Detecting Rare Variant Effects Using Extreme Phenotype Sampling in Sequencing Association Studies
Article first published online: 26 NOV 2012
© 2012 WILEY PERIODICALS, INC.
Volume 37, Issue 2, pages 142–151, February 2013
How to Cite
Barnett, I. J., Lee, S. and Lin, X. (2013), Detecting Rare Variant Effects Using Extreme Phenotype Sampling in Sequencing Association Studies. Genet. Epidemiol., 37: 142–151. doi: 10.1002/gepi.21699
- Issue published online: 10 JAN 2013
- Article first published online: 26 NOV 2012
- Manuscript Accepted: 23 OCT 2012
- Manuscript Revised: 20 OCT 2012
- Manuscript Received: 13 MAY 2012
- complex trait associations;
- selective sampling;
- rare genetic variants;
- extreme phenotype sampling
In the increasing number of sequencing studies aimed at identifying rare variants associated with complex traits, the power of the test can be improved by guided sampling procedures. We confirm both analytically and numerically that sampling individuals with extreme phenotypes can enrich the presence of causal rare variants and can therefore lead to an increase in power compared to random sampling. Although application of traditional rare variant association tests to these extreme phenotype samples requires dichotomizing the continuous phenotypes before analysis, the dichotomization procedure can decrease the power by reducing the information in the phenotypes. To avoid this, we propose a novel statistical method based on the optimal Sequence Kernel Association Test that allows us to test for rare variant effects using continuous phenotypes in the analysis of extreme phenotype samples. The increase in power of this method is demonstrated through simulation of a wide range of scenarios as well as in the triglyceride data of the Dallas Heart Study.