Using VAAST to Identify Disease-Associated Variants in Next-Generation Sequencing Data

Brett Kennedy; Zev Kronenberg; Hao Hu; Barry Moore; Steven Flygare; Martin G Reese; Lynn B Jorde; Mark Yandell; Chad Huff

doi:10.1002/0471142905.hg0614s81

Using VAAST to Identify Disease-Associated Variants in Next-Generation Sequencing Data

Curr Protoc Hum Genet. 2014 Apr 24:81:6.14.1-6.14.25. doi: 10.1002/0471142905.hg0614s81.

Authors

Brett Kennedy^{1

2}, Zev Kronenberg^{1

2}, Hao Hu^{3

2}, Barry Moore¹, Steven Flygare¹, Martin G Reese⁴, Lynn B Jorde¹, Mark Yandell¹, Chad Huff³

Affiliations

¹ Department of Human Genetics, University of Utah School of Medicine, Salt Lake City, Utah.
² These authors collectively are the first authors of the unit.
³ Department of Epidemiology, The University of Texas M.D. Anderson Cancer Center, Houston, Texas.
⁴ Omicia, Inc, Emeryville, California.

Abstract

The VAAST pipeline is specifically designed to identify disease-associated alleles in next-generation sequencing data. In the protocols presented in this paper, we outline the best practices for variant prioritization using VAAST. Examples and test data are provided for case-control, small pedigree, and large pedigree analyses. These protocols will teach users the fundamentals of VAAST, VAAST 2.0, and pVAAST analyses.

Keywords: VAAST; bioinformatics; computational genomics; disease-gene identification; genome-wide association studies; genomics; human disease; next-generation sequencing; rare-variant association test; variant classification.

Publication types

Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't

MeSH terms

Case-Control Studies
Female
Genetic Variation*
High-Throughput Nucleotide Sequencing*
Humans
Male
Pedigree

Abstract

Publication types

MeSH terms

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