Genetic Data Analysis 2: Methods for Discrete Population Genetic Data [Paperback]

Bruce S. Weir (Author)

Book Description

ISBN-10: 0878939024 | ISBN-13: 978-0878939022 | Publication Date: April 1996 | Edition: 2 Sub

This revised and expanded second edition of "Genetic Data Analysis" details the statistical methodology needed to draw inferences from discrete genetic data. Major changes from the first edition include an expanded treatment of linkage, including methods to locate human disease genes, and a new chapter on individual identification.

Editorial Reviews

Review

"...selects itself automatically as the preferred text for a course on the subject." -- —Anthony W. F. Edwards, Heredity

About the Author

BRUCE. S. WEIR is William Neal Reynolds Professor of Statistics and Genetics, and Director of the Bioinformatics Research Centre at North Carolina State University, USA. --This text refers to an out of print or unavailable edition of this title.

Product Details

• Paperback: 445 pages
• Publisher: Sinauer Associates Inc; 2 Sub edition (April 1996)
• Language: English
• ISBN-10: 0878939024
• ISBN-13: 978-0878939022
• Product Dimensions: 8.8 x 6 x 1 inches
• Shipping Weight: 1.4 pounds (View shipping rates and policies)
• Average Customer Review: 4.0 out of 5 stars  See all reviews (1 customer review)
• Amazon Best Sellers Rank: #1,033,834 in Books (See Top 100 in Books)

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2 of 2 people found the following review helpful:

4.0 out of 5 stars An Essential Text, October 26, 2010

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Gregg Silk - See all my reviews
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This review is from: Genetic Data Analysis 2: Methods for Discrete Population Genetic Data (Paperback)

This is a good book for someone with a sound grasp of Mendelian genetics, familiarity with molecular genetics, and some experience with population genetics who wants to step up their understanding two or three levels. The math is at the level of a graduate course in genetics, but not too theoretical, and Weir points out where some of the modelling gets lost in the weeds.

Parts of the book are a tough slog, not because of any flaw in the book, but because it compares allelic, gametic, and population frequencies with recessive and multiallelic traits. Which makes for some painful reading, but the reader will be left with an appreciation of how often genetics papers ignore common scenarios that would make their results ambiguous. And if someone wants to do higher order statistical analysis, this is where they need to go to see how variables are calculated. This is crucial, because many of these variables are fairly similar, but have different sets of assumptions.

Reading this book requires constant flipping of pages back and forth because nothing is repeated. By the time I was finished, the pages were falling out. This book could be four times as large, and if it were in a larger format, it would benefit from extensive footnotes and cross references. Although it would be bulkier and a more reptitive, it would be possible to read it in a linear fashion.