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R Statistical Tools for Gene Discovery

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In Silico Tools for Gene Discovery

Part of the book series: Methods in Molecular Biology ((MIMB,volume 760))

Abstract

A wide assortment of R tools are available for exploratory data analysis in high-dimensional settings and are easily applicable to data arising from population-based genetic association studies. In this chapter we illustrate the application of three such approaches, namely conditional inference trees, random forests, and logic regression. Through applications to simulated data, we explore the relative utility of each approach for uncovering underlying association between genetic polymorphisms and a quantitative trait.

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Author information

Authors and Affiliations

  1. Division of Biostatistics, University of Massachusetts, Amherst, MA, 01003, USA

    Andrea S. Foulkes & Kinman Au

Authors
  1. Andrea S. Foulkes
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  2. Kinman Au
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Corresponding author

Correspondence to Andrea S. Foulkes .

Editor information

Editors and Affiliations

  1. Royal Prince Alfred Hospital, Dept. of Molecular & Clinical Genetics, University of Sydney, Missenden Road, Camperdown, 2050, New South Wales, Australia

    Bing Yu

  2. Royal Prince Alfred Hospital, Dept. Molecular & Clinical Genetics, University of Sydney, Missenden Road, Camperdown, 2050, New South Wales, Australia

    Marcus Hinchcliffe

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© 2011 Springer Science+Business Media, LLC

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Foulkes, A.S., Au, K. (2011). R Statistical Tools for Gene Discovery. In: Yu, B., Hinchcliffe, M. (eds) In Silico Tools for Gene Discovery. Methods in Molecular Biology, vol 760. Humana Press. https://doi.org/10.1007/978-1-61779-176-5_5

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  • DOI: https://doi.org/10.1007/978-1-61779-176-5_5

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  • Publisher Name: Humana Press

  • Print ISBN: 978-1-61779-175-8

  • Online ISBN: 978-1-61779-176-5

  • eBook Packages: Springer Protocols

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