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Nat Commun. 2016 Nov 10;7:13299. doi: 10.1038/ncomms13299.

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

Author information

1
Department of Plant Developmental Biology, Max Planck Institute for Plant Breeding Research (MPIPZ), Carl-von-Linné-Weg 10, 50829 Cologne, Germany.
2
Department of Plant Breeding and Genetics, Max Planck Institute for Plant Breeding Research (MPIPZ), Carl-von-Linné-Weg 10, 50829 Cologne, Germany.
3
Gregor Mendel Institute (GMI), Austrian Academy of Sciences, Vienna Biocenter (VBC), Dr Bohr-Gasse 3, 1030 Vienna, Austria.
4
Seminar for Statistics, Department of Mathematics, Eidgenssische Technische Hochschule Zurich (ETHZ), Rämistrasse 101, 8092 Zurich, Switzerland.

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.

PMID:
27830750
PMCID:
PMC5109549
DOI:
10.1038/ncomms13299
[Indexed for MEDLINE]
Free PMC Article

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