The overarching goal of this proposal is to produce a single deliverable: VAAST+, which will provide innovative and improved solutions for three major bottlenecks in analyses of personal genomes data: variant prioritization, risk assessment and disease-gene finding. Better variant prioritization and risk assessment will aid diagnostic laboratories and clinicians seeking to interpret the impact of rare variants discovered in the course of routine genetic testing;whereas a better tool for disease-gene finding will empower researchers seeking to employ whole-genome and exome sequences to identify novel genes and disease-causing alleles responsible for rare and common diseases. VAAST+ will leverage the VAAST platform, which was developed with support from an NHGRI Grand Opportunity Grant entitled Tool for annotation and analyses of human whole-genome sequence variation data. Doing so will allow us to rapidly implement VAAST+ and distribute it to the research community.

Agency
National Institute of Health (NIH)
Type
Research Project (R01)
Project #
5R01GM104390-02
Application #
8721455
Study Section
Special Emphasis Panel (ZGM1)
Program Officer
Krasnewich, Donna M
Project Start
Project End
Budget Start
Budget End
Support Year
2
Fiscal Year
2014
Total Cost
Indirect Cost
Name
University of Utah
Department
Genetics
Type
Schools of Medicine
DUNS #
City
Salt Lake City
State
UT
Country
United States
Zip Code
84112
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Campbell, Michael S; Holt, Carson; Moore, Barry et al. (2014) Genome Annotation and Curation Using MAKER and MAKER-P. Curr Protoc Bioinformatics 48:4.11.1-4.11.39
Domyan, Eric T; Guernsey, Michael W; Kronenberg, Zev et al. (2014) Epistatic and combinatorial effects of pigmentary gene mutations in the domestic pigeon. Curr Biol 24:459-64
Hu, Hao; Roach, Jared C; Coon, Hilary et al. (2014) A unified test of linkage analysis and rare-variant association for analysis of pedigree sequence data. Nat Biotechnol 32:663-9
Singleton, Marc V; Guthery, Stephen L; Voelkerding, Karl V et al. (2014) Phevor combines multiple biomedical ontologies for accurate identification of disease-causing alleles in single individuals and small nuclear families. Am J Hum Genet 94:599-610