Taking sequence to bedside is the stated and primary focus of the NHGRI for the next 5 years. Ironically, cheap genome sequencing is now producing analysis bottlenecks, especially as regards the clinical interpretation of genetic variants. The purpose of this SBIR FastTrack proposal is to obtain funding for the integration of two innovative analysis tools, developed by the PIs in parts by other NIH sponsored projects, to produce an integrated system called CGIS for sequenced-based clinical diagnostics using personal genome sequences. CGIS will greatly speed the clinical decision making process, and hence has enormous potential for commercial impact. Early adopters and collaborators include ARUP, one of the nation's top tier diagnostic, CLIA regulated, reference laboratories.

Public Health Relevance

The purpose of this application is to build an integrated system that will reduce the time and costs for personal genome analysis by at least a factor of 100, while at the same time improve analysis quality. Specifically we are requesting funding for the integration of two innovative analysis tools, both developed by the PIs in part with NIH support, in order to produce an integrated system for sequence variant prioritization for purposes of clinical diagnostics.

Agency
National Institute of Health (NIH)
Institute
National Human Genome Research Institute (NHGRI)
Type
Small Business Innovation Research Grants (SBIR) - Phase II (R44)
Project #
5R44HG006579-03
Application #
8542887
Study Section
Special Emphasis Panel (ZHG1-HGR-M (O3))
Program Officer
Sofia, Heidi J
Project Start
2012-06-06
Project End
2014-08-31
Budget Start
2013-09-01
Budget End
2014-08-31
Support Year
3
Fiscal Year
2013
Total Cost
$781,754
Indirect Cost
Name
Omicia, Inc.
Department
Type
DUNS #
148382315
City
Emeryville
State
CA
Country
United States
Zip Code
94608
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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
Kennedy, Brett; Kronenberg, Zev; Hu, Hao et al. (2014) Using VAAST to Identify Disease-Associated Variants in Next-Generation Sequencing Data. Curr Protoc Hum Genet 81:6.14.1-6.14.25
Coonrod, Emily M; Margraf, Rebecca L; Russell, Archie et al. (2013) Clinical analysis of genome next-generation sequencing data using the Omicia platform. Expert Rev Mol Diagn 13:529-40
Hu, Hao; Huff, Chad D; Moore, Barry et al. (2013) VAAST 2.0: improved variant classification and disease-gene identification using a conservation-controlled amino acid substitution matrix. Genet Epidemiol 37:622-34