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

被引:0
作者
Niall J. Lennon
Leah C. Kottyan
Christopher Kachulis
Noura S. Abul-Husn
Josh Arias
Gillian Belbin
Jennifer E. Below
Sonja I. Berndt
Wendy K. Chung
James J. Cimino
Ellen Wright Clayton
John J. Connolly
David R. Crosslin
Ozan Dikilitas
Digna R. Velez Edwards
QiPing Feng
Marissa Fisher
Robert R. Freimuth
Tian Ge
Joseph T. Glessner
Adam S. Gordon
Candace Patterson
Hakon Hakonarson
Maegan Harden
Margaret Harr
Joel N. Hirschhorn
Clive Hoggart
Li Hsu
Marguerite R. Irvin
Gail P. Jarvik
Elizabeth W. Karlson
Atlas Khan
Amit Khera
Krzysztof Kiryluk
Iftikhar Kullo
Katie Larkin
Nita Limdi
Jodell E. Linder
Ruth J. F. Loos
Yuan Luo
Edyta Malolepsza
Teri A. Manolio
Lisa J. Martin
Li McCarthy
Elizabeth M. McNally
James B. Meigs
Tesfaye B. Mersha
Jonathan D. Mosley
Anjene Musick
Bahram Namjou
机构
[1] Broad Institute of MIT and Harvard,Cincinnati Children’s Hospital Medical Center
[2] University of Cincinnati,Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences
[3] Icahn School of Medicine at Mount Sinai,undefined
[4] National Human Genome Research Institute,undefined
[5] National Institutes of Health,undefined
[6] Vanderbilt University Medical Center,undefined
[7] Columbia University,undefined
[8] University of Alabama at Birmingham,undefined
[9] Children’s Hospital of Philadelphia,undefined
[10] Tulane University,undefined
[11] University of Washington,undefined
[12] Mayo Clinic,undefined
[13] Mass General Brigham,undefined
[14] Northwestern University,undefined
[15] Boston Children’s Hospital,undefined
[16] Fred Hutchinson Cancer Center,undefined
[17] University of Copenhagen,undefined
[18] The Charles Bronfman Institute for Personalized Medicine,undefined
[19] Icahn School of Medicine at Mount Sinai,undefined
[20] National Institutes of Health,undefined
[21] Nanjing Medical University,undefined
关键词
D O I
暂无
中图分类号
学科分类号
摘要
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.
引用
收藏
页码:480 / 487
页数:7
相关论文
共 57 条
[21]  
Mars N(2023)Returning integrated genomic risk and clinical recommendations: the eMERGE study Genet. Med. 25 211-1448
[22]  
Ruan Y(2011)The eMERGE Network: a consortium of biorepositories linked to electronic medical records data for conducting genomic studies BMC Med. Genomics 4 1412-959
[23]  
Márquez-Luna C(2013)The Electronic Medical Records and Genomics (eMERGE) Network: past, present, and future Genet. Med. 15 1219-1718
[24]  
Loh P-R(2021)Improving reporting standards for polygenic scores in risk prediction studies Nature 591 635-39
[25]  
Price AL(2022)Genome-wide polygenic score to predict chronic kidney disease across ancestries Nat. Med. 28 1443-1227
[26]  
Hujoel MLA(2018)Genome-wide polygenic scores for common diseases identify individuals with risk equivalent to monogenic mutations Nat. Genet. 50 955-781
[27]  
Loh P-R(2022)Development and validation of a trans-ancestry polygenic risk score for type 2 diabetes in diverse populations Genome Med. 14 1708-1664
[28]  
Neale BM(2017)Human demographic history impacts genetic risk prediction across diverse populations Am. J. Hum. Genet. 100 30-undefined
[29]  
Price AL(2016)Reference-based phasing using the Haplotype Reference Consortium panel Nat. Genet. 48 1216-undefined
[30]  
Elliott J(2012)Fast and accurate genotype imputation in genome-wide association studies through pre-phasing Nat. Genet. 44 774-undefined