Abstract
There is solid evidence that rare variants contribute to complex disease etiology. Next-generation sequencing technologies make it possible to uncover rare variants within candidate genes, exomes, and genomes. Working in a novel framework, the kernel-based adaptive cluster (KBAC) was developed to perform powerful gene/locus based rare variant association testing. The KBAC combines variant classification and association testing in a coherent framework. Covariates can also be incorporated in the analysis to control for potential confounders including age, sex, and population substructure. To evaluate the power of KBAC: 1) variant data was simulated using rigorous population genetic models for both Europeans and Africans, with parameters estimated from sequence data, and 2) phenotypes were generated using models motivated by complex diseases including breast cancer and Hirschsprung's disease. It is demonstrated that the KBAC has superior power compared to other rare variant analysis methods, such as the combined multivariate and collapsing and weight sum statistic. In the presence of variant misclassification and gene interaction, association testing using KBAC is particularly advantageous. The KBAC method was also applied to test for associations, using sequence data from the Dallas Heart Study, between energy metabolism traits and rare variants in ANGPTL 3,4,5 and 6 genes. A number of novel associations were identified, including the associations of high density lipoprotein and very low density lipoprotein with ANGPTL4. The KBAC method is implemented in a user-friendly R package.
Highlights
There is great interest in investigating the etiology of complex disease due to rare variants [1,2,3,4,5,6]
The power varies dependent on the underlying model used to generate the data, in all cases the kernel-based adaptive cluster (KBAC) is the most powerful method followed by the weighted sum statistic (WSS), combined multivariate and collapsing (CMC) and the RVE
When non-causal variants are included in the analysis, KBAC is consistently more powerful and more robust than the other three methods
Summary
There is great interest in investigating the etiology of complex disease due to rare variants [1,2,3,4,5,6]. Indirect mapping of common variants has been the emphasis of complex trait association studies. For mapping complex diseases due to common variants, instead of genotyping functional variants, tagSNPs are genotyped which act as a proxy for the underlying causal variants. For rare variant association studies, indirect mapping is not an optimal approach due to low correlations (r2) between tagSNPs and rare variants. Direct mapping should be used, where functional variants are analyzed. Next-generation sequencing technologies e.g. Roche 454, ABI SOLiD, and Illumina HiSeq, have made it feasible to carry-out rare variant association studies of candidate regions, exomes and genomes
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