Current estimates place the number of personal variants at approximately 4 million per genome. Given the rapid advances in genome sequencing technologies and the future democratization of human genome sequencing, small groups and even individual scientists will soon be performing their own human genome projects. We believe that the ability to automatically annotate the millions of variants that these projects will produce, to combine data from multiple projects, and to recover subsets of annotated variants for diverse downstream analyses will become a critical analysis bottleneck. Despite the need, there are no publically available tools that automate these procedures. In response to the NHGRI's RFA """"""""Development and Application of Statistical and Computational Data Analysis Methods for DNA Sequence, Variation, GWAS, Genomic Function, Chemical Biology and Related Genomic Data Sets"""""""" we propose in this GO grant to develop a standalone software tool called VAAST-Variant Annotation, Analysis and Selection Tool. This system will fulfill NHGRI's need for a technology to assess data quality and call variants and will allow for analysis of data from all sequencing centers and will be useable for data from all sequencing platforms. We believe VAAST will fill a huge void in the software landscape by helping individual scientists to extract meaningful results from whole genome variant files.

Public Health Relevance

It is now known that on average any two individual human genomes differ by approximately 4 million positions. These differences, called sequence variants, underlie the inherited physical differences between individuals, including their predisposition to develop certain diseases. This project proposes to develop a tool called VAAST- Variation Annotation, Analysis and Selection Tool. VAAST will help researchers sort through these millions of variants in their quest to identify which of them underlie different phenotypic traits of individuals and susceptibility to diseases.

National Institute of Health (NIH)
National Human Genome Research Institute (NHGRI)
High Impact Research and Research Infrastructure Programs (RC2)
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Study Section
Special Emphasis Panel (ZHG1-HGR-N (O1))
Program Officer
Brooks, Lisa
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University of Utah
Schools of Medicine
Salt Lake City
United States
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Flygare, Steven; Hernandez, Edgar Javier; Phan, Lon et al. (2018) The VAAST Variant Prioritizer (VVP): ultrafast, easy to use whole genome variant prioritization tool. BMC Bioinformatics 19:57
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
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 81:6.14.1-25
Shapiro, Michael D; Kronenberg, Zev; Li, Cai et al. (2013) Genomic diversity and evolution of the head crest in the rock pigeon. Science 339:1063-7
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
Yandell, Mark; Huff, Chad; Hu, Hao et al. (2011) A probabilistic disease-gene finder for personal genomes. Genome Res 21:1529-42
Rope, Alan F; Wang, Kai; Evjenth, Rune et al. (2011) Using VAAST to identify an X-linked disorder resulting in lethality in male infants due to N-terminal acetyltransferase deficiency. Am J Hum Genet 89:28-43
Moore, Barry; Hu, Hao; Singleton, Marc et al. (2011) Global analysis of disease-related DNA sequence variation in 10 healthy individuals: implications for whole genome-based clinical diagnostics. Genet Med 13:210-7
Lyon, Gholson J; Jiang, Tao; Van Wijk, Richard et al. (2011) Exome sequencing and unrelated findings in the context of complex disease research: ethical and clinical implications. Discov Med 12:41-55

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