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Life Sciences - Biochemistry & Biophysics | A Guide to QTL Mapping with R/qtl

A Guide to QTL Mapping with R/qtl

Broman, Karl W., Sen, Saunak

2009, XII, 400p. 151 illus., 87 illus. in color.

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  • Gives an overview of the practical aspects of the analysis of QTL mapping experiments based on inbred line crosses, with explicit instructions on the use of the R/qtl software

Quantitative trait locus (QTL) mapping is used to discover the genetic and molecular architecture underlying complex quantitative traits. It has important applications in agricultural, evolutionary, and biomedical research. R/qtl is an extensible, interactive environment for QTL mapping in experimental crosses. It is implemented as a package for the widely used open source statistical software R and contains a diverse array of QTL mapping methods, diagnostic tools for ensuring high-quality data, and facilities for the fit and exploration of multiple-QTL models, including QTL x QTL and QTL x environment interactions. This book is a comprehensive guide to the practice of QTL mapping and the use of R/qtl, including study design, data import and simulation, data diagnostics, interval mapping and generalizations, two-dimensional genome scans, and the consideration of complex multiple-QTL models. Two moderately challenging case studies illustrate QTL analysis in its entirety.

The book alternates between QTL mapping theory and examples illustrating the use of R/qtl. Novice readers will find detailed explanations of the important statistical concepts and, through the extensive software illustrations, will be able to apply these concepts in their own research. Experienced readers will find details on the underlying algorithms and the implementation of extensions to R/qtl. There are 150 figures, including 90 in full color.

Karl W. Broman is Professor in the Department of Biostatistics and Medical Informatics at the University of Wisconsin-Madison, and is the chief developer of R/qtl. Saunak Sen is Associate Professor in Residence in the Department of Epidemiology and Biostatistics and the Center for Bioinformatics and Molecular Biostatistics at the University of California, San Francisco.

Content Level » Professional/practitioner

Keywords » best fit - complex traits - epistasis - genomics - quantitative trait loci - statistical genetics - statistical software

Related subjects » Animal Sciences - Biochemistry & Biophysics - Life Sciences, Medicine & Health

Table of contents 

Importing and simulating data.- Data checking.- Single-QTL analysis.- Non-normal phenotypes.- Experimental design and power.- Working with covariates.- Two-dimensional, two-QTL scans.- Fit and exploration of multiple-QTL models.- Case study I.- Case study II.

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