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Genetic Analysis of Complex Disease

3. Aufl.

von: William K. Scott, Marylyn D. Ritchie
83,99 €
Verlag:	Wiley-Blackwell
Format:	PDF
Veröffentl.:	10.11.2021
ISBN/EAN:	9781119104087
Sprache:	englisch
Anzahl Seiten:	336

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Titelbeschreibung

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

Inhaltsverzeichnis

List of Contributors xv Foreword xvii 1 Designing a Study for Identifying Genes in Complex Traits 1 William K. Scott, Marylyn D. Ritchie, Jonathan L. Haines,and Margaret A. Pericak-Vance Introduction 1 Components of a Disease Gene Discovery Study 3 Define Disease Phenotype 4 Clinical Definition 4 Determining that a Trait Has a Genetic Component 5 Identification of Datasets 5 Develop Study Design 5 Family-Based Studies 6 Population-Based Studies 6 Approaches for Gene Discovery 7 Analysis 7 Genomic Analysis 7 Statistical Analysis 8 Bioinformatics 8 Follow-up 8 Variant Detection 8 Replication 9 Functional Studies 9 Keys to a Successful Study 10 Foster Interaction of Necessary Expertise 10 Develop Careful Study Design 11 References 11 2 Basic Concepts in Genetics 13 Kayla Fourzali, Abigail Deppen, and Elizabeth Heise Introduction 13 Historical Contributions 13 Segregation and Linkage Analysis 13 Hardy–Weinberg Equilibrium 14 DNA, Genes, and Chromosomes 17 Structure of DNA 17 Genes and Alleles 19 Genes and Chromosomes 20 Genes, Mitosis, and Meiosis 22 When Genes and Chromosomes Segregate Abnormally 25 Inheritance Patterns in Mendelian Disease 25 Autosomal Recessive 25 Autosomal Dominant 25 X-linked Inheritance 28 Mitochondrial Inheritance 29 Y-linked 29 Genetic Changes Associated with Disease/ Trait Phenotypes 29 Mutations Versus Polymorphisms 29 Point Mutations 30 Sickle Cell Anemia 30 Achondroplasia 30 Deletion/Insertion Mutations 31 Duchenne and Becker Muscular Dystrophy 31 Cystic Fibrosis 31 Charcot-Marie- Tooth Disease 31 Nucleotide Repeat Disorders 32 Susceptibility Versus Causative Genes 32 Summary 34 References 34 3 Determining the Genetic Component of a Disease 36 Allison Ashley Koch and Evadnie Rampersaud Introduction 36 Study Design 37 Selecting a Study Population 37 Population-Based 38 Clinic-Based 38 Ascertainment 38 Single Affected Individual 39 Relative Pairs 40 Extended Families 40 Healthy or Unaffected Controls 41 Ascertainment Bias 42 Approaches to Determining the Genetic Component of a Disease 44 Co-segregation with Chromosomal Abnormalities and Other Genetic Disorders 44 Familial Aggregation 44 Family History Approach 44 Example of Calculating Attributable Fraction 46 Correlation Coefficients 46 Twin and Adoption Studies 47 Recurrence Risk in Relatives of Affected Individuals 48 Heritability 49 Example Using Correlation Coefficients to Calculate Heritability 50 Segregation Analysis 51 Summary 52 References 53 4 Study Design for Genetic Studies 58 Dana C. Crawford and Logan Dumitrescu Introduction 58 Selecting a Study Population 58 Family- Based Studies (Linkage) 59 Family- Based Studies (Association) 60 Studies of Unrelated Individuals (Association) 61 Cohort Studies 61 Cross- Sectional Studies 66 Case– Control Studies 66 Other Study Designs 68 Biobanks 69 Other Biobanks 71 Biospecimens for Biobanks 72 Summary 73 References 74 5 Responsible Conduct of Research in Genetic Studies 79 Susan Estabrooks Hahn, Adam Buchanan, Chantelle Wolpert,and Susan H. Blanton Introduction 79 Research Regulations and Genetics Research 80 Addressing Pertinent ELSI in Genetic Research 83 Genetic Discrimination 83 Privacy and Confidentiality 84 Certificate of Confidentiality 85 Coding Data and Samples 85 Secondary Subjects 86 Future Use of Samples/Data Sharing 87 Handling of Research Results 88 CLIA Regulations: Separation of Research and Clinical Laboratories 89 Releasing Children’s Genetic Research Results 90 DNA Ownership 90 DNA Banking 90 Family Coercion 91 Practical Methods for Efficient High-Quality Genetic Research Services 91 The Investigator as the Genetic Study Coordinator 92 Time Spent 92 Recruitment 93 Support Groups and Organizations 93 Referrals from Health Care Providers 93 Research Databases and the Internet 94 Institution Databases 94 Medical Clinics 94 Recruitment by Family Members 95 Informed Consent 95 Vulnerable Populations 96 Minors 97 Persons with Cognitive Impairment 97 Data and Sample Collection 97 Sample Collection 97 Confirmation of Diagnosis 98 The Art of Field Studies 99 Referring for Additional Medical Services 99 Maintaining Contact with Participants 100 Future Considerations 100 References 100 6 Linkage Analysis 105 Susan H. Blanton Disease Gene Discovery 107 Ability to Detect Linkage 116 Real World Example of LOD Score Calculation and Interpretation 117 Disease Gene Localization 120 Multipoint Analysis 121 Effects of Misspecified Model Parameters in LOD Score Analysis 124 Impact of Incorrect Disease Allele Frequency 124 Impact of Incorrect Mode of Inheritance 125 Impact of Incorrect Disease Penetrance 125 Impact of Incorrect Marker Allele Frequency 126 Control of Scoring Errors 127 Genetic Heterogeneity 128 Practical Approach for Model-Based Linkage Analysis of Complex Traits 131 Nonparametric Linkage Analysis 133 Identity by State and Identity by Descent 134 Methods for Nonparametric Linkage Analysis 136 Tests for Linkage Using Affected Sibling Pairs (ASP) 137 Test Based on Identity by State 137 Tests Based on Identity by Descent in ASPs 138 Simple Tests 138 Tests Applicable When IBD Status Cannot Be Determined 139 Multipoint Affected Sib-Pair Methods 141 Handling Sibships with More Than 2 Affected Siblings 142 Methods Incorporating Affected Relative Pairs 142 NPL Analysis 143 Fitting Population Parameters 145 Power Analysis and Experimental Design Considerations for Qualitative Traits 147 Factors Influencing Power of Sib-pair Methods 147 The Example of Testicular Cancer 148 Examples of Sib-Pair Methods for Mapping Complex Traits 150 Mapping Quantitative Traits 151 Measuring Genetic Effects in Quantitative Traits 152 Study Design for Quantitative Trait Linkage Analysis 154 Haseman–Elston Regression 155 Variance Components Linkage Analysis 156 Nonparametric Methods 158 The Future 159 Software Available 160 References 160 7 Data Management 169 Stephen D. Turner and William S. Bush Developing a Data Organization Strategy 170 A Brief Overview of Data Normalization 170 Database Management System (DBMS) and Structured Query Language (SQL) 172 Partitioning Data by Type 173 Sequence-Level Data 174 Sample-Level Data 174 Database Implementation 175 Hardware and Software Requirements 175 Implementation and Performance Tuning 175 Interacting with the Database Directly 176 Security 177 Other Tools for Data Management and Manipulation 177 R 177 PLINK 178 SAMtools 178 Workflow Management and Cloud Computing 178 Conclusion 179 References 179 8 Linkage Disequilibrium and Association Analysis 182 Eden R. Martin and Ren-HuaChung Introduction 182 Linkage Disequilibrium 182 Measures of Allelic Association 183 Causes of Allelic Association 184 Mapping Genes Using Linkage Disequilibrium 186 Tests of Association 187 Case–Control Tests 188 Test Statistics 188 Measures of Disease Association and Impact 189 Assessing Confounding Bias 191 Family-Based Tests of Association 192 The Transmission/Disequilibrium Test 192 Tests Using Unaffected Sibling Controls 194 Tests Using Extended Pedigrees 195 Regression and Likelihood-Based Methods 196 Association Tests with Quantitative Traits 197 Analysis of Haplotype Data 197 Genome-Wide Association Studies (GWAS) 198 Special Populations 199 HapMap 200 1000 Genomes Project 200 Summary 201 References 201 9 Genome-Wide Association Studies 205 Jacob L. McCauley, Yogasudha Veturi, Shefali Setia Verma, and Marylyn D. Ritchie Introduction 205 Definition of GWAS 206 Purpose of GWAS 206 Design 206 Technologies for High-Density Genotyping 206 Discrete and Quantitative Trait Analysis 208 Case–Control, Family-Based, and Cohort Study Designs 209 Statistical Power for Association and Correction for Testing Multiple Hypotheses 211 Data Analysis 212 Quality Control on Genotyping Call Data 212 Initial Genotyping Quality Control 213 Sample-Level Quality Control 214 SNP-Level Quality Control 215 Software Programs for Quality Control 215 Population Structure 216 Imputation 219 Genetic Association Testing 220 Meta-Analysis and “Mega-Analysis” 221 Whole-Genome Regression-Based GWAS 222 Conclusion 222 References 222 10 Bioinformatics of Human Genetic Disease Studies 228 Dale J. Hedges Introduction 228 Common Threads Genome Analysis 229 A Brief Note on Study Design 229 Data Format Manipulation 229 Planning for Adequate Computational Resources 230 Storage 231 Processing and Memory 232 Networking 232 Genomics in the Cloud 232 Processing and Analysis of Genomic Data 233 Array-Based Data 233 DNA Arrays and High-Throughput Genotyping 233 Preprocessing and Initial Quality Control 234 Genotype Calling 234 Call Efficiency 235 Data Cleaning and Additional Quality Control 236 Inferring Structural Variation From SNP-based Array Data 236 A Note on Statistical Analysis and Interpretation of Results 236 Array-Based Analysis of Gene Expression 237 Batch Effects and Data Normalization 237 Differential Expression 238 Classification and Clustering Methods 239 Visualization of Expression Data 240 Pathway and Network Analyses 240 Direct Counting and Other Expression Assay Procedures 241 Additional Uses for Oligonucleotide Arrays 242 High-Throughput Sequencing Methods for Genomics 243 Introduction 243 High-Throughput Sequencing for Genotype Inference 244 Expression Analysis from High-Throughput Sequencing Data – RNA-Seq 252 ChIP-Seq and Methylation-based Sequences 255 Bioinformatics Resources 256 Annotation of Genomic Data 257 Genome Browsers as Versatile Tools 258 Bioinformatics Frameworks and Workflows 259 Crowdsourcing and Troubleshooting 260 Data Sharing 260 References 261 11 Complex Genetic Interactions/Data Mining/Dimensionality Reduction 265 William S. Bush and Stephen D. Turner Human Diseases Are Complex 265 Complexity of Biological Systems 266 Genetic Heterogeneity 267 Statistical and Mathematical Concepts of Complex Genetic Models 268 Analytic Approaches to the Detection of Complex Interactions 270 Linkage Analysis/Genomic Sharing 270 Association Analysis 270 Genome‐Wide Association Analysis 272 Conclusion 273 References 273 12 Sample Size, Power, and Data Simulation 278 Sarah A. Pendergrass and Marylyn D. Ritchie Introduction 278 Sample Size and Power 279 Power Calculations and Simulation 282 Power Studies for Association Analysis 282 Software for Calculating Power for Association Studies, Family- or Population-Based 283 PGA: Power for Genetic Association Analyses 283 Fine-Mapping Power Calculator 284 Quanto 284 PAWE: Power for Association with Errors 284 PAWE-3D 284 GPC: Genetic Power Calculator 284 CaTS 284 INPower 284 Software for Calculating Power for Transmission Disequilibrium Testing (TDT) and Affected Sib-Pair Testing (ASP) 284 GPC: Genetic Power Calculator 284 TDT-PC: Transmission Disequilibrium Test Power Calculator 284 TDTASP 285 TDTPOWER 285 ASP/ASPSHARE 285 Simulation Software for Association Study Power Assessment 285 Backward and Forward Model Simulations 285 Coalescent Model Simulation – Short Genetic Sequences 286 Larger Coalescent Simulated Models 286 Forward Model Simulations – Short Genetic Sequences 286 Forward Model Simulations – Large Genetic Sequences 286 Resampling Simulation Tools 287 Software for Simulation of Phenotypic Data 287 Power Simulations for Linkage Analysis 288 Definitions for Power Assessments for Linkage Analysis 288 Computer Simulation Methods for Linkage Analysis of Mendelian Disease 289 SIMLINK 289 SLINK: Simulation Program for Linkage Analysis 289 SUP: Slink Utility Program 290 ALLEGRO 290 MERLIN: Multipoint Engine for Rapid Likelihood Inference 290 SimPED 290 Power Studies for Linkage Analysis – Complex Disease 290 Inclusion of Unaffected Siblings 291 Affected Relative Pairs of Other Types 291 Other Considerations 291 Genomic Screening Strategies: One-Stage versus Two-Stage Designs 291 Software for Designing Linkage Analysis Studies of Complex Disease 292 SIMLA 292 Quantitative Traits 292 Extreme Discordant Pairs 292 Sampling Consideration for the Variance Component Method 293 Software for Designing Linkage Analysis Studies for Quantitative Traits 294 SOLAR: Sequential Oligogenic Linkage Analysis Routines 294 MERLIN: Multipoint Engine for Rapid Likelihood Inference 294 SimuPOP 294 Summary 294 References 294 Index 298

Autorenportrait

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.

Back cover copy

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