Mathematical and Statistical Methods for Genetic Analysis

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Springer Science & Business Media, Dec 6, 2012 - Medical - 370 pages
During the past decade, geneticists have cloned scores of Mendelian disease genes and constructed a rough draft of the entire human genome. The unprecedented insights into human disease and evolution offered by mapping, cloning, and sequencing will transform medicine and agriculture. This revolution depends vitally on the contributions of applied mathematicians, statisticians, and computer scientists. Mathematical and Statistical Methods for Genetic Analysis is written to equip students in the mathematical sciences to understand and model the epidemiological and experimental data encountered in genetics research. Mathematical, statistical, and computational principles relevant to this task are developed hand in hand with applications to population genetics, gene mapping, risk prediction, testing of epidemiological hypotheses, molecular evolution, and DNA sequence analysis. Many specialized topics are covered that are currently accessible only in journal articles. This second edition expands the original edition by over 100 pages and includes new material on DNA sequence analysis, diffusion processes, binding domain identification, Bayesian estimation of haplotype frequencies, case-control association studies, the gamete competition model, QTL mapping and factor analysis, the Lander-Green-Kruglyak algorithm of pedigree analysis, and codon and rate variation models in molecular phylogeny. Sprinkled throughout the chapters are many new problems. Kenneth Lange is Professor of Biomathematics and Human Genetics at the UCLA School of Medicine. At various times during his career, he has held appointments at the University of New Hampshire, MIT, Harvard, and the University of Michigan. While at the University of Michigan, he was the Pharmacia & Upjohn Foundation Professor of Biostatistics. His research interests include human genetics, population modeling, biomedical imaging, computational statistics, and applied stochastic processes. Springer-Verlag published his book Numerical Analysis for Statisticians in 1999.
 

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Contents

Preface to the Second Edition
1
Newtons Method and Scoring
57
Hypothesis Testing and Categorical Data
59
Genetic Identity Coefficients
81
Applications of Identity Coefficients
97
Computation of Mendelian Likelihoods
115
The Polygenic Model 141
140
Descent Graph Methods
169
Radiation Hybrid Mapping 231
230
Models of Recombination
257
Sequence Analysis
281
Poisson Approximation 299
298
Diffusion Processes
317
Molecular Genetics in Brief
341
The Normal Distribution
351
Copyright

Molecular Phylogeny
203

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