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Bioinformatics pp 175-190 | Cite as

Adjusting for Familial Relatedness in the Analysis of GWAS Data

  • Russell ThomsonEmail author
  • Rebekah McWhirter
Protocol
Part of the Methods in Molecular Biology book series (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.

Key words

Genome-wide association studies GWAS Relatedness Confounding Population stratification Cryptic relatedness Familial structure 

Supplementary material

316168_2_En_10_MOESM1_ESM.zip (2.2 mb)
ExampleDataSet

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Copyright information

© Springer Science+Business Media New York 2017

Authors and Affiliations

  1. 1.Centre for Research in Mathematics, School of Computing, Engineering and MathematicsWestern Sydney UniversityParramattaAustralia
  2. 2.Menzies Institute for Medical ResearchUniversity of TasmaniaHobartAustralia

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