Detecting Rare Variant Effects Using Extreme Phenotype Sampling in Sequencing Association Studies
Version of Record 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 online: 10 JAN 2013
- Version of Record online: 26 NOV 2012
- Manuscript Accepted: 23 OCT 2012
- Manuscript Revised: 20 OCT 2012
- Manuscript Received: 13 MAY 2012
Disclaimer: Supplementary materials have been peer-reviewed but not copyedited.
Figure S1: EPS test sensitivity to extreme cutoff in DHS triglyceride data The p-values using six EPS association tests using different extreme cutoffs are demonstrated. Each of the three genes, ANGPTL3, ANGPTL4, and ANGPTL5 are tested separately. A test combining all three genes is also included.
Figure S2: Power comparisons with constant effect sizes Simulated power comparisons between four rare variants association tests with all causal variants having a positive effect on phenotype. The five tests are random sample optimal SKAT (RS-SKAT-O), dichotomized extreme phenotype burden test (DEP-Burden), continuous extreme phenotype burden test (CEP-Burden), dichotomized extreme phenotype optimal SKAT (DEP-SKAT-O), and continuous extreme phenotype optimal SKAT (CEP-SKAT-O). The left panel considers the situation where 10% high/low extremes are sampled with the three rows corresponding to 20% (0.6% heritability), 40% (1.2% heritability) and 60% (1.8% heritability) variants in a 3kb region being causal. Three total sample sizes are considered: n = 500, 1000, 2000. The right panel considers the situation where 25% high/low extremes are sampled. Exonic regions are simulated with effect sizes for each causal variant equal to β = 1. Power is estimated by the proportion of tests that detect an association at the α = 10−6 level.
Figure S3: Additional comparison of theoretical and empirical power for CEP-SKAT-O In this setting, 60% of variants were considered causal in a 3kb region. Theoretical power for optimal continuous extreme phenotype SKAT (CEP-SKAT-O) is compared with the empirical power estimated using 300 simulations for each estimate. Four settings are considered: sampling 10% and 20% high/low extreme phenotypes; 80%/20% causal variants have positive/negative effects and 100% causal variants have positive effects.
Table S1: Type I error estimates for Continuous Extreme Phenotype (CEP-SKAT-O)
Table S2: Analysis of the Dallas Heart Study triglyceride data
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