title: Beyond Rare-Variant Association Testing: Pinpointing Rare Causal Variants in Case-Control Sequencing Study.
abstract: Rare-variant association testing usually requires some method of aggregation. The next important step is to pinpoint individual rare causal variants among a large number of variants within a genetic region. Recently Ionita-Laza et al. propose a backward elimination (BE) procedure that can identify individual causal variants among the many variants in a gene. The BE procedure removes a variant if excluding this variant can lead to a smaller P-value for the BURDEN test (referred to as "BE-BURDEN") or the SKAT test (referred to as "BE-SKAT"). We here use the adaptive combination of P-values (ADA) method to pinpoint causal variants. Unlike most gene-based association tests, the ADA statistic is built upon per-site P-values of individual variants. It is straightforward to select important variants given the optimal P-value truncation threshold found by ADA. We performed comprehensive simulations to compare ADA with BE-SKAT and BE-BURDEN. Ranking these three approaches according to positive predictive values (PPVs), the percentage of truly causal variants among the total selected variants, we found ADA > BE-SKAT > BE-BURDEN across all simulation scenarios. We therefore recommend using ADA to pinpoint plausible rare causal variants in a gene.
authors.affiliation: Institute of Epidemiology and Preventive Medicine, College of Public Health, National Taiwan University, Taipei, Taiwan.|Department of Public Health, College of Public Health, National Taiwan University, Taipei, Taiwan.
authors.forename: Wan-Yu
authors.initials: WY
authors.lastname: Lin
mesh_terms: D000483:Alleles; D000075803:Angiopoietin-Like Protein 4; D042682:Angiopoietins / Q000097:blood / Q000235:genetics*; D016022:Case-Control Studies; D003198:Computer Simulation; D005260:Female; D015870:Gene Expression; D005787:Gene Frequency; D056726:Genetic Association Studies; D059014:High-Throughput Nucleotide Sequencing / Q000706:statistics & numerical data*; D006801:Humans; D015228:Hypertriglyceridemia / Q000097:blood / Q000175:diagnosis / Q000235:genetics*; D008297:Male; D008957:Models, Genetic; D011110:Polymorphism, Genetic; D014280:Triglycerides / Q000097:blood*
chemical_list: C474772:ANGPTL4 protein, human; D000075803:Angiopoietin-Like Protein 4; D042682:Angiopoietins; D014280:Triglycerides