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

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

Epidemiology

Research output: Contribution to journal › Article › peer-review

26 Scopus citations

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.

Original language	English (US)
Article number	6.14
Journal	Current Protocols in Human Genetics
Issue number	SUPPL.81
DOIs	https://doi.org/10.1002/0471142905.hg0614s81
State	Published - 2014

Keywords

Bioinformatics
Computational genomics
Disease-gene identification
Genome-wide association studies
Genomics
Human disease
Next-generation sequencing
Rare-variant association test
Vaast
Variant classification

ASJC Scopus subject areas

Genetics
Genetics(clinical)

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10.1002/0471142905.hg0614s81

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title = "Using vaast to identify disease-associated variants in next-generation sequencing data",

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 = "Bioinformatics, Computational genomics, Disease-gene identification, Genome-wide association studies, Genomics, Human disease, Next-generation sequencing, Rare-variant association test, Vaast, Variant classification",

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doi = "10.1002/0471142905.hg0614s81",

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AU - Kennedy, Brett

AU - Kronenberg, Zev

AU - Hu, Hao

AU - Moore, Barry

AU - Flygare, Steven

AU - Reese, Martin G.

AU - Jorde, Lynn B.

AU - Yandell, Mark

AU - Huff, Chad

PY - 2014

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AB - 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.

KW - Bioinformatics

KW - Computational genomics

KW - Disease-gene identification

KW - Genome-wide association studies

KW - Genomics

KW - Human disease

KW - Next-generation sequencing

KW - Rare-variant association test

KW - Vaast

KW - Variant classification

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ER -

Using vaast to identify disease-associated variants in next-generation sequencing data

Abstract

Keywords

ASJC Scopus subject areas

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