Several alternative burden methods are largely based on the same approach. Examples of the burden tests include the cohort allelic sum test, 8 the combined multivariate and collapsing method, 9 and the nonparametric weighted sum test (WST), 10 which imposes weights when collapsing rare variants. These tests can be broadly classified as burden and nonburden tests.īurden tests collapse rare variants in a genetic region into a single burden variable and then regress the phenotype on the burden variable to test for the cumulative effects of rare variants in the region. Instead, the statistical development of rare-variant analysis has been focused on testing cumulative effects of rare variants in genetic regions or SNP sets, such as genes. Single-variant tests are typically conducted to investigate associations of common variants and phenotypes however the same approach has little power for testing for rare-variant effects because of their low frequencies and large numbers. In recent years, significant efforts have been devoted to developing powerful and computationally efficient statistical methods for testing associations between rare variants and complex traits. 3,4 Several complex traits have been found to be associated with rare variants. Rare variants, which have minor allele frequencies (MAFs) of less than 0.01∼0.05, might play an important role in the etiology of complex traits and account for missing heritability unexplained by common variants.
These emerging sequencing technologies provide a rich opportunity to study the association between rare variants and complex traits. The recent advance of massively parallel sequencing technologies 1,2 has transformed human genetic research. We evaluate the finite sample performance of the proposed methods using extensive simulation studies and illustrate their application using the acute-lung-injury exome-sequencing data of the National Heart, Lung, and Blood Institute Exome Sequencing Project.Īrray-based genotyping technologies have been used successfully in hundreds of genome-wide association studies in the last few years for identifying over one thousand common genetic variants associated with many complex diseases. Both small-sample-adjusted SKAT and the optimal unified test (SKAT-O) are computationally efficient and can easily be applied to genome-wide sequencing association studies. The second goal of this paper is to develop a small-sample adjustment procedure for the proposed methods for the correction of conservative type I error rates of SKAT family tests when the trait of interest is dichotomous and the sample size is small. We show that the unified test corresponds to the optimal test in an extended family of SKAT tests, which we refer to as SKAT-O.
The proposed unified test maintains the power in both scenarios. Burden tests are more powerful when most variants in a region are causal and the effects are in the same direction, whereas SKAT is more powerful when a large fraction of the variants in a region are noncausal or the effects of causal variants are in different directions. This approach maximizes power by adaptively using the data to optimally combine the burden test and the nonburden sequence kernel association test (SKAT).
We propose in this paper a unified approach for testing the association between rare variants and phenotypes in sequencing association studies.
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