MGAS: a powerful tool for multivariate gene-based genome-wide association analysis.

S. van der Sluis, C.V. Dolan, J. Li, Y. Song, P. Sham, D. Posthuma, M. Li

Research output: Contribution to JournalArticleAcademicpeer-review

Abstract

Motivation: Standard genome-wide association studies, testing the association between one phenotype and a large number of single nucleotide polymorphisms (SNPs), are limited in two ways: (i) traits are often multivariate, and analysis of composite scores entails loss in statistical power and (ii) gene-based analyses may be preferred, e.g. to decrease the multiple testing problem. Results: Here we present a new method, multivariate gene-based association test by extended Simes procedure (MGAS), that allows gene-based testing of multivariate phenotypes in unrelated individuals. Through extensive simulation, we show that under most trait-generating genotype-phenotype models MGAS has superior statistical power to detect associated genes compared with gene-based analyses of univariate phenotypic composite scores (i.e. GATES, multiple regression), and multivariate analysis of variance (MANOVA). Re-analysis of metabolic data revealed 32 False Discovery Rate controlled genome-wide significant genes, and 12 regions harboring multiple genes; of these 44 regions, 30 were not reported in the original analysis. Conclusion: MGAS allows researchers to conduct their multivariate gene-based analyses efficiently, and without the loss of power that is often associated with an incorrectly specified genotype-phenotype models.
Original languageEnglish
Pages (from-to)1007-1015
JournalBioinformatics
Volume31
Issue number7
DOIs
Publication statusPublished - 2015

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Genome-Wide Association Study
Genome
Genes
Gene
Phenotype
Statistical Power
Genotype
Multivariate Analysis
Composite
Multivariate Analysis of Variance
Testing
Multiple Testing
Single nucleotide Polymorphism
Multiple Regression
Regression Analysis
Univariate
Single Nucleotide Polymorphism
Composite materials
Analysis of Variance
Nucleotides

Cite this

@article{3fb2e07ffaf141d28c165cbbcafb2933,
title = "MGAS: a powerful tool for multivariate gene-based genome-wide association analysis.",
abstract = "Motivation: Standard genome-wide association studies, testing the association between one phenotype and a large number of single nucleotide polymorphisms (SNPs), are limited in two ways: (i) traits are often multivariate, and analysis of composite scores entails loss in statistical power and (ii) gene-based analyses may be preferred, e.g. to decrease the multiple testing problem. Results: Here we present a new method, multivariate gene-based association test by extended Simes procedure (MGAS), that allows gene-based testing of multivariate phenotypes in unrelated individuals. Through extensive simulation, we show that under most trait-generating genotype-phenotype models MGAS has superior statistical power to detect associated genes compared with gene-based analyses of univariate phenotypic composite scores (i.e. GATES, multiple regression), and multivariate analysis of variance (MANOVA). Re-analysis of metabolic data revealed 32 False Discovery Rate controlled genome-wide significant genes, and 12 regions harboring multiple genes; of these 44 regions, 30 were not reported in the original analysis. Conclusion: MGAS allows researchers to conduct their multivariate gene-based analyses efficiently, and without the loss of power that is often associated with an incorrectly specified genotype-phenotype models.",
author = "{van der Sluis}, S. and C.V. Dolan and J. Li and Y. Song and P. Sham and D. Posthuma and M. Li",
year = "2015",
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pages = "1007--1015",
journal = "Bioinformatics",
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MGAS: a powerful tool for multivariate gene-based genome-wide association analysis. / van der Sluis, S.; Dolan, C.V.; Li, J.; Song, Y.; Sham, P.; Posthuma, D.; Li, M.

In: Bioinformatics, Vol. 31, No. 7, 2015, p. 1007-1015.

Research output: Contribution to JournalArticleAcademicpeer-review

TY - JOUR

T1 - MGAS: a powerful tool for multivariate gene-based genome-wide association analysis.

AU - van der Sluis, S.

AU - Dolan, C.V.

AU - Li, J.

AU - Song, Y.

AU - Sham, P.

AU - Posthuma, D.

AU - Li, M.

PY - 2015

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AB - Motivation: Standard genome-wide association studies, testing the association between one phenotype and a large number of single nucleotide polymorphisms (SNPs), are limited in two ways: (i) traits are often multivariate, and analysis of composite scores entails loss in statistical power and (ii) gene-based analyses may be preferred, e.g. to decrease the multiple testing problem. Results: Here we present a new method, multivariate gene-based association test by extended Simes procedure (MGAS), that allows gene-based testing of multivariate phenotypes in unrelated individuals. Through extensive simulation, we show that under most trait-generating genotype-phenotype models MGAS has superior statistical power to detect associated genes compared with gene-based analyses of univariate phenotypic composite scores (i.e. GATES, multiple regression), and multivariate analysis of variance (MANOVA). Re-analysis of metabolic data revealed 32 False Discovery Rate controlled genome-wide significant genes, and 12 regions harboring multiple genes; of these 44 regions, 30 were not reported in the original analysis. Conclusion: MGAS allows researchers to conduct their multivariate gene-based analyses efficiently, and without the loss of power that is often associated with an incorrectly specified genotype-phenotype models.

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