VAAST 2.0: Improved Variant Classification and Disease‐Gene Identification Using a Conservation‐Controlled Amino Acid Substitution Matrix. Issue 6 (8th July 2013)

• Record Type: Journal Article
• Authors: Hu, Hao; Huff, Chad D.; Moore, Barry; Flygare, Steven; Reese, Martin G.; Yandell, Mark
• Is Part Of: Genetic epidemiology. Volume 37:Issue 6(2013)
• Page Start: 622
• Page End: 634
• Publication Date: 2013-07-08
• Subjects: Genetic epidemiology -- Periodicals; Heredity -- Periodicals; Medical geography -- Periodicals; 614
• DOI: 10.1002/gepi.21743
• Languages: English
• ISSNs: 0741-0395

Abstract

<abstract abstract-type="main"> <title>ABSTRACT</title> <p>The need for improved algorithmic support for variant prioritization and disease‐gene identification in personal genomes data is widely acknowledged. We previously presented the Variant Annotation, Analysis, and Search Tool (VAAST), which employs an aggregative variant association test that combines both amino acid substitution (AAS) and allele frequencies. Here we describe and benchmark VAAST 2.0, which uses a novel conservation‐controlled AAS matrix (CASM), to incorporate information about phylogenetic conservation. We show that the CASM approach improves VAAST's variant prioritization accuracy compared to its previous implementation, and compared to SIFT, PolyPhen‐2, and MutationTaster. We also show that VAAST 2.0 outperforms KBAC, WSS, SKAT, and variable threshold (VT) using published case‐control datasets for Crohn disease (<italic>NOD2</italic>), hypertriglyceridemia (<italic>LPL</italic>), and breast cancer (<italic>CHEK2</italic>). VAAST 2.0 also improves search accuracy on simulated datasets across a wide range of allele frequencies, population‐attributable disease risks, and allelic heterogeneity, factors that compromise the accuracies of other aggregative variant association tests. We also demonstrate that, although most aggregative variant association tests are designed for common genetic diseases, these tests can be easily adopted as rare Mendelian disease‐gene finders with a simple ranking‐by‐statistical‐significance protocol, and the performance compares very favorably to state‐of‐art filtering approaches. The latter, despite their popularity, have suboptimal performance especially with the increasing case sample size.</p> </abstract>