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Adjusting for Familial Relatedness in the Analysis of GWAS Data

Citation

Thomson, R and McWhirter, R, Adjusting for Familial Relatedness in the Analysis of GWAS Data, Bioinformatics: Volume II: Structure, Function, and Applications. Methods in Molecular Biology, Humana Press, JM Keith (ed), New York, United States, pp. 175-190. ISBN 978-1-4939-6613-4 (2017) [Research Book Chapter]

Copyright Statement

Copyright 2017 Springer Science+Business Media New York

DOI: doi:10.1007/978-1-4939-6613-4_10

Abstract

© Springer Science+Business Media New York 2017. 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.

Item Details

Item Type:Research Book Chapter
Keywords:genomewide association studies, GWAS, relatedness, confounding, population stratification, cryptic relatedness, familial structure
Research Division:Biological Sciences
Research Group:Genetics
Research Field:Quantitative Genetics (incl. Disease and Trait Mapping Genetics)
Objective Division:Expanding Knowledge
Objective Group:Expanding Knowledge
Objective Field:Expanding Knowledge in the Medical and Health Sciences
Author:Thomson, R (Dr Russell Thomson)
Author:McWhirter, R (Dr Rebekah McWhirter)
ID Code:116357
Year Published:2017
Deposited By:Menzies Institute for Medical Research
Deposited On:2017-05-08
Last Modified:2017-07-18
Downloads:0

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