Abstract
Statistical association tests for rare variants can be classified as the burden approach and the sequence kernel association test (SKAT) approach. The burden and SKAT approaches, originally developed for case–control analysis, have also been extended to family-based tests. In the presence of both case–control and family data for a study, joint analysis for the combined data set can increase the statistical power. We extended the Combined Association in the Presence of Linkage (CAPL) test, using both case–control and family data for testing common variants, to rare variant association analysis. The burden and SKAT algorithms were applied to the CAPL test. We used simulations to verify that the CAPL tests incorporating the burden and SKAT algorithms have correct type I error rates. Power studies suggested that both tests have adequate power to identify rare variants associated with the disease. We applied the tests to the Genetic Analysis Workshop 19 data set using the combined family and case–control data for hypertension. The analysis identified several candidate genes for hypertension.
Highlights
Rare variants may contribute to a large portion of disease risks [1]
As we focused on analyzing rare variants, variants with minor allele frequencies (MAFs) greater than 5 % were removed
The significance for the Combined Association in the Presence of Linkage (CAPL)-burden and the CAPL-sequence kernel association test (SKAT) statistics are assessed with their bootstrap statistics under the null hypothesis
Summary
Rare variants may contribute to a large portion of disease risks [1]. Rare variant association tests can be classified as the burden test [2] and the sequence kernel association test (SKAT) [3]. The burden test, assuming variants have the same direction of effects on a disease, collapses minor alleles at variants in a region and compares the difference in allele frequencies for the collapsed alleles between cases and controls. SKAT uses a regression framework and a variance-component test to consider variants with different directions of effects. The burden and SKAT approaches, originally developed for case–control analysis, have been extended to familybased tests [4, 5]. FamSKAT [6], which accounts for familial correlation based on kinship coefficients in a linear mixed model, may be able to use both family and unrelated samples
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