This article is part of the supplement: Genetic Analysis Workshop 17: Unraveling Human Exome Data
Incorporating predicted functions of nonsynonymous variants into gene-based analysis of exome sequencing data: a comparative study
1 Division of Biostatistics, University of Texas School of Public Health, 1200 Herman Presser Drive, Houston, TX 77030, USA
2 Human Genetics Center, University of Texas School of Public Health, 1200 Herman Presser Drive, Houston, TX 77030, USA
BMC Proceedings 2011, 5(Suppl 9):S20 doi:10.1186/1753-6561-5-S9-S20Published: 29 November 2011
Next-generation sequencing has opened up new avenues for the genetic study of complex traits. However, because of the small number of observations for any given rare allele and high sequencing error, it is a challenge to identify functional rare variants associated with the phenotype of interest. Recent research shows that grouping variants by gene and incorporating computationally predicted functions of variants may provide higher statistical power. On the other hand, many algorithms are available for predicting the damaging effects of nonsynonymous variants. Here, we use the simulated mini-exome data of Genetic Analysis Workshop 17 to study and compare the effects of incorporating the functional predictions of single-nucleotide polymorphisms using two popular algorithms, SIFT and PolyPhen-2, into a gene-based association test. We also propose a simple mixture model that can effectively combine test results based on different functional prediction algorithms.