The Polygenic Risk Score Knowledge Base offers a centralized online repository for calculating and contextualizing polygenic risk scores

doi:10.1038/s42003-022-03795-x

The Polygenic Risk Score Knowledge Base offers a centralized online repository for calculating and contextualizing polygenic risk scores

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

14 Scopus citations

Abstract

The process of identifying suitable genome-wide association (GWA) studies and formatting the data to calculate multiple polygenic risk scores on a single genome can be laborious. Here, we present a centralized polygenic risk score calculator currently containing over 250,000 genetic variant associations from the NHGRI-EBI GWAS Catalog for users to easily calculate sample-specific polygenic risk scores with comparable results to other available tools. Polygenic risk scores are calculated either online through the Polygenic Risk Score Knowledge Base (PRSKB; https://prs.byu.edu) or via a command-line interface. We report study-specific polygenic risk scores across the UK Biobank, 1000 Genomes, and the Alzheimer’s Disease Neuroimaging Initiative (ADNI), contextualize computed scores, and identify potentially confounding genetic risk factors in ADNI. We introduce a streamlined analysis tool and web interface to calculate and contextualize polygenic risk scores across various studies, which we anticipate will facilitate a wider adaptation of polygenic risk scores in future disease research.

Original language	English
Article number	899
Journal	Communications Biology
Volume	5
Issue number	1
DOIs	https://doi.org/10.1038/s42003-022-03795-x
State	Published - Dec 2022

Bibliographical note

Publisher Copyright:
© The Author(s) 2022.

Funding

This work was supported by the BrightFocus Foundation and its donors [A2020118F to Miller; A2020161S to Ebbert], the National Institutes of Health [RF1AG054052 to Kauwe; 1P30AG072946-01 to the University of Kentucky Alzheimer’s Disease Research Center; AG068331 to Ebbert; GM138636 to Ebbert], and the Alzheimer’s Association [2019-AARG-644082 to Ebbert]. We also acknowledge Brigham Young University and the University of Kentucky for supporting this research. Data collection and sharing for this project was funded by the Alzheimer’s Disease Neuroimaging Initiative (ADNI) (National Institutes of Health Grant U01 AG024904) and DOD ADNI (Department of Defense award number W81XWH-12-2-0012). ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and through generous contributions from the following: AbbVie, Alzheimer’s Association; Alzheimer’s Drug Discovery Foundation; Araclon Biotech; BioClinica, Inc.; Biogen; Bristol-Myers Squibb Company; CereSpir, Inc.; Cogstate; Eisai Inc.; Elan Pharmaceuticals, Inc.; Eli Lilly and Company; EuroImmun; F. Hoffmann-La Roche Ltd and its affiliated company Genentech, Inc.; Fujirebio; GE Healthcare; IXICO Ltd.; Janssen Alzheimer Immunotherapy Research & Development, LLC.; Johnson & Johnson Pharmaceutical Research & Development LLC.; Lumosity; Lundbeck; Merck & Co., Inc.; Meso Scale Diagnostics, LLC.; NeuroRx Research; Neurotrack Technologies; Novartis Pharmaceuticals Corporation; Pfizer Inc.; Piramal Imaging; Servier; Takeda Pharmaceutical Company; and Transition Therapeutics. The Canadian Institutes of Health Research is providing funds to support ADNI clinical sites in Canada. Private sector contributions are facilitated by the Foundation for the National Institutes of Health (www.fnih.org). The grantee organization is the Northern California Institute for Research and Education, and the study is coordinated by the Alzheimer’s Therapeutic Research Institute at the University of Southern California. ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of Southern California. Data used in preparation of this article were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (adni.loni.usc.edu). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report. Per the ADNI data usage agreement, ADNI affiliations are listed in the Acknowledgements. A full list of ADNI investigators and their affiliations appears in the Acknowledgement List for ADNI Publications and at https://adni.loni.usc.edu/wp-content/uploads/how_to_apply/ADNI_Acknowledgement_List.pdf.

Funders	Funder number
Northern California Institute for Research and Education
University of Southern California
National Institute of Biomedical Imaging and Bioengineering
University of Kentucky
Brigham Young University–Hawaii
DOD ADNI
U.S. Department of Health & Human Services \| NIH \| National Institute on Aging (U.S. National Institute on Aging)
DoD Alzheimer's Disease Neuroimaging Initiative	U01 AG024904
National Institutes of Health (NIH)	1P30AG072946-01, RF1AG054052
BrightFocus Foundation (BrightFocus)	A2020161S, A2020118F
U.S. Department of Defense	W81XWH-12-2-0012
University of Kentucky Alzheimer’s Disease Research Center	GM138636, AG068331
Alzheimer's Association	2019-AARG-644082

ASJC Scopus subject areas

Medicine (miscellaneous)
General Biochemistry, Genetics and Molecular Biology
General Agricultural and Biological Sciences

Access to Document

10.1038/s42003-022-03795-x

Cite this

@article{4c21e0d4d3454a5887f08d5de3de4aae,

title = "The Polygenic Risk Score Knowledge Base offers a centralized online repository for calculating and contextualizing polygenic risk scores",

abstract = "The process of identifying suitable genome-wide association (GWA) studies and formatting the data to calculate multiple polygenic risk scores on a single genome can be laborious. Here, we present a centralized polygenic risk score calculator currently containing over 250,000 genetic variant associations from the NHGRI-EBI GWAS Catalog for users to easily calculate sample-specific polygenic risk scores with comparable results to other available tools. Polygenic risk scores are calculated either online through the Polygenic Risk Score Knowledge Base (PRSKB; https://prs.byu.edu) or via a command-line interface. We report study-specific polygenic risk scores across the UK Biobank, 1000 Genomes, and the Alzheimer{\textquoteright}s Disease Neuroimaging Initiative (ADNI), contextualize computed scores, and identify potentially confounding genetic risk factors in ADNI. We introduce a streamlined analysis tool and web interface to calculate and contextualize polygenic risk scores across various studies, which we anticipate will facilitate a wider adaptation of polygenic risk scores in future disease research.",

author = "Page, \{Madeline L.\} and Vance, \{Elizabeth L.\} and Cloward, \{Matthew E.\} and Ed Ringger and Louisa Dayton and Ebbert, \{Mark T.W.\} and L. Ravdin and M. Lin and A. Lee and G. Chiang and N. Relkin and Santulli, \{R. B.\} and Hynes, \{M. L.\} and M. Seltzer and Flashman, \{L. A.\} and K. Anderson and K. Blank and Perlson, \{G. D.\} and S. Kittur and S. Correia and P. Malloy and S. Salloway and S. Potkin and P. Newhouse and M. Sabbagh and A. Garcia and J. Shi and J. Masdeu and A. Williams and J. Mintzer and A. O{\textquoteright}Connell and A. Mintz and D. Munic and C. Bernick and A. Ritter and Daiello, \{L. A.\} and G. Tremont and Drake, \{J. D.\} and Ott, \{B. R.\} and Sink, \{K. M.\} and M. Yang and J. Cleveland and S. Craft and Williamson, \{J. D.\} and Schultz, \{S. K.\} and Nam, \{K. W.\} and H. Shim and H. Koleva and Jicha, \{G. A.\} and Miller, \{Justin B.\}",

note = "Publisher Copyright: {\textcopyright} The Author(s) 2022.",

year = "2022",

month = dec,

doi = "10.1038/s42003-022-03795-x",

language = "English",

volume = "5",

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T1 - The Polygenic Risk Score Knowledge Base offers a centralized online repository for calculating and contextualizing polygenic risk scores

AU - Page, Madeline L.

AU - Vance, Elizabeth L.

AU - Cloward, Matthew E.

AU - Ringger, Ed

AU - Dayton, Louisa

AU - Ebbert, Mark T.W.

AU - Ravdin, L.

AU - Lin, M.

AU - Lee, A.

AU - Chiang, G.

AU - Relkin, N.

AU - Santulli, R. B.

AU - Hynes, M. L.

AU - Seltzer, M.

AU - Flashman, L. A.

AU - Anderson, K.

AU - Blank, K.

AU - Perlson, G. D.

AU - Kittur, S.

AU - Correia, S.

AU - Malloy, P.

AU - Salloway, S.

AU - Potkin, S.

AU - Newhouse, P.

AU - Sabbagh, M.

AU - Garcia, A.

AU - Shi, J.

AU - Masdeu, J.

AU - Williams, A.

AU - Mintzer, J.

AU - O’Connell, A.

AU - Mintz, A.

AU - Munic, D.

AU - Bernick, C.

AU - Ritter, A.

AU - Daiello, L. A.

AU - Tremont, G.

AU - Drake, J. D.

AU - Ott, B. R.

AU - Sink, K. M.

AU - Yang, M.

AU - Cleveland, J.

AU - Craft, S.

AU - Williamson, J. D.

AU - Schultz, S. K.

AU - Nam, K. W.

AU - Shim, H.

AU - Koleva, H.

AU - Jicha, G. A.

AU - Miller, Justin B.

PY - 2022/12

Y1 - 2022/12

N2 - The process of identifying suitable genome-wide association (GWA) studies and formatting the data to calculate multiple polygenic risk scores on a single genome can be laborious. Here, we present a centralized polygenic risk score calculator currently containing over 250,000 genetic variant associations from the NHGRI-EBI GWAS Catalog for users to easily calculate sample-specific polygenic risk scores with comparable results to other available tools. Polygenic risk scores are calculated either online through the Polygenic Risk Score Knowledge Base (PRSKB; https://prs.byu.edu) or via a command-line interface. We report study-specific polygenic risk scores across the UK Biobank, 1000 Genomes, and the Alzheimer’s Disease Neuroimaging Initiative (ADNI), contextualize computed scores, and identify potentially confounding genetic risk factors in ADNI. We introduce a streamlined analysis tool and web interface to calculate and contextualize polygenic risk scores across various studies, which we anticipate will facilitate a wider adaptation of polygenic risk scores in future disease research.

AB - The process of identifying suitable genome-wide association (GWA) studies and formatting the data to calculate multiple polygenic risk scores on a single genome can be laborious. Here, we present a centralized polygenic risk score calculator currently containing over 250,000 genetic variant associations from the NHGRI-EBI GWAS Catalog for users to easily calculate sample-specific polygenic risk scores with comparable results to other available tools. Polygenic risk scores are calculated either online through the Polygenic Risk Score Knowledge Base (PRSKB; https://prs.byu.edu) or via a command-line interface. We report study-specific polygenic risk scores across the UK Biobank, 1000 Genomes, and the Alzheimer’s Disease Neuroimaging Initiative (ADNI), contextualize computed scores, and identify potentially confounding genetic risk factors in ADNI. We introduce a streamlined analysis tool and web interface to calculate and contextualize polygenic risk scores across various studies, which we anticipate will facilitate a wider adaptation of polygenic risk scores in future disease research.

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ER -

The Polygenic Risk Score Knowledge Base offers a centralized online repository for calculating and contextualizing polygenic risk scores

Abstract

Bibliographical note

Funding

ASJC Scopus subject areas

Access to Document

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S'ORCe Collaborative Group (RENEWED)

Statistical ‘Omics Research Collaborative (S'ORCe)

Cite this

The Polygenic Risk Score Knowledge Base offers a centralized online repository for calculating and contextualizing polygenic risk scores

Abstract

Bibliographical note

Funding

ASJC Scopus subject areas

Access to Document

Other files and links

Fingerprint

Projects

S'ORCe Collaborative Group (RENEWED)

Statistical ‘Omics Research Collaborative (S'ORCe)

Cite this