Statistical Approach to Genetic Epidemiology

Andreas Ziegler is head of the Institute for Medical Biometry and Statistics at the University Clinic Schleswig-Holstein in Lubeck, an acknowledged center of excellence for genetic epidemiological methods. Currently he is President of the German Region of the International Biometric Society. Inke R. Konig studied psychology at the universities of Marburg (Germany) as a scholar of the German National Academic Foundation and Dundee (Scotland) with a grant from the German Academic Exchange Service (DAAD). She has done research work at the Institute of Medical Biometry and Epidemiology in Marburg and since 2001 at the Institute of Medical Biometry and Statistics in Lubeck. In 2004, she became vice director of the latter and also received the Fritz-Linder-Forum-Award from the German Association for Surgery. Besides holding the certificate “Biometrics in Medicine”, she has collected teaching experience since 1998 as a lecturer for biomathematics, behavioural genetics, clinical epidemiology, genetic epidemiology, and evidence-based medicine. Friedrich Pahlke is Dipl. Inf. at the Institute for Medical Biometry and Statistics at the University Clinic Schleswig-Holstein in Lubeck. He has created the e-learning course which is optionally available with the book.

“This is a well-written, quality addition to the literature. It is an excellent resource/textbook for those wanting to teach genetic epidemiology as well as those wishing to learn the basics of genetic epidemiology. The new edition improves on the previous edition and expands on necessary topics that have grown in importance over the last five years.”  (Doody’s, 4 January 2013)

• Foreword to the First Edition viiForeword to the Second Edition viiiPreface xiAcknowledgments xv1 Molecular Genetics 11.1 Genetic information 21.1.1 Location of genetic information 21.1.2 Interpretation of genetic information 51.1.3 Translation of genetic information 51.2 Transmission of genetic information 71.3 Variations in genetic information 101.3.1 Individual differences in genetic information 101.3.2 Detection of variations 121.3.3 Probability for detection of variations 161.4 Problems 182 Formal Genetics 212.1 Mendel and his laws 222.2 Segregation patterns 232.2.1 Autosomal dominant inheritance 242.2.2 Autosomal recessive inheritance 252.2.3 X-chromosomal dominant inheritance 262.2.4 X-chromosomal recessive inheritance 272.2.5 Y-chromosomal inheritance 282.3 Complications of Mendelian segregation 282.3.1 Variable penetrance and expression 292.3.2 Age-dependent penetrance 312.3.3 Imprinting 332.3.4 Phenotypic and genotypic heterogeneity 352.3.5 Complex diseases 362.4 Hardy–Weinberg law 382.5 Problems 433 Genetic Markers 473.1 Properties of genetic markers 473.2 Types of genetic markers 523.2.1 Short tandem repeats (STRs) 523.2.2 Single nucleotide polymorphisms (SNPs) 543.3 Genotyping methods for SNPs 573.3.1 Restriction fragment length polymorphism analysis 583.3.2 Real-time polymerase chain reaction 583.3.3 Matrix assisted laser desorption/ionization time of flight genotyping 613.3.4 Chip-based genotyping 613.3.5 Choice of genotyping method 633.4 Problems 654 Data Quality 674.1 Pedigree errors 684.2 Genotyping errors in pedigrees 704.2.1 Frequency of genotyping errors 704.2.2 Reasons for genotyping errors 714.2.3 Mendel checks 724.2.4 Checks for double recombinants 744.3 Genotyping errors and Hardy–Weinberg equilibrium (HWE) 764.3.1 Causes of deviations from HWE 774.3.2 Tests for deviation from HWE for SNPs 784.3.3 Tests for deviation from HWE for STRs 814.3.4 Measures for deviation from HWE 834.3.5 Tests for compatibility with HWE for SNPs 864.4 Quality control in high-throughput studies 914.4.1 Sample quality control 944.4.2 SNP quality control 974.5 Cluster plot checks and internal validity 984.5.1 Cluster compactness measures 1014.5.2 Cluster connectedness measures 1014.5.3 Cluster separation measures 1014.5.4 Genotype stability measures 1024.5.5 Combinations of criteria 1024.6 Problems 1095 Genetic Map Distances 1135.1 Physical distance 1135.2 Map distance 1145.2.1 Distance 1145.2.2 Specific map functions 1155.2.3 Correspondence between physical distance and map distance 1165.2.4 Multilocus feasibility 1175.3 Linkage disequilibrium distance 1185.4 Problems 1236 Family Studies 1256.1 Family history method and family study method 1276.2 Familial correlations and recurrence risks 1296.2.1 Familial resemblance 1296.2.2 Recurrence risk ratios 1316.3 Heritability 1346.3.1 The simple Falconer model 1356.3.2 The general Falconer model 1376.3.3 Kinship coefficient and Jacquard’s Δ7 coefficient 1386.4 Twin and adoption studies 1416.4.1 Twin studies 1416.4.2 Adoption studies 1426.5 Critique on investigating familial resemblance 1436.6 Segregation analysis 1446.7 Problems 1547 Model-Based Linkage Analysis 1557.1 Linkage analysis between two genetic markers 1567.1.1 Linkage analysis in phase-known pedigrees 1567.1.2 Linkage analysis in phase-unknown pedigrees 1607.1.3 Linkage analysis in pedigrees with missing genotypes 1617.2 Linkage analysis between a genetic marker and a disease 1677.2.1 Linkage analysis between a genetic marker and a disease in phase-known pedigrees 1687.2.2 Linkage analysis between a genetic marker and a disease in general cases 1727.2.3 Gain in information by genotyping additional individuals; power calculations 1777.3 Significance levels in linkage analysis 1807.4 Problems 1848 Model-Free Linkage Analysis 1898.1 The principle of similarity 1908.2 Mathematical foundation of affected sib-pair analysis 1928.3 Common tests for affected sib-pair analysis 1938.3.1 The maximum LOD score and the triangle test 1948.3.2 Score- and Wald–type 1 degree of freedom tests 2018.3.3 Affected sib-pair tests using alleles shared identical by state 2068.4 Properties of affected sib-pair tests 2068.5 Sample size and power calculations for affected sib-pair studies 2078.5.1 Functional relation between identical by descent probabilities and recurrence risk ratios 2078.5.2 Sample size and power calculations for the mean test using recurrence risk ratios 2098.6 Extensions to multiple marker loci 2128.7 Extension to large sibships 2138.8 Extension to large pedigrees 2148.9 Extensions of the affected sib-pair approach 2168.9.1 Covariates in affected sib-pair analyses 2168.9.2 Multiple disease loci in affected sib-pair analyses 2168.9.3 Estimating the position of the disease locus in affected sib-pair analyses 2178.9.4 Typing unaffected relatives in sib-pair analyses 2178.10 Problems 2189 Quantitative Traits 2219.1 Quantitative versus qualitative traits 2229.2 The Haseman–Elston method 2239.2.1 The expected squared phenotypic difference at the trait locus 2259.2.2 The expected squared phenotypic difference at the marker locus 2279.3 Extensions of the Haseman–Elston method 2299.3.1 Double squared trait difference 2309.3.2 Extension to large sibships 2309.3.3 Haseman–Elston revisited and the new Haseman–Elston method 2319.3.4 Power and sample size calculations 2349.4 Variance components models 2379.4.1 The univariate variance components model 2379.4.2 The multivariate variance components model 2389.5 Random sib-pairs, extreme probands and extreme sib-pairs 2409.6 Empirical determination of p-values 2439.7 Problems 24510 Fundamental Concepts of Association Analyses 24710.1 Introduction to association 24710.1.1 Principles of association 24710.1.2 Study designs for association 24910.2 Linkage disequilibrium 25010.2.1 Allelic linkage disequilibrium 25010.2.2 Genotypic linkage disequilibrium 25510.2.3 Extent of linkage disequilibrium 25910.3 Problems 26211 Association Analysis in Unrelated Individuals 26511.1 Selection of cases and controls 26611.2 Tests, estimates, and a comparison 26611.2.1 Association tests 26711.2.2 Choice of a test in applications 27211.2.3 Effect measures 27411.2.4 Selection of the genetic model 28011.2.5 Association tests for the X chromosome 28711.3 Sample size calculation 28911.4 Population stratification 29111.4.1 Testing for population stratification 29311.4.2 Structured association 29411.4.3 Genomic control 29511.4.4 Comparison of structured association and genomic control 29711.4.5 Principal components analysis 29711.5 Gene-gene and gene-environment interaction 29911.5.1 Classical examples for gene-gene and gene-environment interaction 29911.5.2 Coat color in the Labrador retriever 30111.5.3 Concepts of interaction 30311.5.4 Statistical testing of gene-environment interactions 30711.5.5 Statistical testing of gene-gene interactions 31111.5.6 Multifactor dimensionality reduction 31511.6 Problems 31612 Family-based Association Analysis 31912.1 Haplotype relative risk 32012.2 Transmission disequilibrium test (TDT) 32212.3 Risk estimates for trio data 32512.4 Sample size and power calculations for the TDT 32712.5 Alternative test statistics 32912.6 TDT for multiallelic markers 33012.6.1 Test of single alleles 33012.6.2 Global test statistics 33112.7 TDT type tests for different family structures 33312.7.1 TDT type tests for missing parental data 33412.7.2 TDT type tests for sibship data 33612.7.3 TDT type tests for extended pedigrees 34112.8 Association analysis for quantitative traits 34412.9 Problems 34613 Haplotypes in Association Analyses 34913.1 Reasons for studying haplotypes 35013.2 Inference of haplotypes 35113.2.1 Algorithms for haplotype assignment 35213.2.2 Algorithms for estimating haplotype probabilities 35313.3 Association tests using haplotypes 35613.4 Haplotype blocks and tagging SNPs 35913.4.1 Selection of markers by haplotypes or linkage disequilibrium 36013.4.2 Evaluation of marker selection approaches 36313.5 Problems 36414 Genome-wide Association (GWA) Studies 36714.1 Design options in GWA studies 36914.2 Genotype imputation 37014.2.1 Imputation algorithms 37014.2.2 Quality of imputation 37114.3 Statistical analysis of GWA studies 37214.4 Multiple testing 37414.4.1 Region-wide multiple testing adjustment by simulation 37514.4.2 Genome-wide multiple testing adjustment by simulation 37614.4.3 Multiple testing adjustment by effective number of tests 37714.5 Analysis of accumulating GWA data 37814.5.1 Multistage designs for GWA studies 37814.5.2 Replication in GWA studies 37914.5.3 Meta-analysis of GWA studies 38014.6 Clinical impact of a GWA study 38314.6.1 Evaluation of a genetic predictive test 38314.6.2 Clinical validity of a single genetic marker 38514.6.3 Clinical validity of multiple genetic markers 38614.7 Outlook 38914.8 Problems 391AppendixAlgorithms Used in Linkage Analyses 393A.1 The Elston–Stewart algorithm 394A.1.1 The fundamental ideas of the Elston–Stewart algorithm 394A.1.2 The Elston–Stewart algorithm for a trait and a linked marker locus 400A.2 The Lander–Green algorithm 401A.2.1 The inheritance vector at a single genetic marker 401A.2.2 The inheritance distribution given all genetic markers 405A.3 The Cardon–Fulker algorithm 412A.4 Problem 414Solutions 415References 451Index 489