title: VAAST 2.0: improved variant classification and disease-gene identification using a conservation-controlled amino acid substitution matrix.
abstract: 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 (NOD2), hypertriglyceridemia (LPL), and breast cancer (CHEK2). 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.
authors.affiliation: Department of Epidemiology, The University of Texas M.D. Anderson Cancer Center, Houston, Texas, USA.
authors.forename: Hao
authors.initials: H
authors.lastname: Hu
authors.forename: Chad D
authors.initials: CD
authors.lastname: Huff
authors.forename: Barry
authors.initials: B
authors.lastname: Moore
authors.forename: Steven
authors.initials: S
authors.lastname: Flygare
authors.forename: Martin G
authors.initials: MG
authors.lastname: Reese
authors.forename: Mark
authors.initials: M
authors.lastname: Yandell
mesh_terms: D000465:Algorithms; D019943:Amino Acid Substitution; D001943:Breast Neoplasms / Q000235:genetics; D016022:Case-Control Studies; D064447:Checkpoint Kinase 2 / Q000235:genetics; D003424:Crohn Disease / Q000235:genetics; D016208:Databases, Factual; D005260:Female; D005787:Gene Frequency; D020022:Genetic Predisposition to Disease; D014644:Genetic Variation; D006801:Humans; D015228:Hypertriglyceridemia / Q000235:genetics; D008071:Lipoprotein Lipase / Q000235:genetics; D053473:Nod2 Signaling Adaptor Protein / Q000235:genetics; D010802:Phylogeny; D018401:Sample Size; D012984:Software
chemical_list: C429018:NOD2 protein, human; D053473:Nod2 Signaling Adaptor Protein; D064447:Checkpoint Kinase 2; C578634:CHEK2 protein, human; C548261:LPL protein, human; D008071:Lipoprotein Lipase
keywords: aggregative association test; complex disease; disease-gene finder; rare Mendelian disease; variant classifier