A unified test of linkage analysis and rare-variant association for analysis of pedigree sequence data View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2014-07

AUTHORS

Hao Hu, Jared C Roach, Hilary Coon, Stephen L Guthery, Karl V Voelkerding, Rebecca L Margraf, Jacob D Durtschi, Sean V Tavtigian, Shankaracharya, Wilfred Wu, Paul Scheet, Shuoguo Wang, Jinchuan Xing, Gustavo Glusman, Robert Hubley, Hong Li, Vidu Garg, Barry Moore, Leroy Hood, David J Galas, Deepak Srivastava, Martin G Reese, Lynn B Jorde, Mark Yandell, Chad D Huff

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. More... »

PAGES

663-669

References to SciGraph publications

Identifiers

URI

http://scigraph.springernature.com/pub.10.1038/nbt.2895

DOI

http://dx.doi.org/10.1038/nbt.2895

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1036221211

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/24837662


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