Abstract
Genetic studies often collect multiple correlated traits, which could be analyzed jointly to increase power by aggregating multiple weak effects and provide additional insights into the etiology of complex human diseases. Existing methods for multiple trait association tests have primarily focused on common variants. There is a surprising dearth of published methods for testing the association of rare variants with multiple correlated traits. In this paper, we extend the commonly used sequence kernel association test (SKAT) for single-trait analysis to test for the joint association of rare variant sets with multiple traits. We investigate the performance of the proposed method through extensive simulation studies. We further illustrate its usefulness with application to the analysis of diabetes-related traits in the Atherosclerosis Risk in Communities (ARIC) Study. We identified an exome-wide significant rare variant set in the gene YAP1 worthy of further investigations.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 91-100 |
| Number of pages | 10 |
| Journal | Genetic epidemiology |
| Volume | 40 |
| Issue number | 2 |
| DOIs | |
| State | Published - Feb 1 2016 |
Bibliographical note
Funding Information:This research was supported in part by NIH grant GM083345 and CA134848. We are grateful to the University of Minnesota Supercomputing Institute for assistance with the computations, and the ARIC publications committee for helpful comments. We want to thank the editor and reviewer for their constructive comments that have greatly improved the presentation of the paper. The ARIC Study is carried out as a collaborative study sup- ported by National Heart, Lung, and Blood Institute (NHLBI) contracts (HHSN268201100005C, HHSN268201100006C, HHSN268201100007C, HHSN268201100008C, HHSN268201100009C, HHSN2682011000010C, HHSN2682011000011C, and HHSN2682011000012C). The authors thank the staff and participants of the ARIC study for their important contributions. Support for exome chip genotyping in the ARIC Study was provided by the National Institutes of Health (NIH) American Recovery and Reinvestment Act of 2009 (ARRA) (5RC2HL102419).
Funding Information:
This research was supported in part by NIH grant GM083345 and CA134848. We are grateful to the University of Minnesota Supercomputing Institute for assistance with the computations, and the ARIC publications committee for helpful comments. We want to thank the editor and reviewer for their constructive comments that have greatly improved the presentation of the paper. The ARIC Study is carried out as a collaborative study sup- ported by National Heart, Lung, and Blood Institute (NHLBI) contracts (HHSN268201100005C, HHSN268201100006C, HHSN268201100007C, HHSN268201100008C, HHSN268201100009C, HHSN2682011000010C, HHSN2682011000011C, and HHSN2682011000012C). The authors thank the staff and participants of the ARIC study for their important contributions. Support for exome chip genotyping in the ARIC Study was provided by the National Institutes of Health (NIH) American Recovery and Reinvestment Act of 2009 (ARRA) (5RC2HL102419).
Publisher Copyright:
© 2016 Wiley Periodicals, Inc.
Keywords
- GWAS
- Rare variant
- SKAT
- Score statistic
