title: 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.
authors.affiliation: Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, New Jersey, USA.
authors.forename: Minsun
authors.initials: M
authors.lastname: Song
authors.affiliation: Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, New Jersey, USA.
authors.forename: Wei
authors.initials: W
authors.lastname: Hao
authors.affiliation: 1] Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, New Jersey, USA. [2] Center for Statistics and Machine Learning, Princeton University, Princeton, New Jersey, USA. [3] Department of Molecular Biology, Princeton University, Princeton, New Jersey, USA.
authors.forename: John D
authors.initials: JD
authors.lastname: Storey
mesh_terms: D003198:Computer Simulation; D055106:Genome-Wide Association Study; D006801:Humans; D016014:Linear Models; D008957:Models, Genetic; D020641:Polymorphism, Single Nucleotide; D025341:Principal Component Analysis
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