Search Results

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 discusses gene-disease associations in osteoporosis. Osteoporosis is — together with osteoarthritis — the most common locomotor disease, and its clinical sequela, including fractures, cause substantial disease burden and costs. It has strong genetic influences, and identification of the underlying DNA variants can help in understanding the disease process and might benefit the development of interventions and diagnostics. The GENOMOS and GEFOS consortia have been established, using large collections of DNA samples from subjects with osteoporosis phenotypes that use standardized methodology and definitions. These collaborative consortia have identified — and refuted — associations of well-known candidate genes, and also play an important role in validation of risk alleles from genome-wide association studies (GWAS) for osteoporosis. Together with studies on rare variants, the GWA approach, in combination with the GENOMOS/GEFOS consortia, will help in clarifying the genetic architecture of complex bone traits such as bone mineral density (BMD), and — eventually — in understanding the genetics of fracture risk, the clinically more relevant but biologically more challenging endpoint in osteoporosis.

Inherited genetic factors play an important role in the etiology of colorectal cancer. Rare high-penetrance mutations account for a small proportion of diseases but their identification plays an important role in the clinical management of the high-risk families in which these mutations segregate. The results of most candidate gene association studies of colorectal cancer have not been replicated consistently. Many results can be considered false positives; others may represent very small effects, which will require replication in larger studies before firm conclusions can be reached. This chapter reviews recent, genome-wide association studies that have discovered many common, low-penetrance genetic variants associated with risk of colorectal cancer.

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.

Coronary heart disease (CHD) — which includes myocardial infarction, angina pectoris, and stenosis of the coronary arteries — is the leading cause of death worldwide, with over 7 million deaths per year. The tendency for CHD to cluster in families suggests that genetic variation, either directly or through modulation of known or as yet unidentified risk factors, importantly influences CHD risk. This chapter provides a critical and quantitative review of the current state of evidence regarding potential genetic susceptibility loci and CHD. It reviews published quantitative reviews of candidate gene polymorphisms and published GWAS addressing CHD.

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.

This chapter discusses the genetics of type 2 diabetes (T2D). T2D represents one of the most important causes of global morbidity and mortality. On current projections, the prevalence of this condition will double within a generation, with most of this increase occurring in the countries least well equipped to deal with the social and economic consequences. These rapid changes in prevalence clearly reflect global shifts in lifestyle (greater caloric intake and reduced energy expenditure) that are closely linked to rising rates of obesity. Nevertheless, twin and family studies have repeatedly demonstrated that individual predisposition to T2D has a substantial genetic component. Identification of the genes and variants responsible for these predisposition effects provides valuable insights into pathogenesis; these should, in turn, spur translational advances in clinical care, including the development of novel therapeutic and diagnostic approaches. In addition, it may well become increasingly possible to use personal genetic profile information as a means toward more targeted, individualized clinical management.

Efforts to identify the genes that modulate the risk for schizophrenia (SZ) have met with only limited success. This is at least in part due to problems that aggravate epidemiologic research in many psychiatric diseases, for example, a considerable degree of phenotypic variability and diagnostic uncertainty, the lack of extended pedigrees with Mendelian inheritance, and the absence of definitive disease-specific neuropathological features or biomarkers. The identification of susceptibility genes is further complicated by gene—gene interactions that are difficult to predict and model, and a likely substantial but difficult to detect, environmental component. Notwithstanding these challenges, several chromosomal regions thought to harbor SZ genes have been identified via whole genome linkage analyses, a few overlapping across different samples. This chapter focuses on the “SzGene” database developed by the Schizophrenia Research Forum, which systematically collects, summarizes, and meta-analyzes all genetic association studies published in the field of SZ, including genome-wide association studies (GWAS).

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.

A literature search was performed using the HuGE Navigator with the term “bladder cancer” and PubMed searches with the terms “ bladder cancer polymorphisms” and “bladder cancer risk variants” through September, 2008 for the purpose of performing systematic meta-analysis. Publications that did not have controls that were related to outcomes other than bladder cancer risk (e.g. survival), or were performed in special populations (e.g. non-smokers), were excluded. From this search, 32 SNPs reported on in three or more studies were identified, and this chapter summarizes the current evidence for the role of common genetic variation in the etiology of the bladder.

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.

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.

Preterm birth (PTB) is a perplexing clinical condition and major public health challenge. In 2006, 12.8% of all births in the United States were preterm, defined as occurring before 37 completed weeks of gestation. Preterm birth is the second leading cause of infant mortality and the leading cause of infant mortality among black infants in the United States, as well as the major contributor to worldwide infant mortality and morbidity. Despite the significant public health burden of PTB, there are few effective strategies to reliably predict or prevent PTB. The etiology of this common complex condition remains elusive. Efforts to identify environmental contributors suggest that smoking, stress, black race, nutritional deficits, and infection contribute to, but do not explain, the majority of PTBs. Therefore, the discovery of predisposing genetic variants and relevant gene-environment interactions will likely be of great value in unraveling the mystery of PTB, by identifying women at risk and setting the stage for research and enhanced clinical and public health prevention strategies. This chapter discusses gene-disease associations PTBs.

The term “leukemia” refers broadly to cancer of the white blood cells, or leukocytes. As a group, leukemias are the most common cancer among people under 15 years of age, accounting for 32% of all childhood malignancies. This chapter summarizes the total body of literature on the genetic epidemiology of childhood leukemias, focusing specifically on main effects of gene variants, highlighting conclusions that can be drawn based on the work to date, and emphasizing challenges and future directions.

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.

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.

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 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.