Elsevier S & T

Genetic Dissection of Complex Traits

Rao, D.C.; Gu, C. Charles

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Table of contents

Contentsv
Contributorsxv
Preface to First Editionxxi
Preface to Second Editionxxv
Acknowledgmentsxxvii
Part I: Overview and Fundamentals1
Chapter 1: An Overview of the Genetic Dissection of Complex Traits3
I. Introduction4
II. Challenges Arising From Complex Traits7
III. Study Design9
A. Phenotype9
B. Genotype10
C. Linkage versus association10
D. Sampling type11
E. Sample size, significance level, and power11
IV. Analytical Methods12
A. Familial aggregation and genetic effects13
B. Linkage, association, and admixture mapping13
C. Dense SNPs and haplotype analysis16
D. Composite likelihoods18
E. Multiple testing18
V. Special Topics19
A. Pathway-based association studies and Bayesian networks19
B. Gene expression and systems biology20
C. Comparative genomics21
VI. GWA Studies22
A. Recent GWA studies22
B. Designing GWA studies23
C. On transferability of genome-wide tagSNPs24
D. Follow-up studies25
VII. Efficient Strategies for Enhancing Gene Discovery25
A. Lumping and splitting25
B. Meta-analysis27
C. Multivariate phenotypes28
VIII. Discussion28
Acknowledgments29
References30
Chapter 2: Familial Resemblance and Heritability35
I. Introduction36
II. Familial Resemblance and Heritability38
A. Family resemblance38
B. Heritability38
C. Risk-based estimates of heritability39
III. Study Designs and Multifactorial Models41
A. Nuclear families41
B. Extended pedigrees43
C. Twins43
D. Adoptions43
E. Modeling extensions44
F. Factors affecting heritability estimation45
IV. Discussion46
Acknowledgments47
References47
Chapter 3: Linkage and Association: Basic Concepts51
I. Introduction52
II. Historical Perspective54
III. Fundamentals and Methods55
A. Linkage analysis55
B. Association analysis59
IV. Seven Stages of Relationship With GW Studies64
V. Contemporary Approaches66
VI. Challenges and Issues69
A. Multiple comparisons and type I and type II errors69
B. Genetic heterogeneity69
Acknowledgments70
References70
Chapter 4: Definition of Phenotype75
I. Introduction76
II. Phenotype Choices/Options78
A. Discrete versus continuous traits78
B. Study base differences78
C. Clinical heterogeneity81
D. Endophenotype87
E. Composite phenotype88
F. Narrow versus broad phenotype definition92
III. Study Design Issues92
A. Measurement error92
B. Misclassification93
C. Loss due to dichotomization/categorization97
D. Effect of narrowing phenotype98
E. Multiple comparisons99
IV. Discussion99
Acknowledgments100
References100
Chapter 5: Genotyping Platforms for Mass-Throughput Genotyping with SNPs, Including Human Genome-Wid107
I. Introduction: Explosive Need for Mass-Throughput Genotyping108
II. Microsatellites Versus SNPs110
A. Microsatellites110
B. SNPs111
C. SNPs in whole-genome association studies112
D. SNPs in whole-genome linkage scans113
III. Genotyping Platforms and Goals115
A. Challenges to mass-throughput genotyping115
B. Genome-Wide platforms117
C. Targeted genotyping platforms123
IV. Selection of Markers and SNPs126
A. Selection of genome-wide platforms126
B. Selection of targeted platforms130
V. Novel Markers130
VI. Future Prospects133
A. SNP-typing platforms in the clinic134
B. Integrative array-based genomic analyses135
VII. Conclusions135
References136
Chapter 6: Genotyping Errors and Their Impact on Genetic Analysis141
I. Introduction142
II. Misspecification of Genetic Relationships143
A. Dissociation of marker data from the individual ID143
B. Inaccurate information about familial relations143
C. Errors in data entry144
D. Related families specified as unrelated144
III. Detection and Correction of Misspecification of Relationships144
A. GRR144
B. ASPEX147
C. Eclipse147
IV. Genotype Misclassification147
A. Misclassification rates148
B. Mendelian inconsistencies148
C. Unlikely double recombinants149
D. Allele shifting150
V. Conclusion151
References151
Part II: Linkage and Association Analysis153
Chapter 7: Model-Based Methods for Linkage Analysis155
I. Introduction156
II. The Generalized Single Major Locus Model Description156
III. The Lod Score157
A. Definition157
B. Examples157
C. The LOD score of 3 criterion160
D. Genome-wide significance level161
E. Affected sib pair methods and LOD scores162
F. Maximum LOD score for ASP163
G. Maximum LOD score versus the LOD score164
IV. The Mod Score Method165
V. The Lod Score and Meta-Analysis166
A. SML traits166
B. Complex traits167
VI. Multilocus Models168
VII. Strengths and Weaknesses of Model-Based Methods170
VIII. Discussion170
References171
Chapter 8: Contemporary Model-Free Methods for Linkage Analysis175
I. Introduction176
II. Identity by Descent and Identity by State177
A. Multipoint IBD179
B. IBD estimation and intermarker linkage disequilibrium180
III. Relative Pair Linkage Methods180
A. Discordant pairs182
B. Covariates in affected sib pair analyses183
C. Power186
IV. Variance Components Linkage Methods184
A. Nonnormality of the trait distribution185
B. Discrete and categorical traits186
C. Power186
V. Strengths and Weaknesses of Model-Free Linkage Methods187
VI. Conclusion189
Acknowledgments189
References189
Chapter 9: DNA Sequence-Based Phenotypic Association Analysis195
I. Introduction196
A. DNA sequencing and association studies198
B. Potential analysis methods199
II. Multivariate Distance Matrix Regression202
A. Computing a distance matrix202
B. MDMR analysis204
C. Assessing significance of the F statistic205
D. Graphical display of distance/similarity matrices206
III. Simulation Studies206
A. The determination of critical values207
B. The assessment of the power of MDMR207
IV. Results209
A. The determination of critical values209
B. The assessment of the power of MDMR: Equal effect sizes211
C. The assessment of the power of MDMR: Diminishing effect sizes213
D. The assessment of the power of MDMR: The influence of neutral loci213
V. Discussion213
Acknowledgments215
References215
Chapter 10: Family-Based Methods for Linkage and Association Analysis219
I. Introduction220
A. Hypothesis testing in family designs220
B. The TDT test for trios222
C. Extensions to the TDT223
D. Design issues224
II. Analysis Methods: FBAT and PBAT228
A. General test statistic230
B. Coding the genotype230
C. Coding the trait: Dichotomous outcomes231
D. The test statistic: Large sample distribution under the null231
E. The TDT and chi2FBAT232
F. Computing the distribution with general pedigrees and/or missing founders233
G. Haplotypes and multiple markers234
H. Coding the trait for complex phenotypes: Age-to-onset phenotypes, quantitative outcomes, and FBAT236
I. A General approach to complex phenotypes: Separating the population and family information in fam238
J. Testing strategies for large-scale association studies240
III. Other Approaches to Family-Based Analyses, Including the PDT and the QTDT240
A. The PDT and APL244
B. Quantitative traits: The QTDT245
IV. Software246
V. Discussion246
References248
Chapter 11: Searching for Additional Disease Loci in a Genomic Region253
I. Introduction254
A. Background254
B. Primary disease genes257
C. Additional (secondary) disease genes in a genetic region258
II. Primary Disease-Predisposing Genes and Their Genetic Features261
A. Background261
B. Linkage and association tests262
C. Modes of inheritance (recessive versus additive models)264
D. The patient/control ratio267
E. Relative predispositional effects268
III. Detecting Additional Genes in a Genetic Region271
A. Background271
B. Linkage analyses of two linked disease susceptibility genes271
C. Linkage disequilibrium of markers with primary disease genes272
D. Matched cases and controls273
E. Homozygous parent linkage and TDT methods274
F. Conditional haplotype methods275
G. Conditional genotype methods279
I. Combining association and IBD values281
IV. Discussion283
Acknowledgments285
References285
Chapter 12: Methods for Handling Multiple Testing293
I.Introduction294
II. Types of Errors in Hypothesis Testing (Type I and Type II)296
III. Striking a Balance Between False Positives and False Negatives298
IV. Alternative Adjustment Methods299
A. The Bonferroni correction299
B. Permutation testing300
C. False discovery rate301
D. Sequential methods302
E. Other methods304
V. Conclusion305
Acknowledgments305
References305
Part III: Special Topics309
Chapter 13: Meta-Analysis Methods311
I. Introduction312
II. Meta-Analysis of Population-Based Association Studies313
A. Background considerations313
B. Special issues in genetic applications318
III. Meta-Analysis of Linkage Studies319
A. Meta-analysis of significance levels320
B. Parametric meta-analysis for linkage studies322
C. Meta-analysis of genome scans322
IV. Special Issues324
A. Nonreplication of early genetic claims324
B. Biases in meta-analyses, with emphasis on genetic meta-analysis326
C. Meta-analyses of individual participant data and consortia of investigators327
D. Meta-analysis of genome-wide association studies327
E. Meta-analysis of gene-gene-environment data328
References329
Chapter 14: Haplotype-Association Analysis335
I. Introduction336
II. Haplotype Inference From Unrelated Individuals337
A. Statistical methods338
B. Combinatorial algorithms342
C. Some extensions and other methods345
III. Haplotype Inference From Pedigrees349
A. Brief introduction for haplotype inference using pedigrees349
B. Rule-based methods for haplotype inference using pedigrees350
C. Likelihood-based methods for haplotype inference using pedigrees355
D. The program packages362
IV. Population-Based Haplotype-Association Methods362
A. Score statistics for haplotype analysis362
B. Haplotype-association analysis using regression models367
V. Family-Based Association Methods Using Haplotypes376
A. Brief introduction376
B. Family-based association methods using single markers377
C. Methods for phase known data379
D. Methods for phase unknown data382
E. The program packages391
VI. Discussion391
Acknowledgments393
References393
Chapter 15: Characterization of LD Structures and the Utility of HapMap in Genetic Association Studi407
I. Introduction408
II. Haplotype Similarity in Candidate Genes and LD Mapping409
A. Founder heterogeneity410
B. Multiple candidate genes and global test411
C. Other practical issues413
III. Global Organization of LD Structures414
A. HapMap and tagSNPs415
B. Similarity of HapMaps and transferability of tagSNPs418
C. Implications to GWA418
IV. Variation of Local LD Structures420
A. The marker ambiguity score method421
B. Implications for GWA studies423
V. Discussion425
Acknowledgment429
References430
Chapter 16: Associations Among Multiple Markers and Complex Disease: Models, Algorithms, and Applica437
I. Introduction438
II. A Model for "Diplotypes"604
A. Independence and the multinomial model442
B. Strand-specific probabilities and resampling442
III. Strand-Specific Inference in the Absence of Randomness in P446
IV. Haploview and Our View604
A. Testing: Individual pairs of alleles and omnibus tests448
B. Clustering452
V. Approaches to Two-Class Association/Classification454
VI. Restrictions on Probabilities Entailed by Hardy-Weinberg Equilibrium459
VII. Discussion and Summary460
Acknowledgments462
References462
Chapter 17: Study Designs for Genome-Wide Association Studies465
I. Introduction466
II. Basic Principles of Association Design471
A. Retrospective case-control studies472
B. Prospective cohort and nested case-control studies476
C. Continuous phenotypes477
D. Using genome-wide SNP data to adjust for population stratification and other biases478
E. Family-based designs479
III. The Genetic Architecture of Complex Traits480
IV. Genotyping Technologies482
V. Power Calculations for Multistage Design485
A. Appropriate significance thresholds for power calculations485
B. Power and cost calculations for multistage designs487
VI. Discussion493
References495
Chapter 18: Ethical, Legal, and Social Implications of Biobanks for Genetics Research505
I. Introduction506
II. Governance of Biobanks507
III. Risks and Benefits512
A. Individual risks and potential benefits513
B. Societal risks and benefits514
IV. Recruitment of Vulnerable and Minority Populations516
V. Informed Consent518
A. Approaches to biobank consents519
B. Tiered consent520
C. General consent521
VI. Storage of Genetic Information„Optimizing Privacy522
VII. Biospecimen/Data Access524
VIII. Ownership and Intellectual Property525
IX. Disclosure of Research Results527
A. Individual research results527
B. Aggregate research results534
X. Commercialization of Biobanks536
XI. Conclusion538
References539
Part IV: Promising Topics545
Chapter 19: Admixture Mapping and the Role of Population Structure for Localizing Disease Genes547
I. Introduction548
II. Population Genetic Structure in Humans549
III. Population Admixture550
IV. Methods of Admixture Mapping551
A. The basic idea551
B. Test statistics553
C. Inferring locus-specific ancestry557
D. Genome-wide significance level558
E. Software available560
V. Design Consideration561
A. Markers for admixture mapping561
B. Power and sample size562
VI. Prospects and Concerns of Admixture Mapping563
VII. Conclusions565
Acknowledgment566
References566
Chapter 20: Integrating Global Gene Expression Analysis and Genetics571
I. Introduction572
II. Technical and Experimental Design Issues for Microarrays575
A. DNA microarray platforms576
B. Microarray data analysis581
III. Identifying Disease Candidates Using DNA Microarrays582
A. Gene expression catalogs582
B. Differential gene expression in disease583
C. Functional annotation of gene expression patterns584
D. Identification of disease biomarkers586
IV. Integration of Genetics and Genomics586
A. Mapping gene expression QTL588
B. Prioritizing candidate genes590
C. Modeling causal interactions592
D. Gene coexpression networks592
E. Genetical genomics in human studies594
V. Conclusions596
References597
Chapter 21: A Systems Biology Approach to Drug Discovery603
I. Introduction604
II. Causal Inference: An Integrated Approach to Drug Discovery606
III. Coexpression Networks612
A. Constructing weighted and unweighted coexpression networks604
B. Using genetics in constructing coexpression networks614
C. Identifying modules of highly interconnected genes in coexpression networks615
IV. Probabilistic Causal Networks: Bayesian Networks as a Framework for Data Integration617
A. Bayesian networks618
B. Deriving structure priors from genetic data619
C. Structure priors derived from other data sources622
V. An Insightful Example: Integrating Data Leads to the Identification of Gene Affecting Plasma Chol625
VI. Conclusions604
References630
Chapter 22: The Promise of Composite Likelihood Methods for Addressing Computationally Intensive Cha637
I. Introduction638
II. Composite Likelihood Methods638
III. Applications in Population Genetics641
IV. Applications in Fine Mapping of Disease Mutations643
A. Terwilliger's method644
B. Devlin et al.'s method645
C. Maleacutecot model for linkage disequilibrium646
D. Other methods648
V. Other Applications649
VI. Prospectives and Discussion650
References652
Chapter 23: Comparative Genomics for Detecting Human Disease Genes655
I. Introduction656
II. The Power and Promise of Comparative Genomics659
A. Characterization of genes and their regulation660
III. Animal Models for Human Disease665
A. QTL to gene666
B. Sequence to function671
C. Comparing phenomes674
IV. Comparative Genomics and Building Better Animal Models676
A. Transgenesis and mutagenesis676
B. Humanizing disease models678
C. Comparative QTL679
D. Genes positionally cloned680
V. Discussion681
References683
Part V: Outstanding Challenges699
Chapter 24: From Genetics to Mechanism of Disease Liability701
I. Introduction702
II. Identification of QTL by Statistical Analysis703
A. QTL mapping in human genetics703
B. Mapping determinants of essential hypertension in humans703
C. The predominant role of the kidney in EH704
D. From QTL to genes in rodents705
E. Few genes, if any, explaining a blood pressure QTL have been identified by the traditional approa706
III. A Strong QTL For Sodium Sensitivity in the C57BL/6J Mouse Inbred Line707
A. QTL mapping of NaS between C57BL/6J and A/J707
B. Combining data for the chromosome 4 QTL708
C. A QTL that accounts for a small proportion of the total variance of a quantitative phenotype of l708
IV. From QTL to Genes: Gene Prioritization713
A. Gene prioritization based on differential expression in select tissues or cells714
B. Gene prioritization based on in silico search of functional annotations and genomic differences a716
V. From QTL to Genes: High-Throughput Functional Screens721
A. The ultimate need for a functional proof721
B. The power of high-throughput functional screens721
C. Functional analysis of sodium transport in epithelial cells of the kidney722
VI. Perspective723
References723
Chapter 25: Into the Post-HapMap Era727
I. Introduction728
II. Linkage Mapping and Cytogenetic Assignment729
III. Association Mapping and Functional Assignment730
IV. Fully Parametric Analysis732
V. Meta-Analysis732
VI. Analysis of msSNPs733
VII. The False Discovery Rate734
VIII. Early-HapMap Projects735
IX. Genome-Wide Association Scans737
X. What Next?738
References740
Index743

Book details

Vendor Elsevier S & T
SKU 9780123738837
ISBN-13 9780080569116
Author Rao, D.C.; Gu, C. Charles
Edition 2nd
Category Medical
Subject Genetics

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The field of genetics is rapidly evolving and new medical breakthroughs are occuring as a result of advances in knowledge of genetics. This series continually publishes important reviews of the broadest interest to geneticists and their colleagues in affiliated disciplines.

* Five sections on the latest advances in complex traits
* Methods for testing with ethical, legal, and social implications
* Hot topics include discussions on systems biology approach to drug discovery; using comparative genomics for detecting human disease genes; computationally intensive challenges, and more

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