Genetic Epidemiology Methods and Applications 1st edition by Melissa A Austin – Ebook PDF Instant Download/Delivery: 1780641818, 978-1780641812
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ISBN 10: 1780641818
ISBN 13: 978-1780641812
Author: Melissa A Austin
Genetic epidemiology plays a key role in discovering genetic factors influencing health and disease, and in understanding how genes and environmental risk factors interact. There is growing interest in this field within public health, with the goal of translating the results into promoting health and preventing disease in both families and populations. This textbook provides graduate students with a working knowledge of genetic epidemiology research methods. Following an overview of the field, the book reviews key genetic concepts, provides an update on relevant genomic technology, including genome-wide chips and DNA sequencing, and describes methods for assessing the magnitude of genetic influences on diseases and risk factors. The book focuses on research study designs for discovering disease susceptibility genes, including family-based linkage analysis, candidate gene and genome-side association studies, assessing gene-environment interactions and epistasis, studies of Non-Mendelian inheritance, and statistical analyses of data from these studies. Specific applications of each research method are illustrated using a variety of diseases and risk factors relevant to public health, and useful web-based genetic analysis software, human reference panels, and repositories, that can greatly facilitate this work, are described. Concluding with a review of ethical issues and a framework for translating human genomics research to clinical practice and public health benefit, this textbook is an essential new resource for graduate students in epidemiology and public health genetics.
Genetic Epidemiology Methods and Applications 1st Table of contents:
1 The Evolving Field of Genetic Epidemiology
1.1 Introduction
1.2 Assessing Genetic Influences on Disease, Human Genetics Concepts, and Genomic Technology
1.3 Family-based Study Designs: Linkage, Exome Sequencing and Case–Parent Trios
1.4 Genetic Association Studies of Common and Rare Variants, Large-scale Collaborations, and Population Stratification
1.5 Gene–Environment Interactions, Epistasis, and Non-Mendelian Genetics
1.6 Software and Data Resources
1.7 Ethical Issues and Translational Genetic Epidemiology
1.8 Conclusion
2 Assessing Genetic Influences on Diseases and Risk Factors
2.1 Introduction
2.2 Familial Aggregation Studies
2.2.1 Familial aggregation study designs
2.3 Heritability Analysis
2.3.1 Polygenic model
2.3.2 Estimating heritability using twins
2.3.3 Assumptions, biases, and misconceptions in heritability analysis
2.4 Conclusion: Heritability and the Polygenic Model in the Genomic Era
3 Genetic Concepts and Genomic Technology for Genetic Epidemiology
3.1 Introduction
3.2 Mendelian Inheritance and Complex Traits
3.2.1 Mendel’s laws
3.2.2 Modes of inheritance for single genes
3.2.3 Online Mendelian Inheritance in Man (OMIM®)
3.2.4 Complex traits
3.3 Hardy–Weinberg Principle
3.3.1 Deviations from Hardy–Weinberg equilibrium
3.4 Genetic Code, Gene Structure, and Genetic Markers
3.4.1 Exons, introns, and untranslated regions
3.4.2 Single nucleotide polymorphisms (SNPs) and structural variations
3.5 Genetic Linkage and Linkage Disequilibrium
3.5.1 Genetic linkage and recombination
3.5.2 Linkage disequilibrium and haplotypes
3.5.3 Tag SNPs
3.5.4 SNP genotyping platforms
3.6 DNA Sequencing Technologies
3.6.1 Three generations of DNA sequencing
3.6.2 Exome sequencing
3.7 Study Designs for Common and Rare Genetic Variants
3.8 Conclusion
4 Family Studies in Genetic Epidemiology: From Linkage to Exome Sequencing
4.1 Linkage Analysis
4.1.1 Basics of LOD score linkage analysis
4.1.2 Non-parametric linkage analysis
4.2 Family-based Association Studies
4.2.1 Linkage and association in families
4.2.2 Case–parent trios and the transmission disequilibrium test (TDT)
4.2.3 Extensions of the TDT
4.3 Conclusion
5 Genetic Association Studies
5.1 Introduction to Genetic Association Studies
5.2 Study Designs for Candidate Gene Studies
5.2.1 Practical considerations for selecting candidate genes
5.3 Complexities of Interpreting Genetic Association Results
5.3.1 Direct, causal relationship
5.3.2 Indirect association due to linkage disequilibrium
5.3.3 False-positive associations
5.3.4 False-negative associations and power
5.4 Basics of Genome-wide Association Studies (GWAS)
5.4.1 GWAS case–control studies
5.4.2 Data analysis of GWAS
5.5 Large-scale Collaborations: The Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) Consortium
5.5.1 The CHARGE Consortium
5.5.2 CHARGE goals and organization
5.5.3 Genome-wide genotyping methods
5.5.4 Design of CHARGE analyses
5.5.5 Phenotype working groups
5.5.6 Analysis methods
5.5.7 Productivity
5.5.8 Collaborations with non-CHARGE studies and other consortia
5.5.9 Working-group model as innovation
5.5.10 A new era of consortia
5.5.11 CHARGE: past, present, and future
5.5.12 Facilitating large-scale collaborations
5.6 Case–Parent Trio Designs in GWAS
5.7 Heritability and Allelic Architecture of Complex Traits
5.7.1 Missing heritability in GWAS
5.8 Study Designs and Statistical Analysis of DNA Sequencing Data
5.8.1 Study designs for rare genetic variants
5.8.2 Methods for detecting association with rare variants
5.9 Conclusion
6 Population Stratification in Genetic Association Studies
6.1 General Concepts
6.1.1 Rationale for GWAS
6.1.2 Population stratification confounding
6.2 Detecting Population Stratification
6.3 Statistical Methods to Adjust for Global Population Stratification Confounding
6.3.1 Genomic control
6.3.2 Ethnicity adjustment
6.3.3 Principal components adjustment
6.3.4 Estimated ancestry adjustment
6.3.5 Estimated correlation methods
6.4 Statistical Methods to Adjust for Local Population Stratification Confounding
6.4.1 Mixing of populations and local population structure
6.4.2 Lactase persistence gene
6.4.3 Controlling for local ancestry
6.5 Conclusion
7 Gene–Environment Interactions and Epistasis
7.1 Why Study Interaction?
7.2 Models of Interaction
7.3 Statistical Interaction and the Problem of Measurement Scale
7.3.1 Statistical analysis of interaction for cohort and case–control studies
7.3.2 Multiplicative and additive measurement scales
7.3.3 Causal modeling applied to interaction
7.4 Case-only Study Design
7.4.1 Considerations for case-only studies
7.5 Pharmacogenomics
7.6 Gene–Environment-wide Interaction Studies (GEWIS)
7.6.1 Harmonization of environmental data
7.6.2 GEWIS data analysis approaches
7.7 Epistasis: Interactions Between Genes
7.8 Conclusion
8 Non-Mendelian Genetics
8.1 Introduction
8.2 Mitochondrial Genetics
8.3 De Novo Variation
8.4 Parental and Parent-of-origin Effects
8.5 DNA Methylation: An Epigenetic Mechanism Underlying Phenotypic Variation
8.6 Conclusion
9 Software and Data Resources for Genetic Epidemiology Studies
9.1 Introduction
9.2 Software for Genetic Data Analysis
9.2.1 R software
9.2.2 PLINK
9.2.3 Software for association testing in samples from structured populations
9.2.4 Individual ancestry estimation software
9.2.5 Relatedness estimation software for GWAS
9.2.6 Software for power calculations in genetic association studies
9.2.7 Genotype imputation software
9.2.8 An alphabetical list of genetic analysis software
9.3 Human Reference Panels and Resources
9.3.1 International HapMap Project (HapMap)
9.3.2 1000 Genomes Project
9.3.3 Human Genetic Diversity Panel (HGDP)
9.3.4 Genome Variation Server
9.4 Genotype and Phenotype Repositories
9.5 Conclusion
10 Ethical Issues in Genetic Epidemiology
10.1 Introduction
10.2 Current Regulatory Environment
10.3 Trade-offs of Data De-identification
10.4 Informed Consent and Respect
10.5 Research Governance
10.6 Conclusion
11 Public Health and Clinical Applications of Genetic Epidemiology
11.1 Introduction
11.2 T1: From Discovery to Candidate Health Application
11.3 T2: From Health Application to Evidence-based Guidelines
11.4 T3: From Guidelines to Health Practice
11.5 T4: From Practice to Population Health Impact
11.6 Conclusion
Index
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Tags: Melissa A Austin, Genetic Epidemiology, Methods and Applications