Atnaujinkite slapukų nuostatas

Statistical Approach to Genetic Epidemiology: Concepts and Applications [Kietas viršelis]

,
  • Formatas: Hardback, 522 pages, aukštis x plotis x storis: 244x177x30 mm, weight: 1124 g
  • Išleidimo metai: 24-Mar-2010
  • Leidėjas: Wiley-VCH Verlag GmbH
  • ISBN-10: 3527324534
  • ISBN-13: 9783527324538
Kitos knygos pagal šią temą:
Statistical Approach to Genetic Epidemiology: Concepts and ApplicationsDidesnis paveikslėlis
  • Formatas: Hardback, 522 pages, aukštis x plotis x storis: 244x177x30 mm, weight: 1124 g
  • Išleidimo metai: 24-Mar-2010
  • Leidėjas: Wiley-VCH Verlag GmbH
  • ISBN-10: 3527324534
  • ISBN-13: 9783527324538
Kitos knygos pagal šią temą:
Pastovi nuoroda: https://www.kriso.lt/db/9783527324538.html
Raktažodžiai:
This is the second edition of the successful textbook written by Andreas Ziegler and Inke R. Konig, who have been teaching the subject for more than a decade.

The book gives a comprehensive introduction into the relevant statistical methods in genetic epidemiology, from the genetic background to genome-wide association studies. The second edition is thoroughly revised, party rewritten, and a chapter on classical twin and adoption studies and the estimation of heritability has been added Data quality control receives special attention, and an entire new chapter is devoted to genome-wide association studies. The book is ideally suited for advanced students in epidemiology, genetics, statistics, bioinformatics, and biomathematics.

The book contains more than 100 problems and solutions, and comes now optionally with an e-learning course developed by Friedrich Pahlke (9783527323890). Both provide a unique support tool for teaching the subject.

After studying statistics and mathematics at the University of Munich and obtaining his doctoral degree from the University of Dortmund (Germany), Andreas Ziegler received the Johann-Peter-Suβmilch-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. For collaborative studies in the field of genetic epidemiology, he has received further awards. Andreas Ziegler is professor and head of the Institute of Medical Biometry and Statistics at the University Hospital Schleswig-Holstein in Lubeck (Germany), an acknowledged center of excellence for genetic epidemiological methods.

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 Konig, 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 new 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.
Foreword to the first Edition vii
Foreword to the Second Edition viii
Preface xi
Acknowledgments xv
1 Molecular Genetics
1(20)
1.1 Genetic information
2(5)
1.1.1 Location of genetic information
2(3)
1.1.2 Interpretation of genetic information
5(1)
1.1.3 Translation of genetic information
5(2)
1.2 Transmission of genetic information
7(3)
1.3 Variations in genetic information
10(8)
1.3.1 Individual differences in genetic information
10(2)
1.3.2 Detection of variations
12(4)
1.3.3 Probability for detection of variations
16(2)
1.4 Problems
18(3)
2 Formal Genetics
21(26)
2.1 Mendel and his laws
22(1)
2.2 Segregation patterns
23(5)
2.2.1 Autosomal dominant inheritance
24(1)
2.2.2 Autosomal recessive inheritance
25(1)
2.2.3 X-chromosomal dominant inheritance
26(1)
2.2.4 X-chromosomal recessive inheritance
27(1)
2.2.5 Y-chromosomal inheritance
28(1)
2.3 Complications of Mendelian segregation
28(10)
2.3.1 Variable penetrance and expression
29(2)
2.3.2 Age-dependent penetrance
31(2)
2.3.3 Imprinting
33(2)
2.3.4 Phenotypic and genotypic heterogeneity
35(1)
2.3.5 Complex diseases
36(2)
2.4 Hardy---Weinberg law
38(5)
2.5 Problems
43(4)
3 Genetic Markers
47(20)
3.1 Properties of genetic markers
47(5)
3.2 Types of genetic markers
52(5)
3.2.1 Short tandem repeats (STRs)
52(2)
3.2.2 Single nucleotide polymorphisms (SNPs)
54(3)
3.3 Genotyping methods for SNPs
57(8)
3.3.1 Restriction fragment length polymorphism analysis
58(1)
3.3.2 Real-time polymerase chain reaction
58(3)
3.3.3 Matrix assisted laser desorption/ionization time of flight genotyping
61(1)
3.3.4 Chip-based genotyping
61(2)
3.3.5 Choice of genotyping method
63(2)
3.4 Problems
65(2)
4 Data Quality
67(46)
4.1 Pedigree errors
68(2)
4.2 Genotyping errors in pedigrees
70(6)
4.2.1 Frequency of genotyping errors
70(1)
4.2.2 Reasons for genotyping errors
71(1)
4.2.3 Mendel checks
72(2)
4.2.4 Checks for double recombinants
74(2)
4.3 Genotyping errors and Hardy---Weinberg equilibrium (HWE)
76(15)
4.3.1 Causes of deviations from HWE
77(1)
4.3.2 Tests for deviations from HWE for SNPs
78(3)
4.3.3 Tests for deviations from HWE for STRs
81(2)
4.3.4 Measures for deviations from HWE
83(3)
4.3.5 Tests for compatibility with HWE for SNPs
86(5)
4.4 Quality control in high-throughput studies
91(7)
4.4.1 Sample quality control
94(3)
4.4.2 SNP quality control
97(1)
4.5 Cluster plot checks and internal validity
98(11)
4.5.1 Cluster compactness validity
101(1)
4.5.2 Cluster connectedness measures
101(1)
4.5.3 Cluster Connectedness measures
101(1)
4.5.4 Genotype stability measures
102(1)
4.5.5 Combinations of criteria
102(7)
4.6 Problems
109(4)
5 Genetic Map Distances
113(12)
5.1 Physical distance
113(1)
5.2 Map distance
114(4)
5.2.1 Distance
114(1)
5.2.2 Specific map functions
115(1)
5.2.3 Correspondence between physical distance and map distance
116(1)
5.2.4 Multilocus feasibility
117(1)
5.3 Linkage disequilibrium distance
118(5)
5.4 Problems
123(2)
6 Family Studies
125(30)
6.1 Family history method and family study method
127(2)
6.2 Familial correlations and recurrence risks
129(5)
6.2.1 Familial resemblance
129(2)
6.2.2 Recurrence risk ratios
131(3)
6.3 Heritability
134(7)
6.3.1 The simple Falconer model
135(2)
6.3.2 The general Falconer model
137(1)
6.3.3 Kinship coefficient and Jacquard's Δ7 coefficient
138(3)
6.4 Twin and adoption studies
141(2)
6.4.1 Twin studies
141(1)
6.4.2 Adoption studies
142(1)
6.5 Critique on investigating familial resemblance
143(1)
6.6 Segregation analysis
144(10)
6.7 Problems
154(1)
7 Model-Based Linkage Analysis
155(34)
7.1 Linkage analysis between two genetic markers
156(11)
7.1.1 Linkage analysis in phase-known pedigrees
156(4)
7.1.2 Linkage analysis in phase-unknown pedigrees
160(1)
7.1.3 Linkage analysis in pedigrees with missing genotypes
161(6)
7.2 Linkage analysis between a genetic marker and a disease
167(13)
7.2.1 Linkage analysis between a genetic marker and a disease in phase-known pedigrees
168(4)
7.2.2 Linkage analysis between a genetic marker and a disease in general cases
172(5)
7.2.3 Gain in information by genotyping additional individual; power calculations
177(3)
7.3 Significance levels in linkage analysis
180(4)
7.4 Problems
184(5)
8 Model-Free Linkage Analysis
189(32)
8.1 The principle of similarity
190(2)
8.2 Mathematical foundation of affected sib-pair analysis
192(1)
8.3 Common tests for affected sib-pair analysis
193(13)
8.3.1 The maximum LOD score and the triangle test
194(7)
8.3.2 Score-and Wald---type 1 degree of freedom tests
201(5)
8.3.3 Affected sib-pair tests using alleles shared identical by state
206(1)
8.4 Properties of affected sib-pair tests
206(1)
8.5 Sample size and power calculation for affected sib-pair studies
207(5)
8.5.1 Functional relation between identical by descent probabilities and recurrence risk ratios
207(2)
8.5.2 Sample size and power calculations for the mean test using recurrence risk ratios
209(3)
8.6 Extensions to multiple marker loci
212(1)
8.7 Extension to large sibships
213(1)
8.8 Extension to large pedigrees
214(2)
8.9 Extensions of the affected sib-pair approach
216(2)
8.9.1 Covariates in affected sib-pair analyses
216(1)
8.9.2 Multiple disease loci in affected sib-pair analyses
216(1)
8.9.3 Estimating the position of the disease locus in affected sib-pair analyses
217(1)
8.9.4 Typing unaffected relatives in sib-pair analyses
217(1)
8.10 Problems
218(3)
9 Quantitative Traits
221(26)
9.1 Quantitative versus qualitative traits
222(1)
9.2 The Haseman---Elston method
223(6)
9.2.1 The expected squared phenotypic difference at the trait locus
225(2)
9.2.2 The expected squared phenotypic difference at the marker locus
227(2)
9.3 Extensions of the Haseman---Elston method
229(8)
9.3.1 Double squared trait difference
230(1)
9.3.2 Extension to large sibships
230(1)
9.3.3 Haseman---Elston revisited and the new Haseman---Elston method
231(3)
9.3.4 Power and sample size calculation
234(3)
9.4 Variance components models
237(3)
9.4.1 The univariate variance components model
237(1)
9.4.2 The multivariate variance components model
238(2)
9.5 Random sib-pairs, extreme probands and extreme sib-pairs
240(3)
9.6 Empirical determination of p-values
243(2)
9.7 Problems
245(2)
10 Fundamental Concepts of Association Analyses
247(18)
10.1 Introduction to association
247(3)
10.1.1 Principles of association
247(2)
10.1.2 Study designs for association
249(1)
10.2 Linkage disequilibrium
250(12)
10.2.1 Allelic linkage disequilibrium
250(5)
10.2.2 Genotypic linkage disequilibrium
255(4)
10.2.3 Extent of linkage disequilibrium
259(3)
10.3 Problems
262(3)
11 Association Analysis in Unrelated Individuals
265(54)
11.1 Selection of cases and controls
266(1)
11.2 Tests, estimates, and comparison
266(23)
11.2.1 Association tests
267(5)
11.2.2 Choice of a test in applications
272(2)
11.2.3 Effect measures
274(13)
11.2.4 Association tests for the X chromosome
287(1)
11.2.5 Association tests for the X chromosome
287(2)
11.3 Sample size calculation
289(2)
11.4 Population stratification
291(8)
11.4.1 Testing for population stratification
293(1)
11.4.2 Structured association
294(1)
11.4.3 Genomic control
295(2)
11.4.4 Comparison of structured association and genomic control
297(1)
11.4.5 Principle components analysis
297(2)
11.5 Gene-gene and gene-environment interaction
299(17)
11.5.1 Classical examples for gene-gene and gene-environment interaction
299(2)
11.5.2 Coat color in the Labrador retriever
301(2)
11.5.3 Concepts of interaction
303(4)
11.5.4 Statistical testing of gene-environment interactions
307(4)
11.5.5 Statistical testing of gene-gene interactions
311(4)
11.5.6 Multifactor dimensionality reduction
315(1)
11.6 Problems
316(3)
12 Family-based Association Analysis
319(30)
12.1 Haplotype relative risk
320(2)
12.2 Transmission disequilibrium test (TDT)
322(3)
12.3 Risk estimates for trio data
325(2)
12.4 Sample size and power calculations for the TDT
327(2)
12.5 Alternative test statistics
329(1)
12.6 TDT for multiallelic markers
330(3)
12.6.1 Test of single alleles
330(1)
12.6.2 Global test statistics
331(2)
12.7 TDT type tests for different family structures
333(11)
12.7.1 TDT type tests for missing parental data
334(2)
12.7.2 TDT type tests for sibship data
336(5)
12.7.3 TDT type tests for extended pedigrees
341(3)
12.8 Association analysis for quantitative traits
344(2)
12.9 Problems
346(3)
13 Haplotypes in Association Analyses
349(18)
13.1 Reasons for studying haplotypes
350(1)
13.2 Inference of haplotypes
351(5)
13.2.1 Algorithms for haplotype assignment
352(1)
13.2.2 Algorithms for estimating haplotype probabilities
353(3)
13.3 Association tests using haplotypes
356(3)
13.4 Haplotype blocks and tagging SNPs
359(5)
13.4.1 Selection of markers by haplotypes or linkage disequilibrium
360(3)
13.4.2 Evaluation of marker selection approaches
363(1)
13.5 Problems
364(3)
14 Genome-wide Association (GWA) Studies
367(26)
14.1 Design options in GWA studies
369(1)
14.2 Genotype imputation
370(2)
14.2.1 Imputation algorithms
370(1)
14.2.2 Quality of imputation
371(1)
14.3 Statistical analysis of GWA studies
372(2)
14.4 Multiple testing
374(4)
14.4.1 Region-wide multiple testing adjustment by simulation
375(1)
14.4.2 Genome-wide multiple testing adjustment by simulation
376(1)
14.4.3 Multiple testing adjustment by effective number of tests
377(1)
14.5 Analysis of accumulating GWA data
378(5)
14.5.1 Multistage design for GWA studies
378(1)
14.5.2 Replication in GWA studies
379(1)
14.5.3 Meta-analysis of GWA studies
380(3)
14.6 Clinical impact of a GWA study
383(6)
14.6.1 Evaluation of a genetic predictive test
383(2)
14.6.2 Clinical validity of a single genetic marker
385(1)
14.6.3 Clinical validity of multiple genetic markers
386(3)
14.7 Outlook
389(2)
14.8 Problems
391(2)
Appendix Algorithms Used in Linkage Analyses
393(22)
A.1 The Elston-Stewart algorithm
394(7)
A.1.1 The fundamental ideas of the Elston-Stewart algorithm
394(6)
A.1.2 The Elston-Stewart algorithm for a trait and a linked marker locus
400(1)
A.2 The Lander-Green algorithm
401(11)
A.2.1 The inheritance vector at a single genetic markers
401(4)
A.2.2 The inheritance distribution given all genetic markers
405(7)
A.3 The Cardon-Fulker algorithm
412(2)
A.4 Problems
414(1)
Solutions 415(36)
References 451(38)
Index 489
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-Su?milch-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. 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.