We are pleased to announce that the paper describing pVAAST (the pedigree Variant Annotation, Analysis, and Search Tool has just been published in Nature Biotechnology.

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: A unified test of linkage analysis and rare-variant association for analysis of pedigree sequence data. In: Nature Biotechnology, 2014.

pVAAST is a software tool that searches whole-exome and whole-genome sequence data in families to identify genetic variants that directly influence disease risk. pVAAST analyzes the DNA sequences of patients, their relatives, and healthy people in a highly automated fashion to provide probabilistic predictions of the specific genetic variants and genes that are increasing the risk of developing disease. pVAAST combines the existing variant prioritization and case-control association features in VAAST with a new linkage analysis method specifically designed for sequence data. This model is broadly similar to traditional linkage analysis but is capable of modeling de novo mutations and is more sensitive in scenarios with incomplete penetrance or locus heterogeneity. pVAAST supports dominant, recessive, and de novo inheritance models, and maintains high power across a wide variety of study designs, from monogenic, Mendelian diseases in a single family to highly polygenic, common diseases involving hundreds of families.

In a separate paper published two weeks ago in Cancer Discovery and led by our collaborators at the University of Utah and the University of Melbourne, we used pVAAST to aid in the discovery that rare variants in the gene RINT1 increase the risk of developing breast cancer and Lynch-Syndrome spectrum cancers.

Daniel J Park, Kayoko Tao, Florence Le Calvez-Kelm, Tu Nguyen-Dumont, Nivonirina Robinot, Fleur Hammet, Fabrice Odefrey, Helen Tsimiklis, Zhi L Teo, Louise B Thingholm, others: Rare mutations in RINT1 predispose carriers to breast and Lynch Syndrome-spectrum cancers. In: Cancer Discovery, pp. CD–14, 2014.

Learn more about pVAAST or click here to register to download pVAAST as part of the VAAST software package.

ERSA (Estimation of Recent Shared Ancestry) 2.0 has been released and is now available for registration and download. ERSA 2.0 introduces new methods to identify and mask genomic regions with excess IBD information in whole-genome sequencing data and introduces improvements to increase relationship detection accuracy for full sibling, avuncular, and direct ancestor-descendant relationships and to provide support for detecting consanguinity.

Hao Hu, Chad D. Huff: Detecting statistical interaction between somatic mutational events and germline variation from next-generation sequence data. In: Pac Symp Biocomput, pp. 51–62, 2014.

SGI (Somatic-Germline Interaction) was presented at PSB 2014 in January and is now freely-available for academic use. SGI is a software package designed to identify statistical interaction between germline variants and somatic mutational events from next-generation sequence data.

Hong Li, Gustavo Glusman, Hao Hu, Shankaracharya, Juan Caballero, Robert Hubley, David Witherspoon, Stephen L. Guthery, Denise E. Mauldin, Lynn B. Jorde, Leroy Hood, Jared C. Roach, Chad D. Huff: Relationship Estimation from Whole-Genome Sequence Data. In: PLoS Genet, 10 (1), 2014.