Comparing family-based rare variant association tests for dichotomous phenotypes

[摘要] BackgroundIt has been repeatedly stressed that family-based samples suffer less from genetic heterogeneity and that association analyses with family-based samples are expected to be powerful for detecting susceptibility loci for rare disease. Various approaches for rare-variant analysis with family-based samples have been proposed.MethodsIn this report, performances of the existing methods were compared with the simulated data set provided as part of Genetic Analysis Workshop 19 (GAW19). We considered the rare variant transmission disequilibrium test (RV-TDT), generalized estimating equations-based kernel association (GEE-KM) test, an extended combined multivariate and collapsing test for pedigree data (known as Pedigree Combined Multivariate and Collapsing [PedCMC]), gene-level kernel and burden association tests with disease status for pedigree data (PedGene), and the family-based rare variant association test (FARVAT).ResultsThe results show that PedGene and FARVAT are usually the most efficient, and the optimal test statistic provided by FARVAT is robust under different disease models. Furthermore, FARVAT was implemented with C++, which is more computationally faster than other methods.ConclusionsConsidering both statistical and computational efficiency, we conclude that FARVAT is a good choice for rare-variant analysis with extended families.

[发布日期] 2016-10-18

[关键词] Rare Variant;Pedigree Data;Genetic Analysis Workshop;Empirical Size;Rare Variant Association