A Guide to QTL Mapping with R/qtl

  • Karl W. Broman
  • Saunak Sen
Part of the Statistics for Biology and Health book series (SBH)

Table of contents

  1. Front Matter
    Pages i-xii
  2. Karl W. Broman, Śaunak Sen
    Pages 1-20
  3. Karl W. Broman, Śaunak Sen
    Pages 21-46
  4. Karl W. Broman, Śaunak Sen
    Pages 47-73
  5. Karl W. Broman, Śaunak Sen
    Pages 75-133
  6. Karl W. Broman, Śaunak Sen
    Pages 135-151
  7. Karl W. Broman, Śaunak Sen
    Pages 153-177
  8. Karl W. Broman, Śaunak Sen
    Pages 179-211
  9. Karl W. Broman, Śaunak Sen
    Pages 213-239
  10. Karl W. Broman, Śaunak Sen
    Pages 241-282
  11. Karl W. Broman, Śaunak Sen
    Pages 283-312
  12. Karl W. Broman, Śaunak Sen
    Pages 313-354
  13. Back Matter
    Pages 1-40

About this book

Introduction

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.

Keywords

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

Authors and affiliations

  • Karl W. Broman
    • 1
  • Saunak Sen
    • 2
  1. 1.Dept. Biostatistics &University of Wisconsin-MadisonMadisonU.S.A.
  2. 2.Dept. Epidemiology & BiostatisticsUniversity of San FranciscoSan FranciscoU.S.A.

Bibliographic information

  • DOI https://doi.org/10.1007/978-0-387-92125-9
  • Copyright Information Springer-Verlag New York 2009
  • Publisher Name Springer, New York, NY
  • eBook Packages Biomedical and Life Sciences
  • Print ISBN 978-0-387-92124-2
  • Online ISBN 978-0-387-92125-9
  • Series Print ISSN 1431-8776
  • About this book