Testing for genetic associations in arbitrarily structured populations.

Abstract:

:We present a new statistical test of association between a trait and genetic markers, which we theoretically and practically prove to be robust to arbitrarily complex population structure. The statistical test involves a set of parameters that can be directly estimated from large-scale genotyping data, such as those measured in genome-wide association studies (GWAS). We also derive a new set of methodologies, called a 'genotype-conditional association test' (GCAT), shown to provide accurate association tests in populations with complex structures, manifested in both the genetic and non-genetic contributions to the trait. We demonstrate the proposed method on a large simulation study and on the Northern Finland Birth Cohort study. In the Finland study, we identify several new significant loci that other methods do not detect. Our proposed framework provides a substantially different approach to the problem from existing methods, such as the linear mixed-model and principal-component approaches.

journal_name

Nat Genet

journal_title

Nature genetics

authors

Song M,Hao W,Storey JD

doi

10.1038/ng.3244

subject

Has Abstract

pub_date

2015-05-01 00:00:00

pages

550-4

issue

5

eissn

1061-4036

issn

1546-1718

pii

ng.3244

journal_volume

47

pub_type

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