Selection, optimization and validation of ten chronic disease polygenic risk scores for clinical implementation in diverse US populations
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Selection, optimization and validation of ten chronic disease polygenic risk scores for clinical implementation in diverse US populations. / Lennon, Niall J.; Kottyan, Leah C.; Kachulis, Christopher; Abul-Husn, Noura S.; Arias, Josh; Belbin, Gillian; Below, Jennifer E.; Berndt, Sonja I.; Chung, Wendy K.; Cimino, James J.; Clayton, Ellen Wright; Connolly, John J.; Crosslin, David R.; Dikilitas, Ozan; Velez Edwards, Digna R.; Feng, Qi Ping; Fisher, Marissa; Freimuth, Robert R.; Ge, Tian; Glessner, Joseph T.; Gordon, Adam S.; Patterson, Candace; Hakonarson, Hakon; Harden, Maegan; Harr, Margaret; Hirschhorn, Joel; Hoggart, Clive; Hsu, Li; Irvin, Marguerite R.; Jarvik, Gail P.; Karlson, Elizabeth W.; Khan, Atlas; Khera, Amit; Kiryluk, Krzysztof; Kullo, Iftikhar; Larkin, Katie; Limdi, Nita; Linder, Jodell E.; Loos, Ruth; Luo, Yuan; Malolepsza, Edyta; Manolio, Teri A.; Martin, Lisa J.; McCarthy, Li; McNally, Elizabeth M.; Meigs, James B.; Mersha, Tesfaye B.; Mosley, Jonathan D.; Musick, Anjene; Namjou, Bahram; Pai, Nihal; Pesce, Lorenzo L.; Peters, Ulrike; Peterson, Josh F.; Prows, Cynthia A.; Puckelwartz, Megan J.; Rehm, Heidi L.; Roden, Dan M.; Rosenthal, Elisabeth A.; Rowley, Robb; Sawicki, Konrad Teodor; Schaid, Daniel J.; Smit, Roelof A.J.; Smith, Johanna L.; Smoller, Jordan W.; Thomas, Minta; Tiwari, Hemant; Toledo, Diana M.; Vaitinadin, Nataraja Sarma; Veenstra, David; Walunas, Theresa L.; Wang, Zhe; Wei, Wei Qi; Weng, Chunhua; Wiesner, Georgia L.; Yin, Xianyong; Kenny, Eimear E.; Berndt, Sonja; Hirschhorn, Joel.
In: Nature Medicine, Vol. 30, No. 2, 2024, p. 480-487.Research output: Contribution to journal › Journal article › Research › peer-review
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TY - JOUR
T1 - Selection, optimization and validation of ten chronic disease polygenic risk scores for clinical implementation in diverse US populations
AU - Lennon, Niall J.
AU - Kottyan, Leah C.
AU - Kachulis, Christopher
AU - Abul-Husn, Noura S.
AU - Arias, Josh
AU - Belbin, Gillian
AU - Below, Jennifer E.
AU - Berndt, Sonja I.
AU - Chung, Wendy K.
AU - Cimino, James J.
AU - Clayton, Ellen Wright
AU - Connolly, John J.
AU - Crosslin, David R.
AU - Dikilitas, Ozan
AU - Velez Edwards, Digna R.
AU - Feng, Qi Ping
AU - Fisher, Marissa
AU - Freimuth, Robert R.
AU - Ge, Tian
AU - Glessner, Joseph T.
AU - Gordon, Adam S.
AU - Patterson, Candace
AU - Hakonarson, Hakon
AU - Harden, Maegan
AU - Harr, Margaret
AU - Hirschhorn, Joel
AU - Hoggart, Clive
AU - Hsu, Li
AU - Irvin, Marguerite R.
AU - Jarvik, Gail P.
AU - Karlson, Elizabeth W.
AU - Khan, Atlas
AU - Khera, Amit
AU - Kiryluk, Krzysztof
AU - Kullo, Iftikhar
AU - Larkin, Katie
AU - Limdi, Nita
AU - Linder, Jodell E.
AU - Loos, Ruth
AU - Luo, Yuan
AU - Malolepsza, Edyta
AU - Manolio, Teri A.
AU - Martin, Lisa J.
AU - McCarthy, Li
AU - McNally, Elizabeth M.
AU - Meigs, James B.
AU - Mersha, Tesfaye B.
AU - Mosley, Jonathan D.
AU - Musick, Anjene
AU - Namjou, Bahram
AU - Pai, Nihal
AU - Pesce, Lorenzo L.
AU - Peters, Ulrike
AU - Peterson, Josh F.
AU - Prows, Cynthia A.
AU - Puckelwartz, Megan J.
AU - Rehm, Heidi L.
AU - Roden, Dan M.
AU - Rosenthal, Elisabeth A.
AU - Rowley, Robb
AU - Sawicki, Konrad Teodor
AU - Schaid, Daniel J.
AU - Smit, Roelof A.J.
AU - Smith, Johanna L.
AU - Smoller, Jordan W.
AU - Thomas, Minta
AU - Tiwari, Hemant
AU - Toledo, Diana M.
AU - Vaitinadin, Nataraja Sarma
AU - Veenstra, David
AU - Walunas, Theresa L.
AU - Wang, Zhe
AU - Wei, Wei Qi
AU - Weng, Chunhua
AU - Wiesner, Georgia L.
AU - Yin, Xianyong
AU - Kenny, Eimear E.
AU - Berndt, Sonja
AU - Hirschhorn, Joel
N1 - Publisher Copyright: © The Author(s) 2024.
PY - 2024
Y1 - 2024
N2 - 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.
AB - 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.
U2 - 10.1038/s41591-024-02796-z
DO - 10.1038/s41591-024-02796-z
M3 - Journal article
C2 - 38374346
AN - SCOPUS:85185323827
VL - 30
SP - 480
EP - 487
JO - Nature Medicine
JF - Nature Medicine
SN - 1078-8956
IS - 2
ER -
ID: 385588190