[en] 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.
Research center :
Luxembourg Centre for Systems Biomedicine (LCSB): Experimental Neurobiology (Balling Group)
Disciplines :
Life sciences: Multidisciplinary, general & others
Author, co-author :
Hu, Hao
Roach, Jared C.
Coon, Hilary
Guthery, Stephen L.
Voelkerding, Karl V.
Margraf, Rebecca L.
Durtschi, Jacob D.
Tavtigian, Sean V.
Shankaracharya
Wu, Wilfred
Scheet, Paul
Wang, Shuoguo
Xing, Jinchuan
Glusman, Gustavo
Hubley, Robert
Li, Hong
Garg, Vidu
Moore, Barry
Hood, Leroy
Galas, David J. ; University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB)