Customer Reviews

Mathematical and Statistical Methods for Genetic Analysis

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This second edition updates the first with the many advances in the rapidly growing field of genetics. It provides a nice treatment of the mathematical and stochastic models that are useful in genetic studies.
It is a little disappointing that it does not go into the microarray technology that has become so important for experimentation in the last few years. Other recent books that cover statistical aspects of genetic research are Weir (1996) "Genetic Data Analysis II" Sinauer Associates (publisher) and Yang (2000) "Introduction to Statistical Methods in Modern Genetics" Gordon and Breach Science Publishers.

I'm interested in molecular genetics, this seems to be more about population genetics. There is some material, towards the back, about phylogeny. I can bash that a bit to make it match my needs, but it's still a bit of a stretch.

It seems to be a pretty good presentation of population genetics, the kind of genetics taught in high schools in the 70s. I can't comment on this book's merits, but I can warn the biochem types to spend their money elsewhere.

This book has an excellent coverage on the mathematical subtlies of genetics. The complicated theories are complimented by numerous examples. The exercise at the end of each chapter has a collection of probing questions that tests the understanding of the topics covered in the respective chapter. However, there is no discussion on association studies and quantitative traits which are two of the most active areas of genetic epidemiology. Moreover, the coverage on linkage is not adequate.

This book is not for the novice dabbling in statistical genetics. This is a highly sophisticated, thought provoking book targeted to individuals with considerable mathematical ability and training. As such, this book is an invaluable tool for individuals hoping to make a real impact in the field of statistical genetics. I particularly enjoyed the chapter on Markov Chain Monte Carlo methods for pedigree data.

The book has an excellent coverage of the mathematical subtlities of genetics. Theories are illustrated through numerous examples and the exercises at the end of each chapter contain probing questions which would test the understanding of the topics covered in the respective chapters. However, there are no chapters devoted to quantitative traits and association analysis, which are currently active areas of research. The coverage of linkage is also not adequate.