- 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.
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.