A Statistical Approach to Genetic Epidemiology

This is the second edition of the successful textbook written by the prize-winning scientist Andreas Ziegler, former President of the German Chapter of the International Biometric Society, and Inke König, who has been teaching the subject over many years.
The book gives a comprehensive introduction into the relevant statistical methods in genetic epidemiology. The second edition is thoroughly revised, partly rewritten and includes now chapters on segregation analysis, twin studies and estimation of heritability. The book is ideally suited for advanced students in epidemiology, genetics, statistics, bioinformatics and biomathematics.
Like in the first edition the book contains many problems and solutions and it comes now optionally with an e-learning course created by Friedrich Pahlke. This e-learning course has been developed to complement the book. Both provide a unique support tool for teaching the subject.

Auteur
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.

Résumé
A Statistical Approach to Genetic Epidemiology After studying statistics and mathematics at the University of Munich and obtaining his doctoral degree from the University of Dortmund, Andreas Ziegler received the Johann-Peter-Süssmilch-Medal of the German Association for Medical Informatics, Biometry and Epidemiology for his post-doctoral work on Model Free Linkage Analysis of Quantitative Traits in 1999. In 2004, he was one of the recipients of the Fritz-Linder-Forum-Award from the German Association for Surgery.

Contenu
Foreword to the First Edition vii

Foreword to the Second Edition viii

Preface xi

Acknowledgments xv

1 Molecular Genetics 1

1.1 Genetic information 2

1.1.1 Location of genetic information 2

1.1.2 Interpretation of genetic information 5

1.1.3 Translation of genetic information 5

1.2 Transmission of genetic information 7

1.3 Variations in genetic information 10

1.3.1 Individual differences in genetic information 10

1.3.2 Detection of variations 12

1.3.3 Probability for detection of variations 16

1.4 Problems 18

2 Formal Genetics 21

2.1 Mendel and his laws 22

2.2 Segregation patterns 23

2.2.1 Autosomal dominant inheritance 24

2.2.2 Autosomal recessive inheritance 25

2.2.3 X-chromosomal dominant inheritance 26

2.2.4 X-chromosomal recessive inheritance 27

2.2.5 Y-chromosomal inheritance 28

2.3 Complications of Mendelian segregation 28

2.3.1 Variable penetrance and expression 29

2.3.2 Age-dependent penetrance 31

2.3.3 Imprinting 33

2.3.4 Phenotypic and genotypic heterogeneity 35

2.3.5 Complex diseases 36

2.4 Hardy-Weinberg law 38

2.5 Problems 43

3 Genetic Markers 47

3.1 Properties of genetic markers 47

3.2 Types of genetic markers 52

3.2.1 Short tandem repeats (STRs) 52

3.2.2 Single nucleotide polymorphisms (SNPs) 54

3.3 Genotyping methods for SNPs 57

3.3.1 Restriction fragment length polymorphism analysis 58

3.3.2 Real-time polymerase chain reaction 58

3.3.3 Matrix assisted laser desorption/ionization time of flight genotyping 61

3.3.4 Chip-based genotyping 61

3.3.5 Choice of genotyping method 63

3.4 Problems 65

4 Data Quality 67

4.1 Pedigree errors 68

4.2 Genotyping errors in pedigrees 70

4.2.1 Frequency of genotyping errors 70

4.2.2 Reasons for genotyping errors 71

4.2.3 Mendel checks 72

4.2.4 Checks for double recombinants 74

4.3 Genotyping errors and Hardy-Weinberg equilibrium (HWE) 76

4.3.1 Causes of deviations from HWE 77

4.3.2 Tests for deviation from HWE for SNPs 78

4.3.3 Tests for deviation from HWE for STRs 81

4.3.4 Measures for deviation from HWE 83

4.3.5 Tests for compatibility with HWE for SNPs 86

4.4 Quality control in high-throughput studies 91

4.4.1 Sample quality control 94

4.4.2 SNP quality control 97

4.5 Cluster plot checks and internal validity 98

4.5.1 Cluster compactness measures 101

4.5.2 Cluster connectedness measures 101

4.5.3 Cluster separation measures 101

4.5.4 Genotype stability measures 102

4.5.5 Combinations of criteria 102

4.6 Problems 109

5 Genetic Map Distances 113

5.1 Physical distance 113

5.2 Map distance 114

5.2.1 Distance 114

5.2.2 Specific map functions 115

5.2.3 Correspondence between physical distance and map distance 116

5.2.4 Multilocus feasibility 117

5.3 Linkage disequilibrium distance 118

5.4 Problems 123

6 Family Studies 125

6.1 Family history method and family study method 127

6.2 Familial correlations and recurrence risks 129

6.2.1 Familial resemblance 129

6.2.2 Recurrence risk ratios 131

6.3 Heritability 134

6.3.1 The simple Falconer model 135

6.3.2 The general Falconer model 137

6.3.3 Kinship coefficient and Jacquard's 7 coefficient 138

6.4 Twin and adoption studies 141

6.4.1 Twin studies 141

6.4.2 Adoption studies 142

6.5 Critique on investigating familial resemblance 143

6.6 Segregation analysis 144

6.7 Problems 154

7 Model-Based Linkage Analysis 155

7.1 Linkage analysis between two genetic markers 156

7.1.1 Linkage analysis in phase-known pedigrees 156

7.1.2 Linkage analysis in phase-unknown pedigrees 160

7.1.3 Linkage analysis in pedigrees with missing genotypes 161

7.2 Linkage analysis between a genetic marker and a disease 167

7.2.1 Linkage analysis between a genetic marker and a disease in phase-known pedigrees 168

7.2.2 Linkage analysis between a genetic marker and a disease in general cases 172

7.2.3 Gain in information by genotyping additional individuals; power calculations 177

7.3 Significance levels in linkage analysis 180

7.4 Problems 184

8 Model-Free Linkage Analysis 189

8.1 The principle of similarity 190

8.2 Mathematical foundation of affected sib-pair analysis 192

8.3 Common tests for affected sib-pair analysis 193

8.3.1 The maximum LOD score and the triangle test 194

8.3.2 Score- and Wald-type 1 degree of freedom tests 201

8.3.3 Affected sib-pair tests using alleles shared identical by state 206

8.4 Properties of affected sib-pair tests 206

8.5 Sample size and power calculations for affected sib-pair studies 207

8.5.1 Functional relation between identical by descent probabilities and recurrence risk ratios 207

8.5.2 Sample size and power calculations for the mean test using recurrence risk ratios 209

8.6 Extensions to multiple marker loci 212

8.7 Extension to large sibships 213

8.8 Extension to large pedigrees 214

8.9 Extensions of the affected sib-pair approach 216

8.9.1 Covariates in affected sib-pair analyses 216

8.9.2 Multiple disease loci in affected sib-pair …