A multi-marker association method for genome-wide association studies without the need for population structure correction.

ABSTRACT: All common genome-wide association (GWA) methods rely on population structure correction, to avoid false genotype-to-phenotype associations. However, population structure correction is a stringent penalization, which also impedes identification of real associations. Using recent statistical advances, we developed a new GWA method, called Quantitative Trait Cluster Association Test (QTCAT), enabling simultaneous multi-marker associations while considering correlations between markers. With this, QTCAT overcomes the need for population structure correction and also reflects the polygenic nature of complex traits better than single-marker methods. Using simulated data, we show that QTCAT clearly outperforms linear mixed model approaches. Moreover, using QTCAT to reanalyse public human, mouse

SUBMITTER: Klasen JR 

PROVIDER: S-EPMC5109549 | biostudies-literature | 2016 Nov

REPOSITORIES: biostudies-literature