| Literature DB >> 24837662 |
Hao Hu1, Jared C Roach2, Hilary Coon3, Stephen L Guthery4, Karl V Voelkerding5, Rebecca L Margraf6, Jacob D Durtschi6, Sean V Tavtigian7, Wilfred Wu8, Paul Scheet1, Shuoguo Wang9, Jinchuan Xing9, Gustavo Glusman2, Robert Hubley2, Hong Li2, Vidu Garg10, Barry Moore8, Leroy Hood2, David J Galas11, Deepak Srivastava12, Martin G Reese13, Lynn B Jorde8, Mark Yandell8, Chad D Huff1.
Abstract
High-throughput sequencing of related individuals has become an important tool for studying human disease. However, owing to technical complexity and lack of available tools, most pedigree-based sequencing studies rely on an ad hoc combination of suboptimal analyses. Here we present pedigree-VAAST (pVAAST), a disease-gene identification tool designed for high-throughput sequence data in pedigrees. pVAAST uses a sequence-based model to perform variant and gene-based linkage analysis. Linkage information is then combined with functional prediction and rare variant case-control association information in a unified statistical framework. pVAAST outperformed linkage and rare-variant association tests in simulations and identified disease-causing genes from whole-genome sequence data in three human pedigrees with dominant, recessive and de novo inheritance patterns. The approach is robust to incomplete penetrance and locus heterogeneity and is applicable to a wide variety of genetic traits. pVAAST maintains high power across studies of monogenic, high-penetrance phenotypes in a single pedigree to highly polygenic, common phenotypes involving hundreds of pedigrees.Entities:
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Year: 2014 PMID: 24837662 PMCID: PMC4157619 DOI: 10.1038/nbt.2895
Source DB: PubMed Journal: Nat Biotechnol ISSN: 1087-0156 Impact factor: 54.908