© 1997

Mathematical and Statistical Methods for Genetic Analysis


Part of the Statistics for Biology and Health book series (SBH)

Table of contents

  1. Front Matter
    Pages i-xii
  2. Kenneth Lange
    Pages 1-18
  3. Kenneth Lange
    Pages 19-34
  4. Kenneth Lange
    Pages 35-51
  5. Kenneth Lange
    Pages 52-69
  6. Kenneth Lange
    Pages 70-84
  7. Kenneth Lange
    Pages 85-101
  8. Kenneth Lange
    Pages 102-122
  9. Kenneth Lange
    Pages 123-141
  10. Kenneth Lange
    Pages 142-163
  11. Kenneth Lange
    Pages 164-182
  12. Kenneth Lange
    Pages 183-205
  13. Kenneth Lange
    Pages 206-227
  14. Kenneth Lange
    Pages 228-244
  15. Back Matter
    Pages 245-265

About this book


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

Authors and affiliations

  1. 1.Department of Biostatistics and MathematicsUniversity of MichiganAnn ArborUSA

Bibliographic information