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Computational and Mathematical Methods in Medicine
Volume 2018, Article ID 2564531, 9 pages
Research Article

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

1Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, MN, USA
2Division of Epidemiology and Community Health, School of Public Health, University of Minnesota, Minneapolis, MN, USA

Correspondence should be addressed to Baolin Wu; ude.nmu@niloab

Received 27 September 2017; Accepted 24 January 2018; Published 18 March 2018

Academic Editor: Zoran Bursac

Copyright © 2018 Baolin Wu and James S. Pankow. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


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.