Genetic Analysis of Complex Diseases (eBook)

William K. Scott, Marylyn D. Ritchie (Herausgeber)

eBook Download: EPUB

2021 | 3. Auflage
John Wiley & Sons (Verlag)
978-1-119-10407-0 (ISBN)

Infos

Genetic Analysis of Complex Diseases

An up-to-date and complete treatment of the strategies, designs and analysis methods for studying complex genetic disease in human beings

In the newly revised Third Edition of Genetic Analysis of Complex Diseases, a team of distinguished geneticists delivers a comprehensive introduction to the most relevant strategies, designs and methods of analysis for the study of complex genetic disease in humans. The book focuses on concepts and designs, thereby offering readers a broad understanding of common problems and solutions in the field based on successful applications in the design and execution of genetic studies.

This edited volume contains contributions from some of the leading voices in the area and presents new chapters on high-throughput genomic sequencing, copy-number variant analysis and epigenetic studies. Providing clear and easily referenced overviews of the considerations involved in genetic analysis of complex human genetic disease, including sampling, design, data collection, linkage and association studies and social, legal and ethical issues.

Genetic Analysis of Complex Diseases also provides:

• A thorough introduction to study design for the identification of genes in complex traits
• Comprehensive explorations of basic concepts in genetics, disease phenotype definition and the determination of the genetic components of disease
• Practical discussions of modern bioinformatics tools for analysis of genetic data
• Reflecting on responsible conduct of research in genetic studies, as well as linkage analysis and data management
• New expanded chapter on complex genetic interactions

This latest edition of Genetic Analysis of Complex Diseases is a must-read resource for molecular biologists, human geneticists, genetic epidemiologists and pharmaceutical researchers. It is also invaluable for graduate students taking courses in statistical genetics or genetic epidemiology.

William K. Scott, PhD, is Professor at the University of Miami Leonard M. Miller School of Medicine where he teaches design and analysis of human genomic studies. He has authored over 200 peer-reviewed articles on the genetic epidemiology of complex traits.

Marylyn D. Ritchie, PhD, is Professor in the Department of Genetics at the University of Pennsylvania, Perelman School of Medicine. She is also the Director of the Center for Translational Bioinformatics in the Institute for Biomedical Informatics. She has authored over 350 peer-reviewed articles on statistical genetics, translational bioinformatics and biomedical informatics.

Inhalt

CHAPTER 1 Designing A Study For Identifying Genes In Complex Traits
William K. Scott, Marylyn D. Ritchie, Jonathan L. Haines, and Margaret A. Pericak-Vance

CHAPTER 2Basic Concepts In Genetics
Kayla Fourzali, Abigail Deppen, and Elizabeth Heise

CHAPTER 3 Determining The Genetic Component of A Disease
Allison Ashley Koch and Evadnie Rampersaud

CHAPTER 4 Study Design For Genetic Studies
Dana C Crawford and Logan Dumitrescu

CHAPTER 5 Responsible Conduct of Research In Genetic Studies
Susan Estabrooks Hahn, Adam Buchanan, Chantelle Wolpert, and Susan H. Blanton

CHAPTER 6 Linkage Analysis
Susan Blanton

CHAPTER 7 Data Management
Stephen D. Turner and William S. Bush

CHAPTER 8 Linkage Disequilibrium and Association Analysis
Eden R. Martin and Ren-Hua Chung

CHAPTER 9 Genome-Wide Association Studies
Jacob McCauley, Yogasudha Veturi, Shefali Setia Verma, and Marylyn D. Ritchie

CHAPTER 10 Bioinformatics of Human Genetic Disease Studies
Dale J. Hedges

CHAPTER 11 Complex Genetic Interactions / Data Mining/ Dimensionality Reduction
William S. Bush and Stephen D. Turner

CHAPTER 12 Sample Size, Power, and Data Simulation
Sarah A. Pendergrass and Marylyn D. Ritchie

Index

Textauszug

1
Designing a Study for Identifying Genes in Complex Traits

William K. Scott1, Marylyn D. Ritchie2, Jonathan L. Haines3, and Margaret A. Pericak-Vance1

1 Dr. John T. Macdonald Foundation Department of Human Genetics, University of Miami Miller School of Medicine, Miami, FL, USA

2 Department of Genetics, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA

3 Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, OH, USA

Introduction

Disease gene discovery in humans has a long history, predating even the identification of DNA as the genetic molecule (Watson and Crick 1953) and the determination of the number of human chromosomes (Ford and Hamerton 1956; Tjio and Levan 1956). In fact, as early as the 1930s some simple statistical methods for the analysis of genetic data had been developed (Bernstein 1931; Fisher 1935a,b). However, these methods were severely limited in their application (more on basic concepts of genetics in Chapter 2). Not only were genetic markers lacking (the ABO blood type was one of the few that had been described), but these methods were restricted to small, two to three generation pedigrees. Any calculations were performed by hand, of course, making analysis laborious.

There were two hurdles to overcome before human disease gene discovery would become routine. First, appropriate statistical methods were lacking, as were ways of automating the calculations. Second, sufficient genetic markers to cover the human genome needed to be identified. Morton (1955), building on the work of Haldane and Smith (1947) and Wald (1947), described the use of maximum likelihood approaches in a sequential test for linkage between two loci. He used the term “LOD score” (for logarithm of the odds of linkage) for his test. This score is the basis for most modern genetic linkage analyses and represents a milestone in human disease gene discovery. However, the complex calculations had to be done by hand, severely limiting the use of this approach. Elston and Stewart (1971) described a general approach for calculating the likelihood of any non‐consanguineous pedigree. This algorithm was extended by Lange and Elston (1975) to include pedigrees of arbitrary complexity. Soon thereafter, the first general‐purpose computer program for linkage in humans, LIPED (Ott 1974), was described. Thus, the first of the two major hurdles was overcome.

By the mid‐1970s there were 40–50 red cell antigen and serum protein polymorphisms available as genetic markers. A few markers could be arranged into initial linkage groups, but these markers covered only approximately 5–15% of the human genome. In addition to this limited coverage, genotyping these polymorphisms was labor intensive, time consuming, and often quite technically demanding. This remaining hurdle was crossed with the description of restriction fragment length polymorphisms (RFLPs) by Botstein et al. (1980). Not only were these markers easier to genotype in a standard manner, but they were frequent in the genome, covering the remaining 85–95% of the genome for the first time.

With these tools in place, the field of human disease gene discovery blossomed. The first successful disease gene linkage using RFLPs was reported (Gusella et al. 1983), localizing the Huntington disease gene to chromosome 4p. This discovery marked the beginning of disease gene identification through the positional cloning approach. Early successes using positional cloning were for diseases inherited in Mendelian fashion: autosomal dominant, autosomal recessive, or X‐linked. Although confounding factors such as genetic heterogeneity, variable penetrance, and phenocopies might exist for single‐gene or Mendelian traits, it is generally possible with a known genetic model to determine the best and most efficient approach to identifying the responsible gene. The success of these tools is apparent since by mid‐2017 over 3350 single‐gene disorders had at least one causative genetic variant identified (OMIM, accessed May 2017 at http://omim.org).

However, the inheritance patterns for traits such as the common form of Alzheimer’s disease, multiple sclerosis, and non‐insulin‐dependent diabetes (to name a few) do not fit any simple genetic explanation, making it far more difficult to determine the best approach to identifying the unknown underlying effect. In addition to the confounding factors involved in single‐gene disorders, such as genetic heterogeneity and phenocopies, gene–gene and gene–environment interactions must be considered when a complex trait is dissected. However, the tools that enabled efficient mapping of Mendelian trait loci through positional cloning were not as effective in dissecting these more complex traits. New statistical tools, study designs, and genotyping technologies were needed to perform large‐scale analysis of genetic factors underlying these complex traits. As these technologies were developed, a new approach to complex disease gene identification via genome‐wide association studies (GWAS) was enabled. The shift to this approach was predicted by a seminal perspective published by Risch and Merikangas (1996), in which they showed that large‐scale case–control analyses of complex traits would be a powerful and efficient method of identifying alleles underlying complex traits, once genotyping technology allowed the cost‐effective determination of a dense map of genetic markers. The first GWAS was published in 2005 (Klein et al. 2005), identifying the association of variation in the CFH gene with age‐related macular degeneration. This was simultaneously confirmed using alternate study designs (Edwards et al. 2005; Haines et al. 2005) proving that GWAS worked, allowing this new era of complex disease genetics to begin in earnest.

With the dawn of the GWAS era, a corresponding shift in the prevailing hypotheses for these studies occurred. No longer were studies solely searching for one or a few rare mutations in a single gene that cause a rare and devastating disease. Studies of common complex diseases were searching for multiple alterations in one or more genes acting alone or in concert to increase or decrease the risk of developing a trait. Early GWAS tended to test the “common disease‐common variant” (CDCV) hypothesis: the risk for common diseases, across ethnic groups, arises from evolutionarily old variants that have had substantial time to spread throughout the human population. Many studies successfully identified thousands of variants associated with the risk of complex diseases. An interactive catalog of these variants is maintained by the National Human Genome Research Institute and the European Molecular Biology Laboratory at http://www.ebi.ac.uk/gwas. Despite these successes, many studies testing the CDCV hypothesis failed to explain all the heritable variation in the risk of the complex traits under study – a phenomenon termed “missing heritability” (Manolio et al. 2009). One explanation for this was that the effect of rare variants was not well studied by early GWAS – an alternative hypothesis termed the “common disease‐rare variant” (CDRV) hypothesis. This hypothesis suggests that risk of common complex diseases arises from a larger number of rare variants in one or more genes, perhaps occurring more recently.

As was the case with common variants and the exploration of the CDCV hypothesis being enabled by GWAS approaches and high‐throughput genotyping technology, exploration of the CDRV hypothesis was enabled by advances in high‐throughput sequencing technology and accompanying statistical analysis methods. Initial screens of coding‐sequence variants in Mendelian traits via whole‐exome sequencing (WES) were published by Ng et al. (2009, 2010) and Choi et al. (2009), demonstrating that in some cases, disease gene mapping could skip the positional cloning strategy and proceed directly to evaluating segregation of mutations in families. This proof of principle has been used to justify this approach for testing the CDRV hypothesis in complex traits but has been met with mixed success. A successful example is the recent analysis of 50 000 individuals in the MyCode Community Health Initiative successfully identified rare variants underlying cardiovascular traits and lipid levels (Dewey et al. 2016). The rapid and continuing decrease in whole‐genome sequencing (WGS) costs suggests that within a few years, it will be possible (and perhaps commonplace) to test the CDRV hypothesis using WGS in large sample sizes – essentially performing genome‐wide association for common and rare variants with direct genotype determination via sequencing.

Study design, laboratory methods, and analytic approaches differ by trait type (Mendelian or complex) and hypothesis being tested (rare disease‐rare variant, Mendelian positional cloning; CDCV [GWAS]; CDRV [WES or WGS and individual variant or set‐based association]). These approaches are described in the following sections.

Components of a Disease Gene Discovery Study

Each genetically complex trait has its own peculiarities that require special attention. However, a guiding paradigm can be applied to most conditions. Originally, the general approach...

Produkt-Details

• Erscheint lt. Verlag: 11.11.2021
• Sprache: englisch
• Themenwelt: Medizin / Pharmazie ► Medizinische Fachgebiete
• Themenwelt: Studium ► 2. Studienabschnitt (Klinik) ► Humangenetik
• Themenwelt: Naturwissenschaften ► Biologie ► Genetik / Molekularbiologie
• Schlagworte: Bioinformatics & Computational Biology • Bioinformatik • Bioinformatik u. Computersimulationen in der Biowissenschaften • Biowissenschaften • Genetics • Genetik • Life Sciences • medical genetics • Medizinische Genetik
• ISBN-10: 1-119-10407-6 / 1119104076
• ISBN-13: 978-1-119-10407-0 / 9781119104070