Title |
A unified test of linkage analysis and rare-variant association for analysis of pedigree sequence data
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Published in |
Nature Biotechnology, May 2014
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DOI | 10.1038/nbt.2895 |
Pubmed ID | |
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. |
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Geographical breakdown
Country | Count | As % |
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United States | 4 | 33% |
United Kingdom | 1 | 8% |
Italy | 1 | 8% |
Spain | 1 | 8% |
Montenegro | 1 | 8% |
Unknown | 4 | 33% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Scientists | 9 | 75% |
Members of the public | 3 | 25% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
United States | 5 | 2% |
Netherlands | 1 | <1% |
Sweden | 1 | <1% |
Taiwan | 1 | <1% |
Finland | 1 | <1% |
China | 1 | <1% |
Belgium | 1 | <1% |
Unknown | 199 | 95% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Researcher | 63 | 30% |
Student > Ph. D. Student | 48 | 23% |
Student > Doctoral Student | 15 | 7% |
Professor > Associate Professor | 14 | 7% |
Professor | 12 | 6% |
Other | 40 | 19% |
Unknown | 18 | 9% |
Readers by discipline | Count | As % |
---|---|---|
Agricultural and Biological Sciences | 81 | 39% |
Biochemistry, Genetics and Molecular Biology | 56 | 27% |
Medicine and Dentistry | 23 | 11% |
Computer Science | 8 | 4% |
Mathematics | 6 | 3% |
Other | 14 | 7% |
Unknown | 22 | 10% |