Mar 17, 2015

Dimensionality and the statistical power of multivariate genome-wide association studies

BioRxiv : the Preprint Server for Biology
Eladio J. Marquez, David Houle

Abstract

Mutations virtually always have pleiotropic effects, yet most genome-wide association studies (GWAS) analyze effects one trait at a time. In order to investigate the performance of a multivariate approach to GWAS, we simulated scenarios where variation in a d- dimensional phenotype space was caused by a known subset of SNPs. Multivariate analyses of variance were then carried out on k traits, where k could be less than, greater than or equal to d . Our results show that power is maximized and false discovery rate (FDR) minimized when the number of traits analyzed, k , matches the true dimensionality of the phenotype being analyzed, d . When true dimensionality is high, the power of a single univariate analysis can be an order of magnitude less than the k=d case, even when the single trait with the largest genetic variance is chosen for analysis. When traits are added to a study in order of their independent genetic variation, the gains in power from increasing k up to d are much larger than the loss in power when k exceeds d . Simulations that explicitly model linkage disequilibrium (LD) indicate that when SNPs in disequilibrium are subjected to multivariate analysis, the magnitude of the apparent effect induced onto null SNPs ...Continue Reading

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Mentioned in this Paper

Genome-Wide Association Study
Study
Univariate Analysis
2-Dimensional
Genome
Dysequilibrium Syndrome
Anatomical Space Structure
Etiology
Genetic Inheritance
Analysis

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