TATES: efficient multivariate genotype-phenotype analysis for genome-wide association studies

Research output: Contribution to JournalArticleAcademicpeer-review

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. © 2013 van der Sluis et al.
Original languageEnglish
Article numbere1003235
Pages (from-to)e1003235
JournalPLoS Genetics
Volume9
Issue number1
DOIs
Publication statusPublished - 2013

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Genome-Wide Association Study
phenotype
genotype
genome
Genotype
Phenotype
phenotypic variation
limiting factor
researchers
methodology
testing
Research Personnel
analysis
genome-wide association study
genotype-phenotype correlation
simulation
method
test

Cite this

@article{49595a7c3fb749e4ba64ff1ad939858f,
title = "TATES: efficient multivariate genotype-phenotype analysis for genome-wide association studies",
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. {\circledC} 2013 van der Sluis et al.",
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TATES: efficient multivariate genotype-phenotype analysis for genome-wide association studies. / van der Sluis, S.; Posthuma, D.; Dolan, C.V.

In: PLoS Genetics, Vol. 9, No. 1, e1003235, 2013, p. e1003235.

Research output: Contribution to JournalArticleAcademicpeer-review

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