Statistical Inference from Genetic Data on Pedigrees

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PAPERBACK Good 0940600498 Fair-to-Good; Softcover; 2000, Inst of Mathematical Statistics; Moderate wear to covers; Moderate-to-heavy handwriting/notes to first 100 pages; Good ... binding with straight spine; White covers with title in black lettering; 169 pages; "Statistical Inference from Genetic Data on Pedigrees (Nsf-Cbms Conference Series in Probability & Statistics Volume 6) (V 6), " by Elizabeth A. Thompson. Read more Show Less

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Editorial Reviews

Booknews
While this monograph is not about show dogs or cats, its statistical methods could be applied to tracing the pedigree of these species as well as humans. Thompson (U. of Washington) covers such topics as genetic models, population allele frequencies, kinship/inbreeding coefficients, and Monte Carlo estimation. Includes supporting tables and figures. Suitable as a supplementary text or primary text for advanced students. Lacks an index. Annotation c. Book News, Inc., Portland, OR (booknews.com)
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Product Details

Table of Contents

Preface xi
Table of Notation xiii
1 Genes, Pedigrees and Genetic Models 1
1.1 DNA, alleles, loci, genotypes, and phenotypes 1
1.2 Mendel's laws and meiosis indicators 3
1.3 Pedigrees: the conditional independence structure 4
1.4 Models, parameters, and inferences 7
2 Likelihood, Estimation and Testing 11
2.1 Likelihood and log-likelihood 11
2.2 Estimation, information, and testing 13
2.3 Population allele frequencies 16
2.4 The EM algorithm; general formulation 20
2.5 Gene counting and the ABO blood types 22
2.6 EM estimation for quantitative trait data 25
3 Gene Identity by Descent 29
3.1 Kinship and inbreeding coefficients 29
3.2 Methods of computation 30
3.3 Data on inbred individuals 32
3.4 Multi-gamete kinship and gene ibd 34
3.5 Patterns of gene ibd in pairs of individuals 36
3.6 Observations on related individuals 39
3.7 Monte Carlo estimation of expectations 44
3.8 Reduction of Monte Carlo variance 46
4 Genetic Linkage 49
4.1 Linkage and recombination: genetic distance 49
4.2 Haplotypes, linkage, and association 51
4.3 Lod scores for two-locus linkage analysis 53
4.4 Power, information and Elods 55
4.5 Two-locus kinship and gene identity 59
4.6 Homozygosity mapping with a single marker 61
4.7 Meiosis at multiple linked loci 64
4.8 Multi-locus kinship and gene identity 65
5 Models for Meiosis 69
5.1 The meiosis process 69
5.2 From chromatids to crossovers 71
5.3 From chiasmata to recombination patterns 72
5.4 The chiasmata avoidance process 73
5.5 Chromatid interference 75
5.6 Count-location models for chiasmata 76
5.7 Renewal process models of chiasma formation 77
6 Likelihoods on Pedigrees 81
6.1 The Baum algorithm and "Peeling" 81
6.2 Exact likelihoods for multiple markers 83
6.3 Computations on large but simple pedigrees 84
6.4 Example of peeling a zero-loop pedigree 86
6.5 Computations on complex pedigrees 90
6.6 Models with Gaussian random effects 91
7 Monte Carlo Estimates on Pedigrees 93
7.1 Baum algorithm for conditional probabilities 93
7.2 An EM algorithm for map estimation 95
7.3 Importance sampling for likelihoods 96
7.4 Risk probabilities and reverse peeling 97
7.5 Elods and SIMLINK 99
7.6 Sequential imputation 100
8 Markov chain Monte Carlo on Pedigrees 103
8.1 Simulation conditional on data: MCMC 103
8.2 Single-site updating methods 107
8.3 Combining exact computation and Monte Carlo 109
8.4 Tightly-linked loci: the M-sampler 111
9 Likelihood Ratios for Genetic Analysis 115
9.1 Monte Carlo likelihood ratio estimation 115
9.2 Monte Carlo relative likelihood surfaces 116
9.3 Monte Carlo EM for the mixed model 118
9.4 Likelihood estimators for complex models 120
9.5 Likelihood estimation of gene locations 123
9.6 Marker ibd and complete-data log-likelihoods 125
10 Case studies using the M- and LM-samplers 129
10.1 Background to a study 129
10.2 Conditional gene ibd probabilities 131
10.3 Likelihoods and log-likelihoods 133
10.4 Gene ibd in a smaller example 135
10.5 MCMC lod score estimation 137
10.6 Better MCMC lod scores 140
11 Other Monte Carlo Likelihoods in Genetics 147
11.1 Improving pedigree samplers 147
11.2 Interference by Metropolis-Hastings 149
11.3 Inference of typing or pedigree error 154
11.4 Other Monte-Carlo procedures for linkage analysis 156
11.5 Monte-Carlo likelihoods in population genetics 156
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