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Fast and Accurate Genome-Wide Association Test of Multiple Quantitative Traits.

TitleFast and Accurate Genome-Wide Association Test of Multiple Quantitative Traits.
Publication TypeJournal Article
Year of Publication2018
AuthorsWu B
Secondary AuthorsPankow JS
JournalComput Math Methods Med
Date Published2018
KeywordsGenome, Genome-Wide Association Study, Humans, Linear Models, Models, Genetic, Phenotype, Polymorphism, Single Nucleotide

Multiple correlated traits are often collected in genetic studies. By jointly analyzing multiple traits, we can increase power by aggregating multiple weak effects and reveal additional insights into the genetic architecture of complex human diseases. In this article, we propose a multivariate linear regression-based method to test the joint association of multiple quantitative traits. It is flexible to accommodate any covariates, has very accurate control of type I errors, and offers very competitive performance. We also discuss fast and accurate significance value computation especially for genome-wide association studies with small-to-medium sample sizes. We demonstrate through extensive numerical studies that the proposed method has competitive performance. Its usefulness is further illustrated with application to genome-wide association analysis of diabetes-related traits in the Atherosclerosis Risk in Communities (ARIC) study. We found some very interesting associations with diabetes traits which have not been reported before. We implemented the proposed methods in a publicly available R package.

Alternate JournalComput Math Methods Med
PubMed ID29743933
PubMed Central IDPMC5878919
Grant ListR01 CA134848 / CA / NCI NIH HHS / United States
R01 GM083345 / GM / NIGMS NIH HHS / United States