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© 2013 van der Sluis et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited: van der Sluis S, Posthuma D, Dolan CV (2013) TATES: Efficient Multivariate Genotype-Phenotype Analysis for Genome-Wide Association Studies. PLoS Genet 9(1): e1003235. doi:10.1371/journal.pgen.1003235

Abstract

To date, the genome-wide association study (GWAS) is the primary tool to identify genetic variants that cause phenotypic variation. As GWAS analyses are generally univariate in nature, multivariate phenotypic information is usually reduced to a single composite score. This practice often results in loss of statistical power to detect causal variants. Multivariate genotype-phenotype methods do exist but attain maximal power only in special circumstances. Here, we present a new multivariate method that we refer to as TATES (Trait-based Association Test that uses Extended Simes procedure), inspired by the GATES procedure proposed by Li et al (2011). For each component of a multivariate trait, TATES combines p-values obtained in standard univariate GWAS to acquire one trait-based p-value, while correcting for correlations between components. Extensive simulations, probing a wide variety of genotype-phenotype models, show that TATES's false positive rate is correct, and that TATES's statistical power to detect causal variants explaining 0.5% of the variance can be 2.5-9 times higher than the power of univariate tests based on composite scores and 1.5-2 times higher than the power of the standard MANOVA. Unlike other multivariate methods, TATES detects both genetic variants that are common to multiple phenotypes and genetic variants that are specific to a single phenotype, i.e. TATES provides a more complete view of the genetic architecture of complex traits. As the actual causal genotype-phenotype model is usually unknown and probably phenotypically and genetically complex, TATES, available as an open source program, constitutes a powerful new multivariate strategy that allows researchers to identify novel causal variants, while the complexity of traits is no longer a limiting factor.

Details

Title
TATES: Efficient Multivariate Genotype-Phenotype Analysis for Genome-Wide Association Studies
Author
Sluis, Sophie vander; Posthuma, Danielle; Dolan, Conor V
Section
Research Article
Publication year
2013
Publication date
Jan 2013
Publisher
Public Library of Science
ISSN
15537390
e-ISSN
15537404
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
1314341859
Copyright
© 2013 van der Sluis et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited: van der Sluis S, Posthuma D, Dolan CV (2013) TATES: Efficient Multivariate Genotype-Phenotype Analysis for Genome-Wide Association Studies. PLoS Genet 9(1): e1003235. doi:10.1371/journal.pgen.1003235