Huff Lab

Chad Huff, Ph.D., Assistant Professor at MD Anderson Cancer Center

Pedigree Variant Annotation, Analysis, and Search Tool (pVAAST)

Download pVAAST

pVAAST (the Pedigree Variant Annotation, Analysis, and Search Tool) is a software tool that searches whole-exome and whole-genome sequence data in families to identify genetic variants that directly influence disease risk. pVAAST analyzes the DNA sequences of patients, their relatives, and healthy people in a highly automated fashion to provide probabilistic predictions of the specific genetic variants and genes that are increasing the risk of developing disease. pVAAST combines the existing variant prioritization and case-control association features in VAAST with a new linkage analysis method specifically designed for sequence data. This model is broadly similar to traditional linkage analysis but is capable of modeling de novo mutations and is more sensitive in scenarios with incomplete penetrance or locus heterogeneity. pVAAST supports dominant, recessive, and de novoinheritance models, and maintains high power across a wide variety of study designs, from monogenic, Mendelian diseases in a single family to highly polygenic, common diseases involving hundreds of families.

Public Release

The release of VAAST 2.1.0 includes pVAAST and is now available for download. Click here to register to download.

Related Publications

Hu, Hao; Roach, Jared; Coon, Hilary; Guthery, Stephen; Voelkerding, Karl; Margraf, Rebecca; Durtschi, Jacob; Tavtigian, Sean; Shankaracharya,; Wu, Wilfred; Paul Scheet, Shuoguo Wang; Xing, Jinchuan; Glusman, Gustavo; Hubley, Robert; Li, Hong; Vidu Garg, Barry Moore; Hood, Leroy; Galas, David; Srivastava, Deepak; Reese, Martin; Jorde, Lynn; Yandell, Mark; Huff, Chad (2014): A unified test of linkage analysis and rare-variant association for analysis of pedigree sequence data. In: Nature Biotechnology, 2014. (Type: Article | Abstract | Links | BibTeX)

Park, Daniel; Tao, Kayoko; Calvez-Kelm, Florence Le; Nguyen-Dumont, Tu; Robinot, Nivonirina; Hammet, Fleur; Odefrey, Fabrice; Tsimiklis, Helen; Teo, Zhi; Thingholm, Louise; others, (2014): Rare mutations in RINT1 predispose carriers to breast and Lynch Syndrome-spectrum cancers. In: Cancer Discovery, pp. CD–14, 2014. (Type: Article | Abstract | Links | BibTeX)

Kennedy, Brett; Kronenberg, Zev; Hu, Hao; Moore, Barry; Flygare, Steven; Reese, Martin; Jorde, Lynn; Yandell, Mark; Huff, Chad(2014): Using VAAST to Identify Disease‐Associated Variants in Next‐Generation Sequencing Data. In: Current Protocols in Human Genetics, 2014. (Type: Article | Abstract | Links | BibTeX)

Coon, Hillary; Darlington, Todd; Pimentel, Richard; Smith, Ken; Huff, Chad; Hu, Hao; Jerominski, Leslie; Hansen, Jessica; Klein, Michael; Callor, Brandon; Byrd, Josh; Bakian, Amanda; Crowell, Sheila; McMahon, William; Rajamanickam, Venkatesh; Camp, Nicola; McGlade, Erin; Yurgelun-Todd, Deborah; Grey, Todd; Gray, Douglas (2013): Genetic risk factors in two Utah pedigrees at high risk for suicide. In: Transl Psychiatry, 3, 2013. (Type: Article | Abstract | Links | BibTeX)

Press Coverage

pVAAST 5-Minute Guide for the Impatient

Call the variants in case and control genomes to create VCF files. Ideally, case and control samples should be matched in a) ethnicity; b) sequencing platform; and c) variant calling pipeline. For the best result, we also recommend jointly calling all case and control genomes with GATK UnifiedGenotyper. However, if no control genomes are available, publicly available exomes can be downloaded at: http://www.yandell-lab.org/software/VAAST/data/hg19/Background_CDR/
Run <VAAST>/bin/vaast_tools/vcf2cdr.pl script to convert multi-sample VCF file(s) to CONDENSER (CDR) file format. (See the command line docs for more information.) This script will create one CDR file for each cohort, which can be unrelated cases/controls or families. An example of this step can be found at:<VAAST>/examples/vcf2cdr_example/vcf2cdr.sh
Create the pedigree file (".ped" file; see http://pngu.mgh.harvard.edu/~purcell/plink/data.shtml#ped). Every family should have a separate pedigree file. For sequenced individuals, the IDs in the ".ped" file should match the IDs in the original VCF file (or the ## FILE-INDEX entries at the bottom of CDR files).
Prepare the pVAAST parameter file. You can find several template parameter files in <VAAST>/data/pvaast/folder, each designed for a different type of family and disease model. At a minimum, the options in the "Basic Options" section should be changed. Other sections are non-essential but can improve performance.
Run pVAAST. The basic command line is:
VAAST -m pvaast -pv_control <parameter file> <GFF3 annotation file> <Control CDR file> --gw <max permutations>
For genome-wide significance, --gw value of at least 1e6 is recommended. An example bash script for this step can be found at:
<VAAST>/examples/pvaast_example/pvaast.sh

Notes

Any required external data files in this pipeline can be downloaded at: http://www.yandell-lab.org/software/VAAST/data/hg19/
The ".simple" file provides a quick ranked list of protein coding genes. The ".vaast" file is the complete VAAST report.
By default pVAAST scores only nonsynonymous and null mutations. To enable support for indels and splice sites, use--indel and --splice_site options in the pVAAST command line. CAUTION: indels andsplice_site may result in significant inflation of the false-positive rate when cases and controls are not matched.
For more information or for advanced options, please see the command line documentation, download VAAST documentation at http://www.yandell-lab.org/software/vaast.html, or read a preprint of our recent paper entitled “Identification of damaged genes and disease-causing alleles with VAAST.”
IF YOU GET STUCK, WE WOULD LOVE TO HEAR FROM YOU AND HELP! Our mailing list is gro.bal-llednay@resu-tsaav, and my email address is .