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Cheng WY, Hakenberg J, Li SD, et al. DIVAS: a centralized genetic variant repository representing 150,000 individuals from multiple disease cohorts. Bioinformatics. 2015;32(1):151-3doi: 10.1093/bioinformatics/btv511.
Cheng, W. Y., Hakenberg, J., Li, S. D., & Chen, R. (2016). DIVAS: a centralized genetic variant repository representing 150,000 individuals from multiple disease cohorts. Bioinformatics (Oxford, England), 32(1), 151-3. https://doi.org/10.1093/bioinformatics/btv511
Cheng, Wei-Yi, et al. "DIVAS: a centralized genetic variant repository representing 150,000 individuals from multiple disease cohorts." Bioinformatics (Oxford, England) vol. 32,1 (2016): 151-3. doi: https://doi.org/10.1093/bioinformatics/btv511
Cheng WY, Hakenberg J, Li SD, Chen R. DIVAS: a centralized genetic variant repository representing 150,000 individuals from multiple disease cohorts. Bioinformatics. 2016 Jan 01;32(1):151-3. doi: 10.1093/bioinformatics/btv511. Epub 2015 Sep 12. PMID: 26363178; PMCID: PMC4681987.
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Bioinformatics. 2016 Jan 01;32(1):151-3. doi: 10.1093/bioinformatics/btv511. Epub 2015 Sep 12.

DIVAS: a centralized genetic variant repository representing 150,000 individuals from multiple disease cohorts.

Bioinformatics (Oxford, England)

Wei-Yi Cheng, Jörg Hakenberg, Shuyu Dan Li, Rong Chen

Affiliations

  1. Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA.

PMID: 26363178 PMCID: PMC4681987 DOI: 10.1093/bioinformatics/btv511

Abstract

MOTIVATION: A plethora of sequenced and genotyped disease cohorts is available to the biomedical research community, spread across many portals and represented in various formats.

RESULTS: We have gathered several large studies, including GERA and GRU, and computed population- and disease-specific genetic variant frequencies. In total, our portal provides fast access to genetic variants observed in 84,928 individuals from 39 disease populations. We also include 66,335 controls, such as the 1000 Genomes and Scripps Wellderly.

CONCLUSION: Combining multiple studies helps validate disease-associated variants in each underlying data set, detect potential false positives using frequencies of control populations, and identify novel candidate disease-causing alterations in known or suspected genes.

AVAILABILITY AND IMPLEMENTATION: https://rvs.u.hpc.mssm.edu/divas

CONTACT: [email protected]

SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

© The Author 2015. Published by Oxford University Press.

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