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Adjusting for Familial Relatedness in the Analysis of GWAS Data

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Bioinformatics

Part of the book series: Methods in Molecular Biology ((MIMB,volume 1526))

Abstract

Relatedness within a sample can be of ancient (population stratification) or recent (familial structure) origin, and can either be known (pedigree data) or unknown (cryptic relatedness). All of these forms of familial relatedness have the potential to confound the results of genome-wide association studies. This chapter reviews the major methods available to researchers to adjust for the biases introduced by relatedness and maximize power to detect associations. The advantages and disadvantages of different methods are presented with reference to elements of study design, population characteristics, and computational requirements.

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Author information

Authors and Affiliations

  1. Centre for Research in Mathematics, School of Computing, Engineering and Mathematics, Western Sydney University, Parramatta, Australia

    Russell Thomson

  2. Menzies Institute for Medical Research, University of Tasmania, Hobart, TAS, Australia

    Rebekah McWhirter

Authors
  1. Russell Thomson
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  2. Rebekah McWhirter
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Correspondence to Russell Thomson .

Editor information

Editors and Affiliations

  1. Monash University, Melbourne, Victoria, Australia

    Jonathan M. Keith

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Thomson, R., McWhirter, R. (2017). Adjusting for Familial Relatedness in the Analysis of GWAS Data. In: Keith, J. (eds) Bioinformatics. Methods in Molecular Biology, vol 1526. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-6613-4_10

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  • DOI: https://doi.org/10.1007/978-1-4939-6613-4_10

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  • Publisher Name: Humana Press, New York, NY

  • Print ISBN: 978-1-4939-6611-0

  • Online ISBN: 978-1-4939-6613-4

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