Journal
BIOINFORMATICS
Volume 31, Issue 7, Pages 1007-1015Publisher
OXFORD UNIV PRESS
DOI: 10.1093/bioinformatics/btu783
Keywords
-
Categories
Funding
- Netherlands Scientific Organization [NWO/MaGW] [VIDI-452-12-014]
- European Research Council [Genetics of Mental Illness] [ERC-230374]
- Hong Kong Research Grants Council [GRF] [HKU 768610M, HKU 776412M, HKU 777511M]
- Hong Kong Research Grants Council Theme-Based Research Scheme [T12-705/11]
- European Community Seventh Framework Programme Grant on European Network of National Schizophrenia Networks Studying Gene-Environment Interactions (EU-GEI)
- Hong Kong Health and Medical Research Fund [01121436, 01121726, 02132236]
- HKU Seed Funding Programme for Basic Research [201302159006]
- University of Hong Kong Strategic Research Theme on Genomics
- NWO Medium Investment grant [480-05-003]
- VU University Amsterdam, the Netherlands
- Dutch Brain Foundation
Ask authors/readers for more resources
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.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available