Mathematical and Statistical Methods for Genetic Analysis

1. Front Matter

   Pages i-xii

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2. Basic Principles of Population Genetics

   • Kenneth Lange

   Pages 1-18

3. Counting Methods and the EM Algorithm

   • Kenneth Lange

   Pages 19-34

4. Newton’s Method and Scoring

   • Kenneth Lange

   Pages 35-51

5. Hypothesis Testing and Categorical Data

   • Kenneth Lange

   Pages 52-69

6. Genetic Identity Coefficients

   • Kenneth Lange

   Pages 70-84

7. Applications of Identity Coefficients

   • Kenneth Lange

   Pages 85-101

8. Computation of Mendelian Likelihoods

   • Kenneth Lange

   Pages 102-122

9. The Polygenic Model

   • Kenneth Lange

   Pages 123-141

10. Markov Chain Monte Carlo Methods

    • Kenneth Lange

    Pages 142-163

11. Reconstruction of Evolutionary Trees

    • Kenneth Lange

    Pages 164-182

12. Radiation Hybrid Mapping

    • Kenneth Lange

    Pages 183-205

13. Models of Recombination

    • Kenneth Lange

    Pages 206-227

14. Poisson Approximation

    • Kenneth Lange

    Pages 228-244

15. Back Matter

    Pages 245-265

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During the past decade, geneticists have constructed detailed maps of the human genome and cloned scores of Mendelian disease genes. They now stand on the threshold of sequencing the genome in its entirety. The unprecedented insights into human disease and evolution offered by mapping 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 graduate 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 gene mapping, risk prediction, and the testing of epidemiological hypotheses. The book includes many topics currently accessible only in journal articles, including pedigree analysis algorithms, Markov chain Monte Carlo methods, reconstruction of evolutionary trees, radiation hybrid mapping, and models of recombination. Exercise sets are included.
Kenneth Lange is Professor of Biostatistics and Mathematics and the Pharmacia & Upjohn Foundations Research Professor at the University of Michigan. He has held visiting appointments at MIT and Harvard. His research interests include human genetics, population modeling, biomedical imaging, computational statistics, and applied stochastic processes.

• DNA
• Likelihood
• Markov chain
• Monte Carlo method
• algorithms
• biostatistics
• chromosome
• expectation–maximization algorithm
• genes
• genetics
• genome
• human genetics
• mathematics
• molecular genetics
• statistics

• Department of Biostatistics and Mathematics, University of Michigan, Ann Arbor, USA

  Kenneth Lange

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