Selection, optimization and validation of ten chronic disease polygenic risk scores for clinical implementation in diverse US populations.

GIANT Consortium; All of Us Research Program

Publisher: Nature Publishing Company Country of Publication: United States NLM ID: 9502015 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1546-170X (Electronic) Linking ISSN: 10788956 NLM ISO Abbreviation: Nat Med Subsets: MEDLINE

Publication: New York Ny : Nature Publishing Company
Original Publication: New York, NY : Nature Pub. Co., [1995-

Chronic Disease* ; Genetic Risk Score* ; Population Health*; Adult ; Child ; Humans ; Communication ; Genetic Predisposition to Disease ; Genome-Wide Association Study ; Risk Factors ; United States

Polygenic risk scores (PRSs) have improved in predictive performance, but several challenges remain to be addressed before PRSs can be implemented in the clinic, including reduced predictive performance of PRSs in diverse populations, and the interpretation and communication of genetic results to both providers and patients. To address these challenges, the National Human Genome Research Institute-funded Electronic Medical Records and Genomics (eMERGE) Network has developed a framework and pipeline for return of a PRS-based genome-informed risk assessment to 25,000 diverse adults and children as part of a clinical study. From an initial list of 23 conditions, ten were selected for implementation based on PRS performance, medical actionability and potential clinical utility, including cardiometabolic diseases and cancer. Standardized metrics were considered in the selection process, with additional consideration given to strength of evidence in African and Hispanic populations. We then developed a pipeline for clinical PRS implementation (score transfer to a clinical laboratory, validation and verification of score performance), and used genetic ancestry to calibrate PRS mean and variance, utilizing genetically diverse data from 13,475 participants of the All of Us Research Program cohort to train and test model parameters. Finally, we created a framework for regulatory compliance and developed a PRS clinical report for return to providers and for inclusion in an additional genome-informed risk assessment. The initial experience from eMERGE can inform the approach needed to implement PRS-based testing in diverse clinical settings.
(© 2024. The Author(s).)

Update of: medRxiv. 2023 Jun 05:2023.05.25.23290535. doi: 10.1101/2023.05.25.23290535. (PMID: 37333246)

Lambert, S. A. et al. The Polygenic Score Catalog as an open database for reproducibility and systematic evaluation. Nat. Genet. 53, 420–425 (2021). (PMID: 10.1038/s41588-021-00783-533692568)
Lewis, C. M. & Vassos, E. Polygenic risk scores: from research tools to clinical instruments. Genome Med. 12, 44 (2020). (PMID: 10.1186/s13073-020-00742-5324234907236300)
Polygenic Risk Score Task Force of the International Common Disease Alliance. Responsible use of polygenic risk scores in the clinic: potential benefits, risks and gaps. Nat. Med. 27, 1876–1884 (2021). (PMID: 10.1038/s41591-021-01549-6)
Torkamani, A., Wineinger, N. E. & Topol, E. J. The personal and clinical utility of polygenic risk scores. Nat. Rev. Genet. 19, 581–590 (2018). (PMID: 10.1038/s41576-018-0018-x29789686)
Duncan, L. et al. Analysis of polygenic risk score usage and performance in diverse human populations. Nat. Commun. 10, 3328 (2019). (PMID: 10.1038/s41467-019-11112-0313461636658471)
Hurson, A. N. et al. Prospective evaluation of a breast-cancer risk model integrating classical risk factors and polygenic risk in 15 cohorts from six countries. Int. J. Epidemiol. 50, 1897–1911 (2022). (PMID: 10.1093/ije/dyab03634999890)
Inouye, M. et al. Genomic risk prediction of coronary artery disease in 480,000 adults: implications for primary prevention. J. Am. Coll. Cardiol. 72, 1883–1893 (2018). (PMID: 10.1016/j.jacc.2018.07.079303094646176870)
Guo, F. et al. Polygenic risk score for defining personalized surveillance intervals after adenoma detection and removal at colonoscopy. Clin. Gastroenterol. Hepatol. 21, 210–219.e11 (2023). (PMID: 10.1016/j.cgh.2022.03.01335331942)
Fantus, R. J. & Helfand, B. T. Germline genetics of prostate cancer: time to incorporate genetics into early detection tools. Clin. Chem. 65, 74–79 (2019). (PMID: 10.1373/clinchem.2018.28665830459162)
Pharoah, P. D. P., Antoniou, A. C., Easton, D. F. & Ponder, B. A. J. Polygenes, risk prediction, and targeted prevention of breast cancer. N. Engl. J. Med. 358, 2796–2803 (2008). (PMID: 10.1056/NEJMsa070873918579814)
Willoughby, A., Andreassen, P. R. & Toland, A. E. Genetic testing to guide risk-stratified screens for breast cancer. J. Pers. Med 9, 15 (2019). (PMID: 10.3390/jpm9010015308322436462925)
Martin, A. R. et al. Clinical use of current polygenic risk scores may exacerbate health disparities. Nat. Genet. 51, 584–591 (2019). (PMID: 10.1038/s41588-019-0379-x309269666563838)
Mars, N. et al. Systematic comparison of family history and polygenic risk across 24 common diseases. Am. J. Hum. Genet. 109, 2152–2162 (2022). (PMID: 10.1016/j.ajhg.2022.10.009363472559748261)
Wang, Z. et al. The value of rare genetic variation in the prediction of common obesity in European ancestry populations. Front. Endocrinol. 13, 863893 (2022).
Ruan, Y. et al. Improving polygenic prediction in ancestrally diverse populations. Nat. Genet. 54, 573–580 (2022). (PMID: 10.1038/s41588-022-01054-7355137249117455)
Márquez-Luna, C., Loh, P.-R., South Asian Type 2 Diabetes (SAT2D) Consortium & Price, A. L. Multiethnic polygenic risk scores improve risk prediction in diverse populations. Genet. Epidemiol. 41, 811–823 (2017). (PMID: 10.1002/gepi.22083291103305726434)
Hujoel, M. L. A., Loh, P.-R., Neale, B. M. & Price, A. L. Incorporating family history of disease improves polygenic risk scores in diverse populations. Cell Genom. 2, 100152 (2022). (PMID: 10.1016/j.xgen.2022.100152359359189351615)
Elliott, J. et al. Predictive accuracy of a polygenic risk score-enhanced prediction model vs a clinical risk score for coronary artery disease. JAMA 323, 636–645 (2020). (PMID: 10.1001/jama.2019.22241320688187042853)
Folkersen, L. et al. Impute.me: an open-source, non-profit tool for using data from direct-to-consumer genetic testing to calculate and interpret polygenic risk scores. Front. Genet. 11, 515901 (2020). (PMID: 10.3389/fgene.2020.00578)
Hao, L. et al. Development of a clinical polygenic risk score assay and reporting workflow. Nat. Med. 28, 1006–1013 (2022). (PMID: 10.1038/s41591-022-01767-6354373329117136)
Vassy, J. L. et al. Cardiovascular disease risk assessment using traditional risk factors and polygenic risk scores in the million veteran program. JAMA Cardiol. 8, 564–574 (2023). (PMID: 10.1001/jamacardio.2023.085737133828)
Linder, J. E. et al. Returning integrated genomic risk and clinical recommendations: the eMERGE study. Genet. Med. 25, 100006 (2023). (PMID: 10.1016/j.gim.2023.1000063662188010085845)
McCarty, C. A. et al. The eMERGE Network: a consortium of biorepositories linked to electronic medical records data for conducting genomic studies. BMC Med. Genomics 4, 13 (2011). (PMID: 10.1186/1755-8794-4-13212694733038887)
Gottesman, O. et al. The Electronic Medical Records and Genomics (eMERGE) Network: past, present, and future. Genet. Med. 15, 761–771 (2013). (PMID: 10.1038/gim.2013.72237435513795928)
NIMHD. Overview. https://www.nimhd.nih.gov/about/overview/ . Accessed 11 Dec 2023.
Wand, H. et al. Improving reporting standards for polygenic scores in risk prediction studies. Nature 591, 211–219 (2021). (PMID: 10.1038/s41586-021-03243-6336925548609771)
Khan, A. et al. Genome-wide polygenic score to predict chronic kidney disease across ancestries. Nat. Med. 28, 1412–1420 (2022). (PMID: 10.1038/s41591-022-01869-1357109959329233)
Khera, A. V. et al. Genome-wide polygenic scores for common diseases identify individuals with risk equivalent to monogenic mutations. Nat. Genet. 50, 1219–1224 (2018). (PMID: 10.1038/s41588-018-0183-z301047626128408)
Ge, T. et al. Development and validation of a trans-ancestry polygenic risk score for type 2 diabetes in diverse populations. Genome Med. 14, 70 (2022). (PMID: 10.1186/s13073-022-01074-2357651009241245)
Martin, A. R. et al. Human demographic history impacts genetic risk prediction across diverse populations. Am. J. Hum. Genet. 100, 635–649 (2017). (PMID: 10.1016/j.ajhg.2017.03.004283664425384097)
Loh, P.-R. et al. Reference-based phasing using the Haplotype Reference Consortium panel. Nat. Genet. 48, 1443–1448 (2016). (PMID: 10.1038/ng.3679276949585096458)
Howie, B., Fuchsberger, C., Stephens, M., Marchini, J. & Abecasis, G. R. Fast and accurate genotype imputation in genome-wide association studies through pre-phasing. Nat. Genet. 44, 955–959 (2012). (PMID: 10.1038/ng.2354228205123696580)
Lee, A. et al. BOADICEA: a comprehensive breast cancer risk prediction model incorporating genetic and nongenetic risk factors. Genet. Med. 21, 1708–1718 (2019). (PMID: 10.1038/s41436-018-0406-9306432176687499)
Holm, S. A simple sequentially rejective multiple test procedure. Scand. J. Stat. 6, 65–70 (1979).
Ding, Y. et al. Large uncertainty in individual polygenic risk score estimation impacts PRS-based risk stratification. Nat. Genet. 54, 30–39 (2022). (PMID: 10.1038/s41588-021-00961-534931067)
Schoeler, T. et al. Participation bias in the UK Biobank distorts genetic associations and downstream analyses. Nat. Hum. Behav. 7, 1216–1227 (2023). (PMID: 10.1038/s41562-023-01579-93710608110365993)
Privé, F. et al. Portability of 245 polygenic scores when derived from the UK Biobank and applied to 9 ancestry groups from the same cohort. Am. J. Hum. Genet. 109, 12–23 (2022).
Ding, Y. et al. Polygenic scoring accuracy varies across the genetic ancestry continuum. Nature 618, 774–781 (2023). (PMID: 10.1038/s41586-023-06079-43719849110284707)
Scutari, M., Mackay, I. & Balding, D. Using genetic distance to infer the accuracy of genomic prediction. PLoS Genet. 12, e1006288 (2016). (PMID: 10.1371/journal.pgen.1006288275892685010218)
Alexander, D. H., Novembre, J. & Lange, K. Fast model-based estimation of ancestry in unrelated individuals. Genome Res. 19, 1655–1664 (2009). (PMID: 10.1101/gr.094052.109196482172752134)

OT2 OD026551 United States OD NIH HHS; U24 OD023121 United States OD NIH HHS; OT2 OD026552 United States OD NIH HHS; OT2 OD026549 United States OD NIH HHS; OT2 OD025337 United States OD NIH HHS; U01 HG011175 United States HG NHGRI NIH HHS; U01 HG011169 United States HG NHGRI NIH HHS; OT2 OD025277 United States OD NIH HHS; OT2 OD026550 United States OD NIH HHS; OT2 OD025276 United States OD NIH HHS; U01 HG011181 United States HG NHGRI NIH HHS; U01 HG011167 United States HG NHGRI NIH HHS; OT2 OD026556 United States OD NIH HHS; U24 OD023176 United States OD NIH HHS; U01 HG011172 United States HG NHGRI NIH HHS; OT2 OD026548 United States OD NIH HHS; P50 HD105351 United States HD NICHD NIH HHS; U01 HG008657 United States HG NHGRI NIH HHS; OT2 OD035404 United States OD NIH HHS; OT2 OD025315 United States OD NIH HHS; OT2 OD030043 United States OD NIH HHS; OT2 OD026555 United States OD NIH HHS; U01 HG008680 United States HG NHGRI NIH HHS; U01 HG011176 United States HG NHGRI NIH HHS; OT2 OD026557 United States OD NIH HHS; U01 HG008685 United States HG NHGRI NIH HHS; U01 HG006379 United States HG NHGRI NIH HHS; OT2 OD026554 United States OD NIH HHS; U01 HG011166 United States HG NHGRI NIH HHS

Investigator: S Berndt; J Hirschhorn; R Loos

Date Created: 20240220 Date Completed: 20240222 Latest Revision: 20240830

20240831

PMC10878968

10.1038/s41591-024-02796-z

38374346