A statistical approach to genetic epidemiology: concepts and applications
Gespeichert in:
| Beteiligte Personen: | , |
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| Format: | Buch |
| Sprache: | Englisch |
| Veröffentlicht: |
Weinheim
Wiley-VCH
2006
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| Schlagwörter: | |
| Links: | http://deposit.dnb.de/cgi-bin/dokserv?id=2708213&prov=M&dok_var=1&dok_ext=htm http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=014726949&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| Beschreibung: | Literaturverz. S. 305 - 328 |
| Umfang: | XXVI, 335 S. Ill., graph. Darst. |
| ISBN: | 3527312528 9783527312528 |
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| 245 | 1 | 0 | |a A statistical approach to genetic epidemiology |b concepts and applications |c Andreas Ziegler and Inke R. König |
| 264 | 1 | |a Weinheim |b Wiley-VCH |c 2006 | |
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| 500 | |a Literaturverz. S. 305 - 328 | ||
| 650 | 4 | |a Biometry | |
| 650 | 4 | |a Biometry |x methods | |
| 650 | 4 | |a Epidemiology, Molecular | |
| 650 | 4 | |a Molecular epidemiology | |
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Datensatz im Suchindex
| _version_ | 1819289691725234176 |
|---|---|
| adam_text | Contents
Foreword vii
Preface ix
Acknowledgments xi
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 11
1.4 Problems 15
2 Formal Genetics 17
2.1 Mendel and his laws 18
2.2 Segregation patterns 19
2.2.1 Autosomal dominant inheritance 20
A Statistical Approach to Genetic Epidemiology. Andreas Ziegler and Inke R. Konig
Copyright © 2006 WILEY VCH Verlag GmbH Co. KGaA, Weinheim
ISBN: 3 527 31252 8
2.2.2 Autosomal recessive inheritance 21
2.2.3 X chromosomal dominant inheritance 22
2.2.4 X chromosomal recessive inheritance 23
2.2.5 Y chromosomal inheritance 24
2.3 Complications of Mendelian segregation 24
2.3.1 Variable penetrance and expression 25
2.3.2 Age dependent penetrance 27
2.3.3 Imprinting 29
2.3.4 Phenotypic and genotypic heterogeneity 31
2.3.5 Complex diseases 32
2.4 Hardy Weinberg law 34
2.5 Problems 39
3 Genetic Markers 43
3.1 Properties of genetic markers 43
3.2 Types of genetic markers 48
3.2.1 Short tandem repeats (STRs) 48
3.2.2 Single nucleotide polymorphisms (SNPs) 50
3.3 Problems 52
4 Data Quality 55
4.1 Pedigree errors 55
4.2 Genotyping errors in pedigrees 56
4.2.1 Frequency of genotyping errors 57
4.2.2 Reasons for genotyping errors 58
4.2.3 Mendel checks 59
4.2.4 Checks for double recombinants 61
4.3 Genotyping errors in population based studies 63
4.3.1 Causes of deviations from Hardy Weinberg
equilibrium 63
4.3.2 Tests for deviation from Hardy Weinberg
equilibrium for SNPs 64
4.3.3 Tests for deviation from Hardy Weinberg
equilibrium for STRs 67
4.3.4 Tests for compatibility with Hardy Weinberg
for SNPs 69
4.4 Problems 72
CONTENTS XV
5 Genetic Map Distances 75
5.1 Physical distance 75
5.2 Map distance 76
5.2.1 Distance 76
5.2.2 Specific map functions 77
5.2.3 Correspondence between physical distance
and map distance 78
5.2.4 Multilocus feasibility 79
5.3 Radiation hybrid distance 80
5.4 Linkage disequilibrium distance 81
5.5 Problems 86
6 Model based Linkage Analysis 89
6.1 Linkage analysis between two genetic markers 90
6.1.1 Linkage analysis in phase known pedigrees 90
6.1.2 Linkage analysis in phase unknown pedigrees 94
6.1.3 Linkage analysis in pedigrees with missing
genotypes 95
6.2 Linkage analysis between a genetic marker and a
disease 101
6.2.1 Linkage analysis between a genetic marker
and a disease in phase known pedigrees 101
6.2.2 Linkage analysis between a genetic marker
and a disease in general cases 105
6.2.3 Gain in information by genotyping additional
individuals; power calculations 110
6.3 Significance levels in linkage analysis 112
6.4 Problems 116
7 Model free Linkage Analysis 121
7.1 The principle of similarity 122
7.2 Mathematical foundation of affected sib pair analysis 124
7.3 Common tests for affected sib pair analysis 125
7.3.1 The maximum LOD score and the triangle test 126
7.3.2 Score and Wald type one degree of freedom
tests 131
7.3.3 Affected sib pair tests using alleles shared
identical by state 136
7.4 Properties of affected sib pair tests 137
7.5 Sample size and power calculations for affected
sib pair studies 139
7.5.1 Recurrence risk ratios 139
7.5.2 Relationship between recurrence risk ratios
and IBD probabilities 142
7.5.3 Sample size and power calculations for the
mean test using recurrence risk ratios 144
7.6 Extensions to multiple marker loci 146
7.7 Extension to large sibships 148
7.8 Extension to large pedigrees 149
7.9 Extensions of the affected sib pair approach 150
7.9.1 Covariates in affected sib pair analyses 151
7.9.2 Multiple disease loci in affected sib pair
analyses 151
7.9.3 Estimating the position of the disease locus in
affected sib pair analyses 151
7.9.4 Typing unaffected relatives in sib pair analyses 152
7.10 Problems 153
8 Quantitative Traits 155
8.1 Quantitative versus qualitative traits 156
8.2 The Haseman Elston method 157
8.2.1 The simple Falconer model 159
8.2.2 The expected squaredphenotypic difference at
the trait locus 161
8.2.3 The expected squared phenotypic difference at
the marker locus 163
8.3 Extensions of the Haseman Elston method 167
8.3.1 Double squared trait difference 167
8.3.2 Extension to large sibships 168
8.3.3 Haseman Elston revisited and the new
Haseman Elston method 169
8.3.4 Power and sample size calculations 173
8.3.5 Further extensions of the Haseman Elston
method 176
8.4 Variance component models 177
8.4.1 The univariate variance component model 177
8.4.2 The multivariate variance component model 178
8.5 Random sib pairs, extreme probands and extreme
sib pairs 181
12 Haplotypes in Association Analyses 243
12.1 Reasons for studying haplotypes 244
12.2 Inference of haplotypes 245
12.2.1 Algorithms for haplotype assignment 245
12.2.2 Algorithms for estimating haplotype
probabilities 247
12.3 Association tests using haplotypes 250
12.4 Haplotype blocks and tagging SNPs 252
12.4.1 Selection of markers by haplotypes 254
12.4.2 Selection of markers by linkage disequilibrium 257
12.4.3 Evaluation of marker selection approaches 259
12.5 Problems 261
Appendix A Algorithms Used in Linkage Analyses 263
A.I The Lander Green algorithm 263
A. 1.1 The inheritance vector at a single genetic
marker 264
A.1.2 The inheritance distribution given all genetic
markers 268
A.2 The Cardon Fulker algorithm 274
A.3 Problems 276
Solutions 277
References 305
Index 329
List of Figures
1.1 Schematic structure of the DNA 3
1.2 Karyogram of a healthy male 4
1.3 Schematic structure of a chromosome 4
1.4 Codon table to translate RNA into amino acid 6
1.5 Six stages of the meiotic process 8
1.6 Recombination between two chromosomal segments 9
1.7 Sequencing of DNA segments 12
1.8 Automated DNA sequencing using fluorescent primers 13
1.9 Polymerase chain reaction (PCR) 14
1.10 Recombination between five chromosomal segments 16
2.1 Conventional symbols for displaying pedigrees 18
2.2 Mendel s law of independent assortment 19
2.3 Pedigree with autosomal dominant inheritance 21
2.4 Pedigree with autosomal recessive inheritance 21
2.5 Pedigree with X chromosomal dominant inheritance 22
A Statistical Approach to Genetic Epidemiology. Andreas Ziegler and Inke R. Konig
Copyright © 2006 WILEY VCH Verlag GmbH Co. KGaA, Weinheim
ISBN: 3 527 31252 8
CONTENTS XVii
8.6 Empirical determination ofp values 184
8.7 Problems 185
9 Fundamental Concepts of Association Analyses 187
9.1 Introduction to association 187
9.1.1 Principles of association 187
9.1.2 Study designs for association 189
9.2 Linkage disequilibrium 190
9.2.1 Measures for linkage disequilibrium 190
9.2.2 Extent of linkage disequilibrium 194
9.3 Problems 197
10 Association Analysis in Unrelated Individuals 199
10.1 Selection of cases and controls 200
10.2 Tests and estimates 200
10.3 Sample size calculation 205
10.4 Population stratification 208
10.4.1 Testing for population stratification 210
10.4.2 Structured association 211
10.4.3 Genomic control 212
10.4.4 Comparison of structured association and
genomic control 213
10.5 Problems 213
11 Family based Association Analysis 215
11.1 Haplotype relative risk 216
11.2 Transmission disequilibrium test (TDT) 217
11.3 Risk estimates for trio data 221
11.4 Sample size and power calculations for the TDT 223
11.5 Alternative test statistics 224
11.6 TDT for multiallelic markers 225
11.6.1 Test of single alleles 226
11.6.2 Global test statistics 226
11.7 TDT type tests for different family structures 228
11.7.1 TDT type tests for missing parental data 229
11.7.2 TDT type tests for sibship data 231
11.7.3 TDT type tests for extended pedigrees 236
11.8 Association analysis for quantitative traits 239
11.9 Problems 241
XX LIST OF FIGURES
2.6 Pedigree with X chromosomal recessive inheritance 23
2.7 Pedigree with Y chromosomal inheritance 24
2.8 De Finetti triangle for genotype frequencies 36
2.9 De Finetti triangle showing population stratification 38
4.1 Pedigree for testing relationships 57
4.2 Stylized examples of short tandem repeat (STR) data 59
4.3 Pedigree with three individuals with known parental
haplotypes 61
4.4 Vasarely chart showing no deviation from Hardy
Weinberg equilibrium (HWE) 68
4.5 Vasarely chart showing deviation from HWE 68
4.6 De Finetti triangle representing genotype distributions 70
5.1 Relation between loci and chromosomal segments 76
5.2 Conversion of the map distance to the recombination
fraction 79
5.3 Haplotypes after n generations 83
5.4 Linkage disequilibrium (LD) probability as a function
of physical distance and swept radius 83
5.5 Intervals used for estimating the LD probability 84
5.6 Heat map 85
5.7 LDmap 85
6.1 Phase known pedigree 91
6.2 Phase known pedigree with haplotypes determined 91
6.3 Phase unknown pedigree 94
6.4 Pedigree with missing genotypes 96
6.5 Kernel of the likelihood function from Example 6.2 99
6.6 Kernel of the likelihood function from Example 6.3 100
6.7 Causal models for genetic linkage 101
6.8 Pedigree with an autosomal dominant disease and a
genetic marker 102
LIST OF FIGURES XXi
6.9 Linkage analysis between an autosomal dominant
disease and a genetic marker; haplotypes determined 103
6.10 Linkage analysis between an autosomal recessive
disease and a genetic marker 104
6.11 Possible offspring genotypes for Figure 6.10 104
6.12 Gain in informativity by typing an individual for an
autosomal dominant disease 110
6.13 Gain in informativity by typing an individual for an
autosomal recessive disease 111
7.1 Identical by descent (IBD) and state values at a single
marker 123
7.2 IBD probabilities as a function of the recombination
fraction 125
7.3 Space of IBD distributions represented as de Finetti
triangle 130
7.4 Multipoint linkage analyses, part 1 of chromosome 16
for type I diabetes data 137
8.1 Illustration of the Haseman Elston method 158
8.2 Trait distribution in the population using the Falconer
model 159
8.3 Histograms for the COAG Perth data as provided for
GAW12 165
8.4 Scatterplots of the Haseman Elston method for the
COAG Perth data 166
8.5 Gain in power for the single proband sib pair design 182
9.1 Causal models for genetic association 188
9.2 Study designs for association analysis with unrelated
individuals 189
9.3 Values ofLD depending on allele frequencies at both
loci 192
9.4 Decay ofLD in successive generations depending on
recombination frequency 195
9.5 Relationship between pairwise LD and physical distance 196
XXll LIST OF FIGURES
11.1 A trio of an affected child with both parents 216
11.2 Construction of a pseudo control from a trio 216
11.3 Three cases with one missing and one heterozygous
parent at a diallelic marker 230
11.4 Example data for calculating sibship transmission
disequilibrium test (TDT) 233
11.5 Example data for calculating the reconstruction
combined TDT 235
11.6 Example data for calculating the pedigree
disequilibrium test 237
11.7 Example trio types ascertained for the calculation of
the Rabinowitz test 241
12.1 Redundant single nucleotide polymorphisms in
haplotype blocks 253
12.2 Example genotype data for Clark s algorithm 261
A.I Pedigrees for illustrating the concept of inheritance
vectors 264
A.2 Graph theoretical approach for determining the
inheritance distribution at a single marker 268
List of Tables
2.1 Penetrances for simple Mendelian inheritance patterns 25
2.2 Penetrances in the ABO blood group system 26
2.3 Genotypic relative risks under the assumption of
phenocopies 26
2.4 Example for penetrances at two loci acting epistatically 32
2.5 Mating frequencies and offspring genotype
probabilities given parental genotypes 35
3.1 Conditional probability for deducing transmitted
allele given mating type 45
3.2 Example of informativeness of different markers 48
3.3 Typology of single nucleotide polymorphisms (SNPs)
and their frequency 51
4.1 Contingency table summarizing genotyping results of
aSNP 65
5.1 Haplotype frequencies at two SNPs 82
5.2 Linkage disequilibrium (LD) patterns in humans 84
6.1 LOD score table for Example 6.2. 98
A Statistical Approach to Genetic Epidemiology. Andreas Ziegler and Inke R. Konig
XXiv CONTENTS
6.2 Linkage method, familial relationship, and crossing
over intensity H4
7.1 Possible IBD values of a sib pair 124
7.2 Sample data for affected sib pair statistics 127
7.3 Estimated IBD probabilities for ten affected sib pairs 128
7.4 Update steps for estimating IBD probabilities 128
7.5 Sample data for affected sib pair statistics 135
7.6 Estimates for Risch s Xs value for diseases 141
7.7 Estimates for Risch s s value for dichotomized
quantitative traits 141
7.8 IBD probability at marker locus given IBD status at
trait locus 143
7.9 Required sample sizes for the mean test at a = 0.0001 145
8.1 Examples of quantitative and related clinically
relevant phenotypes 156
8.2 Squared trait differences and conditional genotype
probabilities 161
8.3 Nominal p valuesfor the Haseman Elston method in
the COAG Perth data 167
8.4 Nominal p values for the improved Haseman Elston
methods in the COAG Perth data 172
8.5 Required samples sizes for linkage analysis based on
the Haseman Elston method 175
8.6 p values for the variance component method in the
COAG Perth data 181
9.1 Genotype frequencies at two loci 190
9.2 Summary ofhaplotype probabilities at two diallelic
markers 191
9.3 LD measures between two diallelic genetic markers 193
9.4 Genotype frequencies of 345 healthy controls at
TNFA 238andILlB 511 192
9.5 Estimated haplotype frequencies for 148 patients at
TNFA 238 and IL1B 511 194
CONTENTS XXV
9.6 Genotyping results at two notional SNPsfor asthma
patients and controls 198
10.1 Observed genotype frequencies for cases and controls
at a diallelic marker 201
10.2 Observed allele frequencies for cases and controls at
a diallelic marker 201
10.3 Genotype frequencies in cases and controls for a
dominant and a recessive genetic model 201
10.4 Observed genotype frequencies in patients and controls 204
10.5 Required sample sizes for detecting an odds ratio at
the marker locus with given allele frequency 206
10.6 Required sample sizes to detect an odds ratio at
the disease locus with a given allele frequencies at
disease and marker locus 207
10.7 Example for case control study in two populations 209
10.8 Genotype frequencies for cases and controls at a
notional marker 214
11.1 Allele frequencies at a diallelic marker for cases and
pseudo controls 216
11.2 Possible parental haplotypes and probabilities for
transmissions and non transmissions 218
11.3 Probabilities of transmissions and non transmissions 219
11.4 Transmission and non transmission probabilities for
the transmission disequilibrium test (TDT) 219
11.5 Frequency table for the TDT 220
11.6 Example for estimating sample size in the TDT based
on previous results 223
11.7 Transmitted and non transmitted number ofalleles 224
11.8 Observed frequencies for transmissions and non
transmissions for a multiallelic marker using affected
children and parents 225
11.9 Exemplary observations of transmissions and non
transmissions at a short tandem repeat 227
XXVi CONTENTS
11.10 Observed genotype counts of the available parent
and the affected offspring for the l transmission
disequilibrium test (1 TDT). 230
11.11 Working table for calculating sibship TDTs 233
11.12 Means and variances under Ho for the number of
alleles in the affected children depending on the family
type 234
11.13 Number of alleles in the affectedsfor the families from
Figure 11.5 236
11.14 Working table for calculating the Rabinowitz test 241
12.1 Exemplary iteration steps in the expectation
maximization algorithm 250
A.I Inheritance vectors for the pedigrees of Figure A.I 266
|
| any_adam_object | 1 |
| author | Ziegler, Andreas 1966- König, Inke R. 1972- |
| author_GND | (DE-588)1038880432 (DE-588)124625088 |
| author_facet | Ziegler, Andreas 1966- König, Inke R. 1972- |
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| author_sort | Ziegler, Andreas 1966- |
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| id | DE-604.BV021510321 |
| illustrated | Illustrated |
| indexdate | 2024-12-20T12:35:16Z |
| institution | BVB |
| isbn | 3527312528 9783527312528 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-014726949 |
| oclc_num | 181498955 |
| open_access_boolean | |
| owner | DE-355 DE-BY-UBR DE-11 DE-578 |
| owner_facet | DE-355 DE-BY-UBR DE-11 DE-578 |
| physical | XXVI, 335 S. Ill., graph. Darst. |
| publishDate | 2006 |
| publishDateSearch | 2006 |
| publishDateSort | 2006 |
| publisher | Wiley-VCH |
| record_format | marc |
| spellingShingle | Ziegler, Andreas 1966- König, Inke R. 1972- A statistical approach to genetic epidemiology concepts and applications Biometry Biometry methods Epidemiology, Molecular Molecular epidemiology Humangenetik (DE-588)4072653-8 gnd Biostatistik (DE-588)4729990-3 gnd Epidemiologie (DE-588)4015016-1 gnd Erbkrankheit (DE-588)4015106-2 gnd |
| subject_GND | (DE-588)4072653-8 (DE-588)4729990-3 (DE-588)4015016-1 (DE-588)4015106-2 |
| title | A statistical approach to genetic epidemiology concepts and applications |
| title_auth | A statistical approach to genetic epidemiology concepts and applications |
| title_exact_search | A statistical approach to genetic epidemiology concepts and applications |
| title_full | A statistical approach to genetic epidemiology concepts and applications Andreas Ziegler and Inke R. König |
| title_fullStr | A statistical approach to genetic epidemiology concepts and applications Andreas Ziegler and Inke R. König |
| title_full_unstemmed | A statistical approach to genetic epidemiology concepts and applications Andreas Ziegler and Inke R. König |
| title_short | A statistical approach to genetic epidemiology |
| title_sort | a statistical approach to genetic epidemiology concepts and applications |
| title_sub | concepts and applications |
| topic | Biometry Biometry methods Epidemiology, Molecular Molecular epidemiology Humangenetik (DE-588)4072653-8 gnd Biostatistik (DE-588)4729990-3 gnd Epidemiologie (DE-588)4015016-1 gnd Erbkrankheit (DE-588)4015106-2 gnd |
| topic_facet | Biometry Biometry methods Epidemiology, Molecular Molecular epidemiology Humangenetik Biostatistik Epidemiologie Erbkrankheit |
| url | http://deposit.dnb.de/cgi-bin/dokserv?id=2708213&prov=M&dok_var=1&dok_ext=htm http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=014726949&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT zieglerandreas astatisticalapproachtogeneticepidemiologyconceptsandapplications AT koniginker astatisticalapproachtogeneticepidemiologyconceptsandapplications |