Using VAAST to Identify Disease-Associated Variants in Next-Generation Sequencing Data.
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.
Department of Human Genetics, University of Utah School of Medicine, Salt Lake City, Utah.|These authors collectively are the first authors of the unit.
Brett
B
Kennedy
Department of Human Genetics, University of Utah School of Medicine, Salt Lake City, Utah.|These authors collectively are the first authors of the unit.
Zev
Z
Kronenberg
Department of Epidemiology, The University of Texas M.D. Anderson Cancer Center, Houston, Texas.|These authors collectively are the first authors of the unit.
Hao
H
Hu
Department of Human Genetics, University of Utah School of Medicine, Salt Lake City, Utah.
Barry
B
Moore
Department of Human Genetics, University of Utah School of Medicine, Salt Lake City, Utah.
Steven
S
Flygare
Omicia, Inc, Emeryville, California.
Martin G
MG
Reese
Department of Human Genetics, University of Utah School of Medicine, Salt Lake City, Utah.
Lynn B
LB
Jorde
Department of Human Genetics, University of Utah School of Medicine, Salt Lake City, Utah.
Mark
M
Yandell
Department of Epidemiology, The University of Texas M.D. Anderson Cancer Center, Houston, Texas.
Chad
C
Huff
D016022:Case-Control Studies; D005260:Female; D014644:Genetic Variation; D059014:High-Throughput Nucleotide Sequencing; D006801:Humans; D008297:Male; D010375:Pedigree
VAAST; bioinformatics; computational genomics; disease-gene identification; genome-wide association studies; genomics; human disease; next-generation sequencing; rare-variant association test; variant classification