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

Bioinformatics. 2015 Apr 1;31(7):1007-15. doi: 10.1093/bioinformatics/btu783. Epub 2014 Nov 26.


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.

Availability and implementation: MGAS is freely available in KGG v3.0 ( Access to the metabolic dataset can be requested at dbGaP ( The R-simulation code is available from

Supplementary information: Supplementary data are available at Bioinformatics online.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Genome, Human*
  • Genome-Wide Association Study*
  • Humans
  • Metabolic Syndrome / genetics*
  • Multivariate Analysis*
  • Phenotype
  • Polymorphism, Single Nucleotide / genetics*
  • Software*