Genetic Analysis of Complex Traits Using SAS [Paperback]

Arnold Saxton (Author)

Book Description

Publication Date: November 10, 2004 | ISBN-10: 1590475070 | ISBN-13: 978-1590475072 | Edition: 1

Arnold Saxton, Ph.D., the editor of this volume, is a professor of animal science at the University of Tennessee, Knoxville. During years of research and teaching in statistics and genetics, Dr. Saxton recognized the need for a how-to introduction to SAS computer analysis for complex-trait genetics. He assembled 16 coauthors from around the world to create this unique compilation. Example-rich and experiment-driven, Genetic Analysis of Complex Traits Using SAS demonstrates how you can use SAS and SAS/Genetics to extract answers from your quantitative and molecular genetics data. The book guides you through the mix of genetic, statistical, and SAS skills that are needed, enabling you to apply what you've learned to your own experimental data. You'll find this an invaluable resource whether you are a researcher, scientist, graduate student, bioinformatician, or statistician--or any other SAS user interested in joining the highly active and exciting field of genetic analysis.

Editorial Reviews

Review

This book is a very welcome addition to the toolbox of anyone using the powerful SAS System to analyze the genetic basis of complex traits. Both classic and Bayesian approaches are discussed, with a focus on genetic parameter estimation and gene mapping. An especially nice feature, and indeed worth the price of the book by themselves, are chapters discussing important, but underappreciated, approaches for AMMI modeling of genotype-environment interactions, the analysis of longitudinal traits, and empirical Bayes estimates. --Bruce Walsh

About the Author

Dr. Balzarini, professor of statistics in the Agricultural College at the National University of Córdoba, is a statistical genomics investigator in Argentina. Professor Cappio-Borlino s scientific interests involve the mathematical modeling of biological phenomena of interest in animal science. Dr. Czika, research statistician at SAS Institute and the principal developer of SAS/GeneticsTM procedures, researches statistical methods for analyzing genetic marker data. Dr. Fry is an assistant professor of biology at the University of Rochester. Dr. Gibson is an associate professor of genetics at North Carolina State University. Dr. Guerra is a consultant in applied statistics at the Federal University of Ceara and a visiting professor at the Federal University of Fortaleza in Brazil. Dr. Kang is a professor of quantitative genetics at Louisiana State University. Nicolò P. P. Macciotta researches animal breeding and genetics at the University of Sassari, Italy. Dr. Pulina is a professor of animal science and head of Animal Science at the University of Sassari, Italy. Dr. Rosa is an assistant professor of statistical genetics at Michigan State University. Dr. Saxton is a professor of animal science at the University of Tennessee, Knoxville. Dr. Stalder is an assistant professor in Animal Science at Iowa State University. Dr. Tempelman is associate professor in Animal Science and adjunct associate professor in Statistics and Probability at Michigan State University. Dr. Wolfinger is Director of Genomics at SAS Institute. He is developing procedures and conducting research at SAS. Chenwu Xu is a post-doctoral research associate in statistical genetics at the University of California Riverside. Shizhong Xu is a professor of genetics and adjunct professor of statistics at the University of California Riverside. Xiang Yu has a Ph.D. in bioinformatics. He is a biometrician at Merck Research Laboratory.

Product Details

• Paperback: 312 pages
• Publisher: Books by Users Press; 1 edition (November 10, 2004)
• Language: English
• ISBN-10: 1590475070
• ISBN-13: 978-1590475072
• Product Dimensions: 10.9 x 8.4 x 0.6 inches
• Shipping Weight: 1.6 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: #296,966 in Books (See Top 100 in Books)

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This review is from: Genetic Analysis of Complex Traits Using SAS (Paperback)

Regardless of how one feels about SAS as a programming language, it is readily apparent that it is very popular in areas such as financial and biological modeling. This book gives an introduction to how it is used in genetic analysis, and even though each chapter is written by a different author, the book can be useful to those (such as this reviewer) who are not experts in genetics but who may be called upon to apply their mathematical and statistical knowledge to problems in genetics (but using SAS instead of some other programming language to do so). Although the book assumes a thorough knowledge of genetics, it can still be read profitably by anyone who has a background in SAS and some knowledge of genetics. Being an interpreted language, SAS performance can be a problem with many applications, and its value in science is questionable for projects that require heavy computational power. For medium-sized projects though it can be helpful, even though its semantics can be hard to get used to for those who have programmed in more object-oriented environments.

SAS has been used widely to perform statistical studies in genetics using "classical" tools such as multivariate analysis and maximum likelihood, but there is one chapter in this book where Bayesian inference techniques are used for genetic analysis. In addition, and this makes the discussion in the chapter even more valuable, is that the estimation of the posterior distribution is done using Markov chain Monte Carlo (MCMC) techniques. The first genetics problem on which this is done regards two-point linkage analysis where Bayesian inference is used to estimate the recombination rate in a backcross between two completely homozygous lines for each of two loci. Even though this problem has an analytical solution, the authors use a simple Monte Carlo simulation to estimate the posterior mean and variance of the recombination rate to motivate how SAS can be used in this case.It should be pointed out here that the authors use SAS PROC Capability in their code and not all readers have this in their SAS implementation, but it can be replaced by PROC Univariate with no problems. This problem is generalized to the case of where there are three linked marker loci, with Bayesian inference and MCMC (via the Metropolis-Hastings algorithm) used to estimate the loci order and the recombination rates between the markers. The authors give the actual SAS code to implement this analysis, which is very readable (in spite of the ancient and annoying "goto" statements that are used within it). MCMC techniques are essential though in more general problems where analytical solutions are not possible. This is the case for a general genetic map construction that the authors discuss but do not give the explicit SAS code for (but it can be found on the Website that is associated with the book). They discuss briefly the pitfalls in doing MCMC for this case, and give a few alternatives. Bayesian inference is then applied to QTL analysis for the simple case of a single QTL model for backcrossing.