Gene-set association tests for next-generation sequencing data

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Abstract

Motivation: Recently, many methods have been developed for conducting rare-variant association studies for sequencing data. These methods have primarily been based on gene-level associations but have not been proven to be as effective as expected. Gene-set-level tests have shown great advantages over gene-level tests in terms of power and robustness, because complex diseases are often caused by multiple genes that comprise of biological gene sets.

Results: Here, we propose several novel gene-set tests that employ rapid and efficient dimensionality reduction. The performance of these tests was investigated using extensive simulations and application to 1058 whole-exome sequences from a Korean population. We identified some known pathways and novel pathways whose rare or common variants are associated with elevated liver enzymes and replicated the results in an independent cohort.

Availability and Implementation: Source R code for our algorithm is freely available at http://statgen.snu.ac.kr/software/QTest.

Contact:tspark@stats.snu.ac.kr

Supplementary information: Supplementary data are available at Bioinformatics online.

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