome-wide association studies (GWAS) being applied to the investigation of Alzheimer disease (AD), primarily in the United States, United Kingdom, and France. These linked research projects make use of many thousands of DNA samples procured from individuals diagnosed with AD, which are then assessed using high-speed throughput technology and compared with control samples, in an attempt to find out what combinations of genes put individuals at increased risk. To date, these enormously expensive projects have provided few if any startling new insights, and many researchers are highly skeptical as to their value. However, others believe that GWAS is a first step toward a more sophisticated way of understanding the interrelated pathways of the numerous genes that appear to be implicated in AD.

This chapter presents an overview of the development and progress in applications of genomic technologies, with a focus on genomic sequence variation. Topics discussed include genetic variation, genotype analysis, genome-wide association studies, genotyping issues, quality control in the laboratory, and bioinformatics.

Human Genome Epidemiology (HuGE) reviews have been a cornerstone of the efforts of the Human Genome Epidemiology Network (HuGENet) to develop an online resource to house the cumulative and changing information on epidemiologic aspects of human genes. HuGE reviews may collate evidence on population frequencies of genetic variants, genotype-phenotype associations, interactions among genes and between genes, and environmental exposures, or a combination of these. More than 70 HuGE reviews have been completed under the auspices of HuGENet, with more than 80 in preparation at the time of writing. This chapter explains what HuGE reviews aim to achieve and describes some key components of the methodology for undertaking them. The material is also directly relevant to reviews and meta-analysis of genetic association studies undertaken by groups outside of HuGENet.

This chapter begins with a discussion of the Rationale for a Second Edition of Human Genome Epidemiology. It then discusses public health applications of genome-wide association studies, the emergence of public health genomics, and phases of translation research in genomics. An overview of the subsequent chapters is presented.

In 2003, Terrie Moffitt and Avshalom Caspi published a groundbreaking study examining how the serotonin transporter gene and stressful life events interact to contribute to the risk of developing depression. When dozens of research teams around the globe attempted to replicate that original result, a peculiar thing emerged—some of the studies supported the original finding, but many came back negative. Faced with this dilemma, scientists performed meta-analyses of the replications; however, the meta-analyses only created their own puzzle—one came back supportive of the original finding, while several came back in conflict with it. Scientists studying the nature and nurture of depression were thus unable to agree whether the original study held up to the scrutiny or fell into disrepute, and unable to agree whether research on gene-environment interaction or research on genome wide association studies was the way forward for human genetics. This episode can be understood as the most recent instantiation of a long-standing dispute about gene-environment interaction. This chapter displays how contemporary scientists debating the nature and nurture of depression have repeated arguments for and against interaction that can be traced back through nearly a century of scientific debate.

Prostate cancer is the most common solid tumor and the second leading cause of cancer-related mortality in American men. Worldwide, prostate cancer ranks second and fifth as a cause of cancer and cancer deaths, respectively. Despite the international burden of disease due to prostate cancer, its etiology is unclear in most cases. Established risk factors include age, race/ancestry, and family history of the disease. Prostate cancer has a strong heritable component, and genome-wide association studies have identified over 110 common risk-associated genetic variants. Family-based sequencing studies have also found rare mutations (e.g., HOXB13) that contribute to prostate cancer susceptibility. Numerous environmental and lifestyle factors (e.g., obesity, diet) have been examined in relation to prostate cancer incidence, but few modifiable exposures have been consistently associated with risk. Some of the variability in results may be related to etiological heterogeneity, with different causes underlying the development of distinct disease subgroups.

This chapter explores the possible contribution of genome-wide association studies (GWAS) to social science. Medical scientists have been looking for the human genome for genes that account for diseases believed to have a genetic basis. Socioeconomic survey data containing genetic markers obtained from respondents’ DNA are expected to become available in the near future. GWAS has the potential to identify genes associated with schooling, earnings, criminality, marital stability, and a variety of socioeconomic phenomena. However, this chapter argues that GWAS have generally not resulted in major breakthroughs in medicine. The problem is that such studies are based on induction, in contrast to successful science which is based on deduction, and may even be less successful in the social sciences than they are in medicine. The chapter also discusses methodological issues associated with behavioral genetics in the context of the role of heredity in determining human outcomes.

This chapter offers an in-depth review of recent developments based on probabilistic graphical models (PGMs) and dedicated to two major concerns: the fundamental task of modeling dependences within genetic data, that is linkage disequilibrium (LD), and the downstream application to genome-wide association studies (GWASs). Throughout the whole chapter, the selected examples illustrate the use of Bayesian networks, as well as that of Markov random fields, including conditional and hidden Markov random fields. First, the chapter surveys PGM-based approaches dedicated to LD modeling. The next section is devoted to PGM-based GWASs and mainly focuses on multilocus approaches, where PGMs allow to fully benefit from LD. This section also provides an illustration for the acknowledgment of confounding factors in GWASs. The next section is dedicated to the detection of epistastic relationships at the genome scale. A recapitulation and a discussion end the chapter. Finally, directions for future works are outlined.

This chapter explores the genetic epidemiology of cancer: the identification and quantification of inherited genetic factors, and their potential interaction with the environment, in the etiology of cancer in human populations. It also describes the techniques used to identify genetic variants that contribute to cancer susceptibility. It describes the older research methods for identifying the chromosomal localization of high-risk predisposing genes, such as linkage analysis within pedigrees and allele-sharing methods, as it is important to understand the foundations of the field. It also reviews the epidemiologic study designs that can be helpful in identifying low-risk alleles in candidate gene and genome-wide association studies, as well as gene–environment interactions. Finally, it describes some of the genotyping and sequencing platforms commonly employed for high-throughput genome analysis, and the concept of Mendelian randomization and how it may be useful in the study of biomarkers and environmental causes of cancer.

Recent development of high-throughput genomics tools has made it possible and affordable to examine the molecular basis of variation in quantitative traits in studies of non-model species in the wild. High-density single nucleotide polymorphism data and genome sequences provide promising methodological advances complementing and strengthening traditional quantitative genetic analyses from long-term pedigrees. This chapter, discusses how high-density genomic data can be used to determine the actual or realised genetic relationship between relatives, which then can be accounted for in further analyses to improve estimates of quantitative genetic parameters, perhaps even without the need to construct a pedigree. Furthermore, this chapter suggests how combining long-term field data with high-density genomic data, to carry out genome-wide association studies or genomic predictions of phenotypes, can provide important insight into the genetic architecture and evolutionary dynamics of fitness-related traits. Empirical results thus far provide good support for the notion that most quantitative genetic traits studied in wild populations have a highly polygenic basis; a key assumption of quantitative genetic analyses. This chapter also discusses how high-density genomic data can be used to identify past signatures of selection in genetic data that can be further compared to loci currently responsible for variation in individual fitness. Finally, this chapter presents some important issues to consider when sampling, storing and preparing DNA for high-throughput genomics analyses. The application of high-throughput genomics tools in quantitative genetic studies of non-model species in the wild shows great promise to increase understanding of ecological and evolutionary processes in natural populations.

This chapter focuses on recent molecular approaches to behavioral genetics including gene finding and molecular genetics. In the latter the investigators identify critical DNA variants and trace the biological pathways from DNA to traits or disorders. The dialogue between Judge Jean and the behavioral geneticist is resumed, now discussing linkage and association methods including the newer genome-wide association studies (GWAS) that have produced a paradigm shift and raised the problem of "missing heritability." Novelty seeking and Alzheimer's disease studies are summarized, and how quantitative and molecular research programs are related is considered. The final dialogue introduces two cases involving genetic testing for IQ and for attention deficit hyperactivity disorder (ADHD). Judge Jean learns how much is known about both the quantitative and the molecular aspects of IQ and ADHD, and she is introduced to a promising but skeptical vision of the future of behavioral genetics in the context of neuroscientific complexity.

Genome-wide association studies (GWASs) are widely used to identify good candidates of disease-associated genes that are of interest for further follow-up studies. However, knowledge of biological pathways and interactions may improve the likelihood of making genuine discoveries in GWASs. A number of methods have been developed to incorporate prior biological knowledge when prioritizing genes. However, most methods treat genes in a specific pathway as an exchangeable set without considering the topological structure of the pathway. Based on results obtained from a standard association study on a Crohn’s disease cohort, it is first verified that neighboring genes in a pathway are more likely to share the same disease status. Then, a Markov Random Field (MRF) model is proposed, to incorporate pathway topology for association analysis. We show that the conditional distribution of our MRF model takes on a simple logistic regression form. Finally, we evaluate our model on real data.

This chapter could as well be titled “Population Genomics,” although many aspects of population genomics are integrated throughout the other chapters. It includes estimates of mutational variance and standing variance, phenotypic evolution under directional selection as measured by the linear selection gradient, and phenotypic evolution under stabilizing selection. It explores the strengths and limitations of genome-wide association studies of quantitative trait loci (QTLs) and expression (eQTLs) to detect genetic influencing complex traits in natural populations and genetic risk factors for complex diseases such as heart disease or diabetes. The number of genes affecting complex traits is considered, as well as evidence bearing on the issue of whether complex diseases are primarily affected by a very large number of genes, almost all of small effect, and how this bears on direct-to-consumer and over-the-counter genetic testing. The population genomics of adaptation is considered, including drug resistance, domestication, and local selection versus gene flow. The chapter concludes with the population genomics of speciation as illustrated by reinforcement of mating barriers, the reproducibility of phenotypic and genetic changes, and the accumulation of genetic incompatibilities.

The arrival of genome-wide association studies (GWASs) has opened the exciting possibility of identifying genetic variations (single nucleotide polymorphisms (SNPs)) that underlie common diseases. However, our knowledge of the genetic architecture of common diseases remains limited. One likely reason for this is the complex interactions between genes, the environment, and the studied disease. This chapter addresses three aspects which are expected to help make progress to reveal some of these complex interactions using GWAS data sets. First, results are shown that compare the performances of various Bayesian network scoring criteria. Second, developing heuristic search algorithms for learning complex interactions from high-dimensional data is a hot topic. Third, the hypothesis testing involved in genome-wide epistasis detection is substantially different from that involved in a standard GWAS analysis, where only a null hypothesis and an alternative are considered.

This book examines the roles of heredity, family, and social environment in shaping outcomes among humans, including anthropometric, behavioral, psychological, and economic outcomes. Drawing on extensive empirical literature, it shows that the outcomes of children are correlated with their parents’ outcomes to a certain extent, in virtually all aspects of life. It investigates the complex interplay among heredity, family, and environments using an axiomatic framework that draws on game theory, control theory, and econometrics. Several disciplines are involved in this study, from psychology and behavioral genetics to sociology, economics, and genetics. The book also recalls the scientific writings of Francis Galton (1822–1911), who laid the foundations for research on heredity and family and invented the dichotomy between “nature and nurture,” and concludes with a methodological critique of genome-wide association studies.

Within the social and behavioral sciences, there is a long history of the study of genetic and environmental influences on individual differences between people. This chapter provides an overview of the methods used to study behavioral and molecular genetics of well-being. The focus of behavior genetics is quantitative modeling using genetically informative family data to determine the relative influence of genes and environment on individual differences. Studies based on the well-known twin method report consistently that the inter-individual variability of almost any psychologically meaningful variable, including well-being, has a significant heritable component. More recently, molecular genetics techniques have been developed and used to identify measured sequences of DNA that can be tied to differences between people. New techniques from both behavior and molecular genetics continue to evolve, including biometric moderation models, biometric growth curve models, genome-wide and epigenome-wide association studies, and most recently, genome-wide complex trait analyses.