A unified test of linkage analysis and rare-variant association for analysis of pedigree sequence data
- 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;
- Srivastava, Deepak;
- Reese, Martin G;
- Jorde, Lynn B;
- Yandell, Mark;
- Huff, Chad D
- et al.
Published Web Location
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4157619/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.
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