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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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