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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 and Arabidopsis GWA data revealed nearly all known and some previously undetected associations. Following up on the most significant novel association in the Arabidopsis data allowed us to identify a so far unknown component of root growth.

journal_name

Nat Commun

journal_title

Nature communications

authors

Klasen JR,Barbez E,Meier L,Meinshausen N,Bühlmann P,Koornneef M,Busch W,Schneeberger K

doi

10.1038/ncomms13299

subject

Has Abstract

pub_date

2016-11-10 00:00:00

pages

13299

issn

2041-1723

pii

ncomms13299

journal_volume

7

pub_type

杂志文章

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