Rank-Based Robust Tests for Quantitative-Trait Genetic Association Studies
Version of Record online: 21 MAR 2013
© 2013 Wiley Periodicals, Inc.
Volume 37, Issue 4, pages 358–365, May 2013
How to Cite
Li, Q., Li, Z., Zheng, G., Gao, G. and Yu, K. (2013), Rank-Based Robust Tests for Quantitative-Trait Genetic Association Studies. Genet. Epidemiol., 37: 358–365. doi: 10.1002/gepi.21723
- Issue online: 15 APR 2013
- Version of Record online: 21 MAR 2013
- Manuscript Accepted: 20 FEB 2013
- Manuscript Revised: 18 FEB 2013
- Manuscript Received: 27 SEP 2012
- National Science Foundation of China. Grant Number: 61134013
- National Institutes of Health. Grant Numbers: NO1-AR-2-2263, RO1-AR-44422
- Association study;
- genetic models;
Standard linear regression is commonly used for genetic association studies of quantitative traits. This approach may not be appropriate if the trait, on its original or transformed scales, does not follow a normal distribution. A rank-based nonparametric approach that does not rely on any distributional assumptions can be an attractive alternative. Although several nonparametric tests exist in the literature, their performance in the genetic association setting is not well studied. We evaluate various nonparametric tests for the analysis of quantitative traits and propose a new class of nonparametric tests that have robust performance for traits with various distributions and under different genetic models. We demonstrate the advantage of our proposed methods through simulation study and real data applications.