title: Adjusting for Familial Relatedness in the Analysis of GWAS Data.
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.
authors.affiliation: Centre for Research in Mathematics, School of Computing, Engineering and Mathematics, Western Sydney University, Parramatta, Australia. russell.thomson@westernsydney.edu.au.
authors.forename: Russell
authors.initials: R
authors.lastname: Thomson
authors.affiliation: Menzies Institute for Medical Research, University of Tasmania, Hobart, TAS, Australia.
authors.forename: Rebekah
authors.initials: R
authors.lastname: McWhirter
mesh_terms: D000818:Animals; D055106:Genome-Wide Association Study / Q000379:methods*; D006801:Humans; D008957:Models, Genetic; D010375:Pedigree
keywords: Confounding; Cryptic relatedness; Familial structure; GWAS; Genome-wide association studies; Population stratification; Relatedness