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Delabays B, Trajanoska K, Walonoski J, Mooser V. Cardiovascular Pharmacogenetics: From Discovery of Genetic Association to Clinical Adoption of Derived Test. Pharmacol Rev 2024; 76:791-827. [PMID: 39122647 DOI: 10.1124/pharmrev.123.000750] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2023] [Revised: 04/24/2024] [Accepted: 05/28/2024] [Indexed: 08/12/2024] Open
Abstract
Recent breakthroughs in human genetics and in information technologies have markedly expanded our understanding at the molecular level of the response to drugs, i.e., pharmacogenetics (PGx), across therapy areas. This review is restricted to PGx for cardiovascular (CV) drugs. First, we examined the PGx information in the labels approved by regulatory agencies in Europe, Japan, and North America and related recommendations from expert panels. Out of 221 marketed CV drugs, 36 had PGx information in their labels approved by one or more agencies. The level of annotations and recommendations varied markedly between agencies and expert panels. Clopidogrel is the only CV drug with consistent PGx recommendation (i.e., "actionable"). This situation prompted us to dissect the steps from discovery of a PGx association to clinical translation. We found 101 genome-wide association studies that investigated the response to CV drugs or drug classes. These studies reported significant associations for 48 PGx traits mapping to 306 genes. Six of these 306 genes are mentioned in the corresponding PGx labels or recommendations for CV drugs. Genomic analyses also highlighted the wide between-population differences in risk allele frequencies and the individual load of actionable PGx variants. Given the high attrition rate and the long road to clinical translation, additional work is warranted to identify and validate PGx variants for more CV drugs across diverse populations and to demonstrate the utility of PGx testing. To that end, pre-emptive PGx combining genomic profiling with electronic medical records opens unprecedented opportunities to improve healthcare, for CV diseases and beyond. SIGNIFICANCE STATEMENT: Despite spectacular breakthroughs in human molecular genetics and information technologies, consistent evidence supporting PGx testing in the cardiovascular area is limited to a few drugs. Additional work is warranted to discover and validate new PGx markers and demonstrate their utility. Pre-emptive PGx combining genomic profiling with electronic medical records opens unprecedented opportunities to improve healthcare, for CV diseases and beyond.
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Affiliation(s)
- Benoît Delabays
- Canada Excellence Research Chair in Genomic Medicine, Victor Phillip Dahdaleh Institute of Genomic Medicine, Department of Human Genetics, Faculty of Medicine and Health Sciences, McGill University, Montreal, QC, Canada (B.D., K.T., V.M.); and Medeloop Inc., Palo Alto, California, and Montreal, QC, Canada (J.W.)
| | - Katerina Trajanoska
- Canada Excellence Research Chair in Genomic Medicine, Victor Phillip Dahdaleh Institute of Genomic Medicine, Department of Human Genetics, Faculty of Medicine and Health Sciences, McGill University, Montreal, QC, Canada (B.D., K.T., V.M.); and Medeloop Inc., Palo Alto, California, and Montreal, QC, Canada (J.W.)
| | - Joshua Walonoski
- Canada Excellence Research Chair in Genomic Medicine, Victor Phillip Dahdaleh Institute of Genomic Medicine, Department of Human Genetics, Faculty of Medicine and Health Sciences, McGill University, Montreal, QC, Canada (B.D., K.T., V.M.); and Medeloop Inc., Palo Alto, California, and Montreal, QC, Canada (J.W.)
| | - Vincent Mooser
- Canada Excellence Research Chair in Genomic Medicine, Victor Phillip Dahdaleh Institute of Genomic Medicine, Department of Human Genetics, Faculty of Medicine and Health Sciences, McGill University, Montreal, QC, Canada (B.D., K.T., V.M.); and Medeloop Inc., Palo Alto, California, and Montreal, QC, Canada (J.W.)
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2
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Kullo IJ. Promoting equity in polygenic risk assessment through global collaboration. Nat Genet 2024:10.1038/s41588-024-01843-2. [PMID: 39103647 DOI: 10.1038/s41588-024-01843-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2023] [Accepted: 06/24/2024] [Indexed: 08/07/2024]
Abstract
The long delay before genomic technologies become available in low- and middle-income countries is a concern from both scientific and ethical standpoints. Polygenic risk scores (PRSs), a relatively recent advance in genomics, could have a substantial impact on promoting health by improving disease risk prediction and guiding preventive strategies. However, clinical use of PRSs in their current forms might widen global health disparities, as their portability to diverse groups is limited. This Perspective highlights the need for global collaboration to develop and implement PRSs that perform equitably across the world. Such collaboration requires capacity building and the generation of new data in low-resource settings, the sharing of harmonized genotype and phenotype data securely across borders, novel population genetics and statistical methods to improve PRS performance, and thoughtful clinical implementation in diverse settings. All this needs to occur while considering the ethical, legal and social implications, with support from regulatory and funding agencies and policymakers.
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Affiliation(s)
- Iftikhar J Kullo
- Department of Cardiovascular Medicine and the Gonda Vascular Center, Mayo Clinic, Rochester, MN, USA.
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3
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Singh S, Stocco G, Theken KN, Dickson A, Feng Q, Karnes JH, Mosley JD, El Rouby N. Pharmacogenomics polygenic risk score: Ready or not for prime time? Clin Transl Sci 2024; 17:e13893. [PMID: 39078255 PMCID: PMC11287822 DOI: 10.1111/cts.13893] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2024] [Revised: 06/11/2024] [Accepted: 06/25/2024] [Indexed: 07/31/2024] Open
Abstract
Pharmacogenomic Polygenic Risk Scores (PRS) have emerged as a tool to address the polygenic nature of pharmacogenetic phenotypes, increasing the potential to predict drug response. Most pharmacogenomic PRS have been extrapolated from disease-associated variants identified by genome wide association studies (GWAS), although some have begun to utilize genetic variants from pharmacogenomic GWAS. As pharmacogenomic PRS hold the promise of enabling precision medicine, including stratified treatment approaches, it is important to assess the opportunities and challenges presented by the current data. This assessment will help determine how pharmacogenomic PRS can be advanced and transitioned into clinical use. In this review, we present a summary of recent evidence, evaluate the current status, and identify several challenges that have impeded the progress of pharmacogenomic PRS. These challenges include the reliance on extrapolations from disease genetics and limitations inherent to pharmacogenomics research such as low sample sizes, phenotyping inconsistencies, among others. We finally propose recommendations to overcome the challenges and facilitate the clinical implementation. These recommendations include standardizing methodologies for phenotyping, enhancing collaborative efforts, developing new statistical methods to capitalize on drug-specific genetic associations for PRS construction. Additional recommendations include enhancing the infrastructure that can integrate genomic data with clinical predictors, along with implementing user-friendly clinical decision tools, and patient education. Ethical and regulatory considerations should address issues related to patient privacy, informed consent and safe use of PRS. Despite these challenges, ongoing research and large-scale collaboration is likely to advance the field and realize the potential of pharmacogenomic PRS.
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Affiliation(s)
- Sonal Singh
- Merck & Co., IncSouth San FranciscoCaliforniaUSA
| | - Gabriele Stocco
- Department of Medical, Surgical and Health SciencesUniversity of TriesteTriesteItaly
- Institute for Maternal and Child Health IRCCS Burlo GarofoloTriesteItaly
| | - Katherine N. Theken
- Department of Oral and Maxillofacial Surgery and Pharmacology, School of Dental MedicineUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Alyson Dickson
- Department of MedicineVanderbilt University Medical CenterNashvilleTennesseeUSA
| | - QiPing Feng
- Department of MedicineVanderbilt University Medical CenterNashvilleTennesseeUSA
| | - Jason H. Karnes
- Department of Pharmacy Practice and Science, R. Ken Coit College of PharmacyUniversity of ArizonaTucsonArizonaUSA
- Department of Biomedical InformaticsVanderbilt University Medical CenterNashvilleTennesseeUSA
| | - Jonathan D. Mosley
- Department of MedicineVanderbilt University Medical CenterNashvilleTennesseeUSA
- Department of Biomedical InformaticsVanderbilt University Medical CenterNashvilleTennesseeUSA
| | - Nihal El Rouby
- Division of Pharmacy Practice and Adminstrative Sciences, James L Winkle College of PharmacyUniversity of CincinnatiCincinnatiOhioUSA
- St. Elizabeth HealthcareEdgewoodKentuckyUSA
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4
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Abramowitz SA, Boulier K, Keat K, Cardone KM, Shivakumar M, DePaolo J, Judy R, Kim D, Rader DJ, Ritchie, Voight BF, Pasaniuc B, Levin MG, Damrauer SM. Population Performance and Individual Agreement of Coronary Artery Disease Polygenic Risk Scores. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.07.25.24310931. [PMID: 39108513 PMCID: PMC11302700 DOI: 10.1101/2024.07.25.24310931] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 08/12/2024]
Abstract
Importance Polygenic risk scores (PRSs) for coronary artery disease (CAD) are a growing clinical and commercial reality. Whether existing scores provide similar individual-level assessments of disease liability is a critical consideration for clinical implementation that remains uncharacterized. Objective Characterize the reliability of CAD PRSs that perform equivalently at the population level at predicting individual-level risk. Design Cross-sectional Study. Setting All of Us Research Program (AOU), Penn Medicine Biobank (PMBB), and UCLA ATLAS Precision Health Biobank. Participants Volunteers of diverse genetic backgrounds enrolled in AOU, PMBB, and UCLA with available electronic health record and genotyping data. Exposures Polygenic risk for CAD from previously published PRSs and new PRSs developed separately from the testing cohorts. Main Outcomes and Measures Sets of CAD PRSs that perform population prediction equivalently were identified by comparing calibration and discrimination (Brier score and AUROC) of generalized linear models of prevalent CAD using Bayesian analysis of variance. Among equivalently performing scores, individual-level agreement between risk estimates was tested with intraclass correlation (ICC) and Light's Kappa, measures of inter-rater reliability. Results 50 PRSs were calculated for 171,095 AOU participants. When included in a model of prevalent CAD, 48 scores had practically equivalent Brier scores and AUROCs (region of practical equivalence = 0.02). Across these scores, 84% of participants had at least one score in both the top and bottom risk quintile. Continuous agreement of individual risk predictions from the 48 scores was poor, with an ICC of 0.351 (95% CI; 0.349, 0.352). Agreement between two statistically equivalent scores was moderate, with an ICC of 0.649 (95% CI; 0.646, 0.652). Light's Kappa, used to evaluate consistency of assignment to high-risk thresholds, did not exceed 0.56 (interpreted as 'fair') across statistically and practically equivalent scores. Repeating the analysis among 41,193 PMBB and 50,748 UCLA participants yielded different sets of statistically and practically equivalent scores which also lacked strong individual agreement. Conclusions and Relevance Across three diverse biobanks, CAD PRSs that performed equivalently at the population level produced unreliable individual risk estimates. Approaches to clinical implementation of CAD PRSs must consider the potential for discordant individual risk estimates from otherwise indistinguishable scores.
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Affiliation(s)
- Sarah A. Abramowitz
- Department of Surgery, University of Pennsylvania Perelman School of Medicine
- Donald and Barbara Zucker School of Medicine at Hofstra/Northwell
| | - Kristin Boulier
- Department of Computational Medicine, University of California, Los Angeles
| | - Karl Keat
- Department of Genetics, University of Pennsylvania Perelman School of Medicine
| | - Katie M. Cardone
- Department of Genetics, University of Pennsylvania Perelman School of Medicine
| | - Manu Shivakumar
- Department of Genetics, University of Pennsylvania Perelman School of Medicine
| | - John DePaolo
- Department of Surgery, University of Pennsylvania Perelman School of Medicine
| | - Renae Judy
- Department of Surgery, University of Pennsylvania Perelman School of Medicine
| | - Dokyoon Kim
- Institute of Biomedical Informatics, University of Pennsylvania
| | - Daniel J. Rader
- Department of Genetics, University of Pennsylvania Perelman School of Medicine
| | - Ritchie
- Department of Genetics, University of Pennsylvania Perelman School of Medicine
| | - Benjamin F. Voight
- Department of Genetics, University of Pennsylvania Perelman School of Medicine
- Department of Systems Pharmacology and Translational Therapeutics, University of Pennsylvania Perelman School of Medicine
- Institute of Translational Medicine and Therapeutics, University of Pennsylvania Perelman School of Medicine
| | - Bogdan Pasaniuc
- Department of Computational Medicine, University of California, Los Angeles
| | - Michael G. Levin
- Cardiovascular Institute, University of Pennsylvania Perelman School of Medicine
- Corporal Michael J. Crescenz VA Medical Center
- Division of Cardiovascular Medicine, University of Pennsylvania Perelman School of Medicine
| | - Scott M. Damrauer
- Department of Surgery, University of Pennsylvania Perelman School of Medicine
- Department of Genetics, University of Pennsylvania Perelman School of Medicine
- Cardiovascular Institute, University of Pennsylvania Perelman School of Medicine
- Corporal Michael J. Crescenz VA Medical Center
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5
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Mabey B, Hughes E, Kucera M, Simmons T, Hullinger B, Pederson HJ, Yehia L, Eng C, Garber J, Gary M, Gordon O, Klemp JR, Mukherjee S, Vijai J, Offit K, Olopade OI, Pruthi S, Kurian A, Robson ME, Whitworth PW, Pal T, Ratzel S, Wagner S, Lanchbury JS, Taber KJ, Slavin TP, Gutin A. Validation of a clinical breast cancer risk assessment tool combining a polygenic score for all ancestries with traditional risk factors. Genet Med 2024; 26:101128. [PMID: 38829299 DOI: 10.1016/j.gim.2024.101128] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2023] [Revised: 03/25/2024] [Accepted: 03/26/2024] [Indexed: 06/05/2024] Open
Abstract
PURPOSE We previously described a combined risk score (CRS) that integrates a multiple-ancestry polygenic risk score (MA-PRS) with the Tyrer-Cuzick (TC) model to assess breast cancer (BC) risk. Here, we present a longitudinal validation of CRS in a real-world cohort. METHODS This study included 130,058 patients referred for hereditary cancer genetic testing and negative for germline pathogenic variants in BC-associated genes. Data were obtained by linking genetic test results to medical claims (median follow-up 12.1 months). CRS calibration was evaluated by the ratio of observed to expected BCs. RESULTS Three hundred forty BCs were observed over 148,349 patient-years. CRS was well-calibrated and demonstrated superior calibration compared with TC in high-risk deciles. MA-PRS alone had greater discriminatory accuracy than TC, and CRS had approximately 2-fold greater discriminatory accuracy than MA-PRS or TC. Among those classified as high risk by TC, 32.6% were low risk by CRS, and of those classified as low risk by TC, 4.3% were high risk by CRS. In cases where CRS and TC classifications disagreed, CRS was more accurate in predicting incident BC. CONCLUSION CRS was well-calibrated and significantly improved BC risk stratification. Short-term follow-up suggests that clinical implementation of CRS should improve outcomes for patients of all ancestries through personalized risk-based screening and prevention.
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Affiliation(s)
| | | | | | | | | | | | | | | | | | | | | | | | | | - Joseph Vijai
- Memorial Sloan Kettering Cancer Center, New York, NY
| | - Kenneth Offit
- Memorial Sloan Kettering Cancer Center, New York, NY
| | | | | | | | - Mark E Robson
- Memorial Sloan Kettering Cancer Center, New York, NY
| | | | - Tuya Pal
- Vanderbilt University Medical Center, Nashville, TN
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Chen T, Pham G, Fox L, Adler N, Wang X, Zhang J, Byun J, Han Y, Saunders GRB, Liu D, Bray MJ, Ramsey AT, McKay J, Bierut L, Amos CI, Hung RJ, Lin X, Zhang H, Chen LS. Genomic Insights for Personalized Care: Motivating At-Risk Individuals Toward Evidence-Based Health Practices. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.03.19.24304556. [PMID: 38562690 PMCID: PMC10984046 DOI: 10.1101/2024.03.19.24304556] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/04/2024]
Abstract
Background Lung cancer and tobacco use pose significant global health challenges, necessitating a comprehensive translational roadmap for improved prevention strategies. Polygenic risk scores (PRSs) are powerful tools for patient risk stratification but have not yet been widely used in primary care for lung cancer, particularly in diverse patient populations. Methods We propose the GREAT care paradigm, which employs PRSs to stratify disease risk and personalize interventions. We developed PRSs using large-scale multi-ancestry genome-wide association studies and standardized PRS distributions across all ancestries. We applied our PRSs to 796 individuals from the GISC Trial, 350,154 from UK Biobank (UKBB), and 210,826 from All of Us Research Program (AoU), totaling 561,776 individuals of diverse ancestry. Results Significant odds ratios (ORs) for lung cancer and difficulty quitting smoking were observed in both UKBB and AoU. For lung cancer, the ORs for individuals in the highest risk group (top 20% versus bottom 20%) were 1.85 (95% CI: 1.58 - 2.18) in UKBB and 2.39 (95% CI: 1.93 - 2.97) in AoU. For difficulty quitting smoking, the ORs (top 33% versus bottom 33%) were 1.36 (95% CI: 1.32 - 1.41) in UKBB and 1.32 (95% CI: 1.28 - 1.36) in AoU. Conclusion Our PRS-based intervention model leverages large-scale genetic data for robust risk assessment across populations. This model will be evaluated in two cluster-randomized clinical trials aimed at motivating health behavior changes in high-risk patients of diverse ancestry. This pioneering approach integrates genomic insights into primary care, promising improved outcomes in cancer prevention and tobacco treatment.
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7
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Lewis ACF, Chisholm RL, Connolly JJ, Esplin ED, Glessner J, Gordon A, Green RC, Hakonarson H, Harr M, Holm IA, Jarvik GP, Karlson E, Kenny EE, Kottyan L, Lennon N, Linder JE, Luo Y, Martin LJ, Perez E, Puckelwartz MJ, Rasmussen-Torvik LJ, Sabatello M, Sharp RR, Smoller JW, Sterling R, Terek S, Wei WQ, Fullerton SM. Managing differential performance of polygenic risk scores across groups: Real-world experience of the eMERGE Network. Am J Hum Genet 2024; 111:999-1005. [PMID: 38688278 PMCID: PMC11179244 DOI: 10.1016/j.ajhg.2024.04.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2024] [Revised: 04/10/2024] [Accepted: 04/11/2024] [Indexed: 05/02/2024] Open
Abstract
The differential performance of polygenic risk scores (PRSs) by group is one of the major ethical barriers to their clinical use. It is also one of the main practical challenges for any implementation effort. The social repercussions of how people are grouped in PRS research must be considered in communications with research participants, including return of results. Here, we outline the decisions faced and choices made by a large multi-site clinical implementation study returning PRSs to diverse participants in handling this issue of differential performance. Our approach to managing the complexities associated with the differential performance of PRSs serves as a case study that can help future implementers of PRSs to plot an anticipatory course in response to this issue.
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Affiliation(s)
- Anna C F Lewis
- Edmond and Lily Safra Center for Ethics, Harvard University, Cambridge, MA, USA; Department of Genetics, Brigham and Women's Hospital, Boston, MA, USA; Broad Institute of MIT and Harvard, Cambridge, MA, USA; Harvard Medical School, Boston, MA, USA.
| | - Rex L Chisholm
- Center for Genetic Medicine, Northwestern University, Evanston, IL, USA
| | - John J Connolly
- Center for Applied Genomics, Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | | | - Joe Glessner
- Center for Applied Genomics, Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Adam Gordon
- Center for Genetic Medicine, Northwestern University, Evanston, IL, USA; Department of Pharmacology, Northwestern University, Evanston, IL, USA
| | - Robert C Green
- Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA; Broad Institute of MIT and Harvard, Cambridge, MA, USA; Ariadne Labs, Boston, MA, USA; Harvard Medical School, Boston, MA, USA
| | - Hakon Hakonarson
- Center for Applied Genomics, Children's Hospital of Philadelphia, Philadelphia, PA, USA; Department of Pediatrics, The Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA; Division of Human Genetics, Children's Hospital of Philadelphia, Philadelphia, PA, USA; Division of Pulmonary Medicine, Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Margaret Harr
- Center for Applied Genomics, Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Ingrid A Holm
- Division of Genetics and Genomics, Boston Children's Hospital, Boston, MA, USA; Department of Pediatrics, Harvard Medical School, Boston, MA, USA
| | - Gail P Jarvik
- Division of Medical Genetics, Department of Medicine and Department of Genome Science, University of Washington Medical Center, Seattle, WA, USA
| | - Elizabeth Karlson
- Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA; Mass General Brigham Personalized Medicine, Boston, MA, USA
| | - Eimear E Kenny
- Institute for Genomic Health, Icahn School of Medicine, New York City, NY, USA; Center for Clinical Translational Genomics, Icahn School of Medicine, New York City, NY, USA; Division of Genomic Medicine, Department of Medicine, Icahn School of Medicine, New York City, NY, USA; Department of Genetics and Genomic Sciences, Icahn School of Medicine, New York City, NY, USA
| | - Leah Kottyan
- Center for Autoimmune Genomics and Etiology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA
| | - Niall Lennon
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Jodell E Linder
- Vanderbilt Institute for Clinical and Translational Research, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Yuan Luo
- Department of Preventive Medicine, Northwestern University, Evanston, IL, USA
| | - Lisa J Martin
- Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA; University of Cincinnati College of Medicine, Cincinnati, OH, USA
| | - Emma Perez
- Mass General Brigham Personalized Medicine, Boston, MA, USA
| | - Megan J Puckelwartz
- Center for Genetic Medicine, Northwestern University, Evanston, IL, USA; Department of Pharmacology, Northwestern University, Evanston, IL, USA
| | - Laura J Rasmussen-Torvik
- Center for Genetic Medicine, Northwestern University, Evanston, IL, USA; Department of Preventive Medicine, Northwestern University, Evanston, IL, USA
| | - Maya Sabatello
- Center for Precision Medicine and Genomics, Department of Medicine, Columbia University Irving Medical Center, New York City, NY, USA; Division of Ethics, Department of Medical Humanities and Ethics, Columbia University Irving Medical Center, New York City, NY, USA
| | | | - Jordan W Smoller
- Center for Precision Psychiatry, Department of Psychiatry, Massachusetts General Hospital, Boston, MA, USA; Psychiatric & Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA; Stanley Center for Psychiatric Research, Broad Institute, Cambridge, MA, USA
| | - Rene Sterling
- Division of Genomics and Society, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
| | - Shannon Terek
- Center for Applied Genomics, Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Wei-Qi Wei
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Stephanie M Fullerton
- Department of Bioethics & Humanities, University of Washington School of Medicine, Seattle, WA, USA
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Dannhauser FC, Taylor LC, Tung JSL, Usher-Smith JA. The acceptability and clinical impact of using polygenic scores for risk-estimation of common cancers in primary care: a systematic review. J Community Genet 2024; 15:217-234. [PMID: 38769249 PMCID: PMC11217210 DOI: 10.1007/s12687-024-00709-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2023] [Accepted: 05/02/2024] [Indexed: 05/22/2024] Open
Abstract
BACKGROUND Polygenic scores (PGS) have been developed for cancer risk-estimation and show potential as tools to prompt earlier referral for high-risk individuals and aid risk-stratification within cancer screening programmes. This review explores the potential for using PGS to identify individuals at risk of the most common cancers seen in primary care. METHODS Two electronic databases were searched up until November 2023 to identify quantitative, qualitative, and mixed methods studies that reported on the acceptability and clinical impact of using PGS to identify individuals at highest risk of breast, prostate, colorectal and lung cancer in primary care. The Mixed Methods Appraisal Tool (MMAT) was used to assess the quality of included studies and a narrative synthesis was used to analyse data. RESULTS A total of 190 papers were identified, 18 of which were eligible for inclusion. A cancer risk-assessment tool incorporating PGS was acceptable to the general practice population and their healthcare providers but major challenges to implementation were identified, including lack of evidence for PGS in non-European ancestry and a need for healthcare provider education in genomic medicine. A PGS cancer risk-assessment had relatively limited impact on psychosocial outcomes and health behaviours. However, for prostate cancer, potential applications for its use in primary care were shown. CONCLUSIONS Cancer risk assessment incorporating PGS in primary care is acceptable to patients and healthcare providers but there is a paucity of research exploring clinical impact. Few studies were identified, and more research is required before clinical implementation of PGS can be recommended.
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Affiliation(s)
| | - Lily C Taylor
- Department of Public Health and Primary Care, University of Cambridge, Cambridge, England
| | - Joanna S L Tung
- Department of Public Health and Primary Care, University of Cambridge, Cambridge, England
| | - Juliet A Usher-Smith
- Department of Public Health and Primary Care, University of Cambridge, Cambridge, England.
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9
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Smith JL, Tcheandjieu C, Dikilitas O, Iyer K, Miyazawa K, Hilliard A, Lynch J, Rotter JI, Chen YDI, Sheu WHH, Chang KM, Kanoni S, Tsao PS, Ito K, Kosel M, Clarke SL, Schaid DJ, Assimes TL, Kullo IJ. Multi-Ancestry Polygenic Risk Score for Coronary Heart Disease Based on an Ancestrally Diverse Genome-Wide Association Study and Population-Specific Optimization. CIRCULATION. GENOMIC AND PRECISION MEDICINE 2024; 17:e004272. [PMID: 38380516 DOI: 10.1161/circgen.123.004272] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/07/2023] [Accepted: 01/23/2024] [Indexed: 02/22/2024]
Abstract
BACKGROUND Predictive performance of polygenic risk scores (PRS) varies across populations. To facilitate equitable clinical use, we developed PRS for coronary heart disease (CHD; PRSCHD) for 5 genetic ancestry groups. METHODS We derived ancestry-specific and multi-ancestry PRSCHD based on pruning and thresholding (PRSPT) and ancestry-based continuous shrinkage priors (PRSCSx) applied to summary statistics from the largest multi-ancestry genome-wide association study meta-analysis for CHD to date, including 1.1 million participants from 5 major genetic ancestry groups. Following training and optimization in the Million Veteran Program, we evaluated the best-performing PRSCHD in 176,988 individuals across 9 diverse cohorts. RESULTS Multi-ancestry PRSPT and PRSCSx outperformed ancestry-specific PRSPT and PRSCSx across a range of tuning values. Two best-performing multi-ancestry PRSCHD (ie, PRSPTmult and PRSCSxmult) and 1 ancestry-specific (PRSCSxEUR) were taken forward for validation. PRSPTmult demonstrated the strongest association with CHD in individuals of South Asian ancestry and European ancestry (odds ratio per 1 SD [95% CI, 2.75 [2.41-3.14], 1.65 [1.59-1.72]), followed by East Asian ancestry (1.56 [1.50-1.61]), Hispanic/Latino ancestry (1.38 [1.24-1.54]), and African ancestry (1.16 [1.11-1.21]). PRSCSxmult showed the strongest associations in South Asian ancestry (2.67 [2.38-3.00]) and European ancestry (1.65 [1.59-1.71]), lower in East Asian ancestry (1.59 [1.54-1.64]), Hispanic/Latino ancestry (1.51 [1.35-1.69]), and the lowest in African ancestry (1.20 [1.15-1.26]). CONCLUSIONS The use of summary statistics from a large multi-ancestry genome-wide meta-analysis improved the performance of PRSCHD in most ancestry groups compared with single-ancestry methods. Despite the use of one of the largest and most diverse sets of training and validation cohorts to date, improvement of predictive performance was limited in African ancestry. This highlights the need for larger genome-wide association study datasets of underrepresented populations to enhance the performance of PRSCHD.
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Affiliation(s)
- Johanna L Smith
- Department of Cardiovascular Medicine (J.L.S., O.D., I.J.K.), Mayo Clinic, Rochester, MN
| | - Catherine Tcheandjieu
- Department of Epidemiology and Biostatistics, University of California San Francisco (C.T.)
- Gladstone Institute of Data Science and Biotechnology, Gladstone Institute, San Francisco, CA (C.T.)
- VA Palo Alto Health Care System (C.T., A.H., P.S.T., S.L.C.)
| | - Ozan Dikilitas
- Department of Cardiovascular Medicine (J.L.S., O.D., I.J.K.), Mayo Clinic, Rochester, MN
| | - Kruthika Iyer
- Stanford University School of Medicine, Palo Alto, CA (K. Iyer, A.H.)
| | - Kazuo Miyazawa
- Riken Center for Integrative Medical Sciences, Yokohama City, Japan (K.M., K. Ito)
| | - Austin Hilliard
- VA Palo Alto Health Care System (C.T., A.H., P.S.T., S.L.C.)
- Stanford University School of Medicine, Palo Alto, CA (K. Iyer, A.H.)
| | | | - Jerome I Rotter
- Institute for Translational Genomics and Population Sciences, Department of Pediatrics, Lundquist Institute at Harbor-UCLA Medical Center, Torrance, CA (J.I.R., Y.-D.I.C.)
| | - Yii-Der Ida Chen
- Institute for Translational Genomics and Population Sciences, Department of Pediatrics, Lundquist Institute at Harbor-UCLA Medical Center, Torrance, CA (J.I.R., Y.-D.I.C.)
| | - Wayne Huey-Herng Sheu
- Institute of Molecular and Genomic Medicine, National Health Research Institute (W.H.-H.S.)
- Division of Endocrinology and Metabolism, Department of Internal Medicine, Taipei Veterans General Hospital (W.H.-H.S.)
- Division of Endocrinology and Metabolism, Department of Internal Medicine, Taichung Veterans General Hospital, Taiwan (W.H.-H.S.)
| | - Kyong-Mi Chang
- Corporal Michael J Crescenz VA Medical Center, Philadelphia, PA (K.-M.C.)
| | - Stavroula Kanoni
- Queen Mary University of London, Cambridge, United Kingdom (S.K.)
| | - Philip S Tsao
- VA Palo Alto Health Care System (C.T., A.H., P.S.T., S.L.C.)
- Stanford University, Stanford, CA (P.S.T., S.L.C., T.L.A.)
| | - Kaoru Ito
- Riken Center for Integrative Medical Sciences, Yokohama City, Japan (K.M., K. Ito)
| | - Matthew Kosel
- Department of Health Sciences Research, Mayo Clinic, Rochester, MN (M.K., D.J.S.)
| | - Shoa L Clarke
- VA Palo Alto Health Care System (C.T., A.H., P.S.T., S.L.C.)
- Stanford University, Stanford, CA (P.S.T., S.L.C., T.L.A.)
| | - Daniel J Schaid
- Department of Health Sciences Research, Mayo Clinic, Rochester, MN (M.K., D.J.S.)
| | | | - Iftikhar J Kullo
- Department of Cardiovascular Medicine (J.L.S., O.D., I.J.K.), Mayo Clinic, Rochester, MN
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10
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Lambert SA, Wingfield B, Gibson JT, Gil L, Ramachandran S, Yvon F, Saverimuttu S, Tinsley E, Lewis E, Ritchie SC, Wu J, Canovas R, McMahon A, Harris LW, Parkinson H, Inouye M. The Polygenic Score Catalog: new functionality and tools to enable FAIR research. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.05.29.24307783. [PMID: 38853961 PMCID: PMC11160819 DOI: 10.1101/2024.05.29.24307783] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2024]
Abstract
Polygenic scores (PGS) have transformed human genetic research and have multiple potential clinical applications, including risk stratification for disease prevention and prediction of treatment response. Here, we present a series of recent enhancements to the PGS Catalog (www.PGSCatalog.org), the largest findable, accessible, interoperable, and reusable (FAIR) repository of PGS. These include expansions in data content and ancestral diversity as well as the addition of new features. We further present the PGS Catalog Calculator (pgsc_calc, https://github.com/PGScatalog/pgsc_calc), an open-source, scalable and portable pipeline to reproducibly calculate PGS that securely democratizes equitable PGS applications by implementing genetic ancestry estimation and score normalization using reference data. With the PGS Catalog & calculator users can now quantify an individual's genetic predisposition for hundreds of common diseases and clinically relevant traits. Taken together, these updates and tools facilitate the next generation of PGS, thus lowering barriers to the clinical studies necessary to identify where PGS may be integrated into clinical practice.
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Affiliation(s)
- Samuel A. Lambert
- Cambridge Baker Systems Genomics Initiative, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- Victor Phillip Dahdaleh Heart and Lung Research Institute, University of Cambridge, Cambridge, UK
- Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Cambridge, UK
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK
| | - Benjamin Wingfield
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK
| | - Joel T. Gibson
- Cambridge Baker Systems Genomics Initiative, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- Victor Phillip Dahdaleh Heart and Lung Research Institute, University of Cambridge, Cambridge, UK
| | - Laurent Gil
- Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Cambridge, UK
- Wellcome Sanger Institute, Hinxton, UK
| | - Santhi Ramachandran
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK
| | - Florent Yvon
- Cambridge Baker Systems Genomics Initiative, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- Victor Phillip Dahdaleh Heart and Lung Research Institute, University of Cambridge, Cambridge, UK
| | - Shirin Saverimuttu
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK
| | - Emily Tinsley
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK
| | - Elizabeth Lewis
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK
| | - Scott C. Ritchie
- Cambridge Baker Systems Genomics Initiative, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- Victor Phillip Dahdaleh Heart and Lung Research Institute, University of Cambridge, Cambridge, UK
- British Heart Foundation Centre of Research Excellence, University of Cambridge, Cambridge, UK
| | - Jingqin Wu
- Cambridge Baker Systems Genomics Initiative, Baker Heart and Diabetes Institute, Melbourne, VIC, Australia
| | - Rodrigo Canovas
- Cambridge Baker Systems Genomics Initiative, Baker Heart and Diabetes Institute, Melbourne, VIC, Australia
| | - Aoife McMahon
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK
| | - Laura W. Harris
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK
| | - Helen Parkinson
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK
| | - Michael Inouye
- Cambridge Baker Systems Genomics Initiative, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- Victor Phillip Dahdaleh Heart and Lung Research Institute, University of Cambridge, Cambridge, UK
- Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Cambridge, UK
- British Heart Foundation Centre of Research Excellence, University of Cambridge, Cambridge, UK
- Cambridge Baker Systems Genomics Initiative, Baker Heart and Diabetes Institute, Melbourne, VIC, Australia
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11
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Andrews SJ, Jonson C, Fulton-Howard B, Renton AE, Yokoyama JS, Yaffe K. The Role of Genomic-Informed Risk Assessments in Predicting Dementia Outcomes. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.04.27.24306488. [PMID: 38903124 PMCID: PMC11188112 DOI: 10.1101/2024.04.27.24306488] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/22/2024]
Abstract
Importance By integrating genetic and clinical risk factors into genomic-informed dementia risk reports, healthcare providers can offer patients detailed risk profiles to facilitate understanding of individual risk and support the implementation of personalized strategies for promoting brain health. Objective To develop a genomic-informed risk assessment composed of family history, genetic, and clinical risk factors and, in turn, evaluate how the risk assessment predicted incident dementia. Design This longitudinal study included data from two clinical case-control cohorts with an average of 6.6 visits. Secondary analyses were conducted from July 2023 - March 2024. Setting Data were previously collected across multiple US locations from 1994 to 2023. Participants Older adults aged 55+ with whole-genome sequencing and dementia-free at baseline. Exposures An additive score comprising the modified Cardiovascular Risk Factors, Aging, and Incidence of Dementia Risk Score (mCAIDE), family history of dementia, APOE genotype, and an AD polygenic risk score. Main Outcomes and Measures The risk of progression to all-cause dementia was evaluated using Cox-proportional hazard models (hazard ratios with 95% confidence intervals [OR 9%CI]). Results A total of 3,429 older adults were included (aged 75 ± 7 years; 59% female; 75% non-Latino White, 15% Black, 5.2% Latino, 3.6% other, and 0.4% Asian; 27% MCI), with 751 participants progressing to dementia. The most common high-risk indicator was a family history of dementia (56%), followed by APOE*ε4 genotype (36%), high mCAIDE score (34%), and high AD-PRS (11%). Most participants had at least one high-risk indicator, with 39% having one, 32% two, 9.8% three, and 1% four. The presence of 1, 2, 3, or 4 risk indicators was associated with a doubling (HR = 1.72, CI: 1.34-2.22, p = 2.5e-05), tripling (HR = 3.09, CI: 2.41-3.95, p = 4.4e-19), quadrupling (HR = 4.46, CI: 3.34-5.94, p = 2.2e-24), and a twelvefold increase (HR = 12.15, CI: 7.33-20.14, p = 3.2e-22) in dementia risk. Conclusion & Relevance We found that most participants in memory and aging clinics had at least one high-risk indicator for dementia. Furthermore, we observed a dose-response relationship where a greater number of risk indicators was associated with an increased risk of incident dementia.
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Affiliation(s)
- Shea J. Andrews
- Department of Psychiatry and Behavioral Sciences, University of California San Francisco, San Francisco, USA
| | - Caroline Jonson
- Department of Neurology, University of California San Francisco, San Francisco, USA
- Center for Alzheimer’s and Related Dementias, National Institutes of Health, Bethesda, MD USA 20892
- DataTecnica LLC, Washington, DC USA 20037
| | - Brian Fulton-Howard
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, USA
| | - Alan E Renton
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, USA
| | - Jennifer S Yokoyama
- Department of Neurology, University of California San Francisco, San Francisco, USA
| | - Kristine Yaffe
- Department of Psychiatry and Behavioral Sciences, University of California San Francisco, San Francisco, USA
- Department of Neurology, University of California San Francisco, San Francisco, USA
- Department of Epidemiology and Biostatistics, University of California
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12
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Fuat A, Adlen E, Monane M, Coll R, Groves S, Little E, Wild J, Kamali FJ, Soni Y, Haining S, Riding H, Riveros-Mckay F, Peneva I, Lachapelle A, Giner-Delgado C, Weale ME, Plagnol V, Harrison S, Donnelly P. A polygenic risk score added to a QRISK®2 cardiovascular disease risk calculator demonstrated robust clinical acceptance and clinical utility in the primary care setting. Eur J Prev Cardiol 2024; 31:716-722. [PMID: 38243727 DOI: 10.1093/eurjpc/zwae004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/01/2023] [Revised: 12/08/2023] [Accepted: 12/08/2023] [Indexed: 01/21/2024]
Abstract
AIMS The aim of the study was to assess the real-world feasibility, acceptability, and impact of an integrated risk tool for cardiovascular disease (CVD IRT, combining the standard QRISK®2 risk algorithm with a polygenic risk score), implemented within routine primary practice in the UK National Health Service. METHODS AND RESULTS The Healthcare Evaluation of Absolute Risk Testing Study (NCT05294419) evaluated participants undergoing primary care health checks. Both QRISK2 and CVD IRT scores were returned to the healthcare providers (HCPs), who then communicated the results to participants. The primary outcome of the study was feasibility of CVD IRT implementation. Secondary outcomes included changes in CVD risk (QRISK2 vs. CVD IRT) and impact of the CVD IRT on clinical decision-making. A total of 832 eligible participants (median age 55 years, 62% females, 97.5% White ethnicity) were enrolled across 12 UK primary care practices. Cardiovascular disease IRT scores were obtained on 100% of the blood samples. Healthcare providers stated that the CVD IRT could be incorporated into routine primary care in a straightforward manner in 90.7% of reports. Participants stated they were 'likely' or 'very likely' to recommend the use of this test to their family or friends in 86.9% of reports. Participants stated that the test was personally useful (98.8%) and that the results were easy to understand (94.6%). When CVD IRT exceeded QRISK2, HCPs planned changes in management for 108/388 (27.8%) of participants and 47% (62/132) of participants with absolute risk score changes of >2%. CONCLUSION Amongst HCPs and participants who agreed to the trial of genetic data for refinement of clinical risk prediction in primary care, we observed that CVD IRT implementation was feasible and well accepted. The CVD IRT results were associated with planned changes in prevention strategies.
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Affiliation(s)
| | - Ella Adlen
- Genomics plc, King Charles House, Park End Street, Oxford OX1 1JD, UK
| | - Mark Monane
- Genomics plc, King Charles House, Park End Street, Oxford OX1 1JD, UK
| | - Ruth Coll
- Genomics plc, King Charles House, Park End Street, Oxford OX1 1JD, UK
| | - Sarah Groves
- Genomics plc, King Charles House, Park End Street, Oxford OX1 1JD, UK
| | | | | | | | - Yusuf Soni
- Riverside General Practice, Stockton-on-Tees, UK
| | - Shona Haining
- Research and Evidence, NHS North of England Commissioning Support, Durham, UK
| | - Helen Riding
- Research and Evidence, NHS North of England Commissioning Support, Durham, UK
| | | | - Iliana Peneva
- Genomics plc, King Charles House, Park End Street, Oxford OX1 1JD, UK
| | | | | | - Michael E Weale
- Genomics plc, King Charles House, Park End Street, Oxford OX1 1JD, UK
| | - Vincent Plagnol
- Genomics plc, King Charles House, Park End Street, Oxford OX1 1JD, UK
| | - Seamus Harrison
- Genomics plc, King Charles House, Park End Street, Oxford OX1 1JD, UK
| | - Peter Donnelly
- Genomics plc, King Charles House, Park End Street, Oxford OX1 1JD, UK
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13
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Sabatello M, Bakken S, Chung WK, Cohn E, Crew KD, Kiryluk K, Kukafka R, Weng C, Appelbaum PS. Return of polygenic risk scores in research: Stakeholders' views on the eMERGE-IV study. HGG ADVANCES 2024; 5:100281. [PMID: 38414240 PMCID: PMC10950748 DOI: 10.1016/j.xhgg.2024.100281] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2023] [Revised: 02/22/2024] [Accepted: 02/22/2024] [Indexed: 02/29/2024] Open
Abstract
Research on polygenic risk scores (PRSs) for common, genetically complex chronic diseases aims to improve health-related predictions, tailor risk-reducing interventions, and improve health outcomes. Yet, the study and use of PRSs in clinical settings raise equity, clinical, and regulatory challenges that can be greater for individuals from historically marginalized racial, ethnic, and other minoritized communities. As part of the National Human Genome Research Institute-funded Electronic Medical Records and Genomics IV Network, we conducted online focus groups with patients/community members, clinicians, and members of institutional review boards to explore their views on key issues, including PRS research, return of PRS results, clinical translation, and barriers and facilitators to health behavioral changes in response to PRS results. Across stakeholder groups, our findings indicate support for PRS development and a strong interest in having PRS results returned to research participants. However, we also found multi-level barriers and significant differences in stakeholders' views about what is needed and possible for successful implementation. These include researcher-participant interaction formats, health and genomic literacy, and a range of structural barriers, such as financial instability, insurance coverage, and the absence of health-supporting infrastructure and affordable healthy food options in poorer neighborhoods. Our findings highlight the need to revisit and implement measures in PRS studies (e.g., incentives and resources for follow-up care), as well as system-level policies to promote equity in genomic research and health outcomes.
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Affiliation(s)
- Maya Sabatello
- Center for Precision Medicine and Genomics, Department of Medicine, Columbia University, New York, NY, USA; Division of Ethics, Department of Medical Humanities and Ethics, Columbia University, New York, NY, USA.
| | - Suzanne Bakken
- School of Nursing and Department of Biomedical Informatic, Columbia University, New York, NY, USA
| | - Wendy K Chung
- Department of Pediatrics, Boston Children's Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Elizabeth Cohn
- Northwell Health 600 Community Drive, Manhasset, NY, USA
| | - Katherine D Crew
- Department of Medicine and Epidemiology, Columbia University, New York, NY 10032, USA
| | - Krzysztof Kiryluk
- Division of Nephrology, Department of Medicine, Columbia University, New York, NY 10032, USA
| | - Rita Kukafka
- Departments of Biomedical Informatics and Sociomedical Sciences, Columbia University, New York, NY 10032, USA
| | - Chunhua Weng
- Department of Biomedical Informatics, Columbia University, New York, NY 10032, USA
| | - Paul S Appelbaum
- Department of Psychiatry, Columbia University, New York, NY 10032, USA
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14
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Bick AG, Metcalf GA, Mayo KR, Lichtenstein L, Rura S, Carroll RJ, Musick A, Linder JE, Jordan IK, Nagar SD, Sharma S, Meller R, Basford M, Boerwinkle E, Cicek MS, Doheny KF, Eichler EE, Gabriel S, Gibbs RA, Glazer D, Harris PA, Jarvik GP, Philippakis A, Rehm HL, Roden DM, Thibodeau SN, Topper S, Blegen AL, Wirkus SJ, Wagner VA, Meyer JG, Cicek MS, Muzny DM, Venner E, Mawhinney MZ, Griffith SML, Hsu E, Ling H, Adams MK, Walker K, Hu J, Doddapaneni H, Kovar CL, Murugan M, Dugan S, Khan Z, Boerwinkle E, Lennon NJ, Austin-Tse C, Banks E, Gatzen M, Gupta N, Henricks E, Larsson K, McDonough S, Harrison SM, Kachulis C, Lebo MS, Neben CL, Steeves M, Zhou AY, Smith JD, Frazar CD, Davis CP, Patterson KE, Wheeler MM, McGee S, Lockwood CM, Shirts BH, Pritchard CC, Murray ML, Vasta V, Leistritz D, Richardson MA, Buchan JG, Radhakrishnan A, Krumm N, Ehmen BW, Schwartz S, Aster MMT, Cibulskis K, Haessly A, Asch R, Cremer A, Degatano K, Shergill A, Gauthier LD, Lee SK, Hatcher A, Grant GB, Brandt GR, Covarrubias M, Banks E, Able A, Green AE, Carroll RJ, Zhang J, Condon HR, Wang Y, Dillon MK, Albach CH, Baalawi W, Choi SH, Wang X, Rosenthal EA, Ramirez AH, Lim S, Nambiar S, Ozenberger B, Wise AL, Lunt C, Ginsburg GS, Denny JC. Genomic data in the All of Us Research Program. Nature 2024; 627:340-346. [PMID: 38374255 PMCID: PMC10937371 DOI: 10.1038/s41586-023-06957-x] [Citation(s) in RCA: 45] [Impact Index Per Article: 45.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2022] [Accepted: 12/08/2023] [Indexed: 02/21/2024]
Abstract
Comprehensively mapping the genetic basis of human disease across diverse individuals is a long-standing goal for the field of human genetics1-4. The All of Us Research Program is a longitudinal cohort study aiming to enrol a diverse group of at least one million individuals across the USA to accelerate biomedical research and improve human health5,6. Here we describe the programme's genomics data release of 245,388 clinical-grade genome sequences. This resource is unique in its diversity as 77% of participants are from communities that are historically under-represented in biomedical research and 46% are individuals from under-represented racial and ethnic minorities. All of Us identified more than 1 billion genetic variants, including more than 275 million previously unreported genetic variants, more than 3.9 million of which had coding consequences. Leveraging linkage between genomic data and the longitudinal electronic health record, we evaluated 3,724 genetic variants associated with 117 diseases and found high replication rates across both participants of European ancestry and participants of African ancestry. Summary-level data are publicly available, and individual-level data can be accessed by researchers through the All of Us Researcher Workbench using a unique data passport model with a median time from initial researcher registration to data access of 29 hours. We anticipate that this diverse dataset will advance the promise of genomic medicine for all.
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15
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Lappalainen T, Li YI, Ramachandran S, Gusev A. Genetic and molecular architecture of complex traits. Cell 2024; 187:1059-1075. [PMID: 38428388 DOI: 10.1016/j.cell.2024.01.023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2023] [Revised: 12/20/2023] [Accepted: 01/16/2024] [Indexed: 03/03/2024]
Abstract
Human genetics has emerged as one of the most dynamic areas of biology, with a broadening societal impact. In this review, we discuss recent achievements, ongoing efforts, and future challenges in the field. Advances in technology, statistical methods, and the growing scale of research efforts have all provided many insights into the processes that have given rise to the current patterns of genetic variation. Vast maps of genetic associations with human traits and diseases have allowed characterization of their genetic architecture. Finally, studies of molecular and cellular effects of genetic variants have provided insights into biological processes underlying disease. Many outstanding questions remain, but the field is well poised for groundbreaking discoveries as it increases the use of genetic data to understand both the history of our species and its applications to improve human health.
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Affiliation(s)
- Tuuli Lappalainen
- New York Genome Center, New York, NY, USA; Science for Life Laboratory, Department of Gene Technology, KTH Royal Institute of Technology, Stockholm, Sweden.
| | - Yang I Li
- Section of Genetic Medicine, University of Chicago, Chicago, IL, USA; Department of Human Genetics, University of Chicago, Chicago, IL, USA
| | - Sohini Ramachandran
- Ecology, Evolution and Organismal Biology, Center for Computational Molecular Biology, and the Data Science Institute, Brown University, Providence, RI 029129, USA
| | - Alexander Gusev
- Harvard Medical School and Dana-Farber Cancer Institute, Boston, MA, USA
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16
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Plym A, Madueke I, Naik S, Isabelle M, Conti DV, Haiman CA, Penney KL, Mucci LA, Khorasani R, Kibel AS. Combining magnetic resonance imaging with a multi-ancestry polygenic risk score to improve identification of clinically significant prostate cancer. JNCI Cancer Spectr 2024; 8:pkae014. [PMID: 38429995 PMCID: PMC10980589 DOI: 10.1093/jncics/pkae014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2023] [Revised: 02/06/2024] [Accepted: 02/26/2024] [Indexed: 03/03/2024] Open
Abstract
Multi-parametric magnetic resonance imaging (mpMRI) has emerged as an important tool for identifying clinically significant prostate cancer. We examined if the addition of a 400-variant multi-ancestry polygenic risk score (PRS) to mpMRI has the potential to improve identification. Based on data from 24 617 men from the Mass General Brigham Biobank, we identified 1243 men who underwent mpMRI. Men in the top PRS quartile were more likely to have clinically significant prostate cancer (47.1% vs 28.6% in the bottom PRS quartile, adjusted relative proportion 1.72 [95% CI = 1.35 to 2.19]). Both among men with a positive and a negative mpMRI, men in the top PRS quartile had the highest frequency of clinically significant cancer. In a constructed scenario for selecting men to undergo biopsy, use of the PRS lowered the frequency of missed clinically significant cancers from 9.1% to 5.9%. Our study provides initial support for using the PRS to improve identification of potentially lethal prostate cancer.
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Affiliation(s)
- Anna Plym
- Department of Urology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Ikenna Madueke
- Department of Urology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Sachin Naik
- Center for Evidence-Based Imaging, Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Mark Isabelle
- Center for Evidence-Based Imaging, Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - David V Conti
- Center for Genetic Epidemiology, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Christopher A Haiman
- Center for Genetic Epidemiology, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Kathryn L Penney
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Lorelei A Mucci
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Rhamin Khorasani
- Center for Evidence-Based Imaging, Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Adam S Kibel
- Department of Urology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
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17
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Xiang R, Kelemen M, Xu Y, Harris LW, Parkinson H, Inouye M, Lambert SA. Recent advances in polygenic scores: translation, equitability, methods and FAIR tools. Genome Med 2024; 16:33. [PMID: 38373998 PMCID: PMC10875792 DOI: 10.1186/s13073-024-01304-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2023] [Accepted: 02/07/2024] [Indexed: 02/21/2024] Open
Abstract
Polygenic scores (PGS) can be used for risk stratification by quantifying individuals' genetic predisposition to disease, and many potentially clinically useful applications have been proposed. Here, we review the latest potential benefits of PGS in the clinic and challenges to implementation. PGS could augment risk stratification through combined use with traditional risk factors (demographics, disease-specific risk factors, family history, etc.), to support diagnostic pathways, to predict groups with therapeutic benefits, and to increase the efficiency of clinical trials. However, there exist challenges to maximizing the clinical utility of PGS, including FAIR (Findable, Accessible, Interoperable, and Reusable) use and standardized sharing of the genomic data needed to develop and recalculate PGS, the equitable performance of PGS across populations and ancestries, the generation of robust and reproducible PGS calculations, and the responsible communication and interpretation of results. We outline how these challenges may be overcome analytically and with more diverse data as well as highlight sustained community efforts to achieve equitable, impactful, and responsible use of PGS in healthcare.
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Affiliation(s)
- Ruidong Xiang
- Cambridge Baker Systems Genomics Initiative, Baker Heart and Diabetes Institute, Melbourne, VIC, Australia
- Cambridge Baker Systems Genomics Initiative, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Martin Kelemen
- Cambridge Baker Systems Genomics Initiative, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- Victor Phillip Dahdaleh Heart and Lung Research Institute, University of Cambridge, Cambridge, UK
| | - Yu Xu
- Cambridge Baker Systems Genomics Initiative, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- Victor Phillip Dahdaleh Heart and Lung Research Institute, University of Cambridge, Cambridge, UK
- Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Cambridge, UK
| | - Laura W Harris
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK
| | - Helen Parkinson
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK
| | - Michael Inouye
- Cambridge Baker Systems Genomics Initiative, Baker Heart and Diabetes Institute, Melbourne, VIC, Australia.
- Cambridge Baker Systems Genomics Initiative, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK.
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK.
- Victor Phillip Dahdaleh Heart and Lung Research Institute, University of Cambridge, Cambridge, UK.
- Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Cambridge, UK.
- British Heart Foundation Centre of Research Excellence, University of Cambridge, Cambridge, UK.
| | - Samuel A Lambert
- Cambridge Baker Systems Genomics Initiative, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- Victor Phillip Dahdaleh Heart and Lung Research Institute, University of Cambridge, Cambridge, UK
- Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Cambridge, UK
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK
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18
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Bakken S, Cimino JJ, Feldman S, Lorenzi NM. Celebrating Eta Berner and her influence on biomedical and health informatics. J Am Med Inform Assoc 2024; 31:549-551. [PMID: 38366906 PMCID: PMC10873777 DOI: 10.1093/jamia/ocae011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2024] [Accepted: 01/11/2024] [Indexed: 02/18/2024] Open
Affiliation(s)
- Suzanne Bakken
- School of Nursing, Columbia University, New York, NY 10032, United States
- Department of Biomedical Informatics, Columbia University, New York, NY 10032, United States
- Data Science Institute, Columbia University, New York, NY 10027, United States
| | - James J Cimino
- Department of Biomedical Informatics, Columbia University, New York, NY 10032, United States
- Department of Biomedical Informatics and Data Science, Heersink School of Medicine, University of Alabama, Birmingham, AL 35233, United States
| | - Sue Feldman
- Department of Health Services Administration, School of Health Professions, University of Alabama, Birmingham, AL 35233, United States
- Department of Medical Education, Heersink School of Medicine, University of Alabama, Birmingham, AL 35233, United States
| | - Nancy M Lorenzi
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN 37232, United States
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19
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Lennon NJ, Kottyan LC, Kachulis C, Abul-Husn NS, Arias J, Belbin G, Below JE, Berndt SI, Chung WK, Cimino JJ, Clayton EW, Connolly JJ, Crosslin DR, Dikilitas O, Velez Edwards DR, Feng Q, Fisher M, Freimuth RR, Ge T, Glessner JT, Gordon AS, Patterson C, Hakonarson H, Harden M, Harr M, Hirschhorn JN, Hoggart C, Hsu L, Irvin MR, Jarvik GP, Karlson EW, Khan A, Khera A, Kiryluk K, Kullo I, Larkin K, Limdi N, Linder JE, Loos RJF, Luo Y, Malolepsza E, Manolio TA, Martin LJ, McCarthy L, McNally EM, Meigs JB, Mersha TB, Mosley JD, Musick A, Namjou B, Pai N, Pesce LL, Peters U, Peterson JF, Prows CA, Puckelwartz MJ, Rehm HL, Roden DM, Rosenthal EA, Rowley R, Sawicki KT, Schaid DJ, Smit RAJ, Smith JL, Smoller JW, Thomas M, Tiwari H, Toledo DM, Vaitinadin NS, Veenstra D, Walunas TL, Wang Z, Wei WQ, Weng C, Wiesner GL, Yin X, Kenny EE. Selection, optimization and validation of ten chronic disease polygenic risk scores for clinical implementation in diverse US populations. Nat Med 2024; 30:480-487. [PMID: 38374346 PMCID: PMC10878968 DOI: 10.1038/s41591-024-02796-z] [Citation(s) in RCA: 14] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2023] [Accepted: 01/02/2024] [Indexed: 02/21/2024]
Abstract
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.
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Affiliation(s)
| | - Leah C Kottyan
- Cincinnati Children's Hospital Medical Center, University of Cincinnati, Cincinnati, OH, USA
| | | | | | - Josh Arias
- National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
| | - Gillian Belbin
- Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | | | - Sonja I Berndt
- National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
| | | | - James J Cimino
- University of Alabama at Birmingham, Birmingham, AL, USA
| | | | | | - David R Crosslin
- Tulane University, New Orleans, LA, USA
- University of Washington, Seattle, WA, USA
| | | | | | - QiPing Feng
- Vanderbilt University Medical Center, Nashville, TN, USA
| | | | | | - Tian Ge
- Mass General Brigham, Boston, MA, USA
| | | | | | | | | | - Maegan Harden
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Margaret Harr
- Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Joel N Hirschhorn
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Boston Children's Hospital, Boston, MA, USA
| | - Clive Hoggart
- Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Li Hsu
- Fred Hutchinson Cancer Center, Seattle, WA, USA
| | | | | | | | | | - Amit Khera
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | | | | | - Katie Larkin
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Nita Limdi
- University of Alabama at Birmingham, Birmingham, AL, USA
| | | | - Ruth J F Loos
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Yuan Luo
- Northwestern University, Evanston, IL, USA
| | | | - Teri A Manolio
- National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
| | - Lisa J Martin
- Cincinnati Children's Hospital Medical Center, University of Cincinnati, Cincinnati, OH, USA
| | - Li McCarthy
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | | | | | - Tesfaye B Mersha
- Cincinnati Children's Hospital Medical Center, University of Cincinnati, Cincinnati, OH, USA
| | | | | | - Bahram Namjou
- Cincinnati Children's Hospital Medical Center, University of Cincinnati, Cincinnati, OH, USA
| | - Nihal Pai
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | | | | | | | - Cynthia A Prows
- Cincinnati Children's Hospital Medical Center, University of Cincinnati, Cincinnati, OH, USA
| | | | - Heidi L Rehm
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Dan M Roden
- Vanderbilt University Medical Center, Nashville, TN, USA
| | | | - Robb Rowley
- National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
| | | | | | | | | | | | | | - Hemant Tiwari
- University of Alabama at Birmingham, Birmingham, AL, USA
| | | | | | | | | | - Zhe Wang
- Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Wei-Qi Wei
- Vanderbilt University Medical Center, Nashville, TN, USA
| | | | | | | | - Eimear E Kenny
- Icahn School of Medicine at Mount Sinai, New York, NY, USA
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20
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Considerations for clinical implementation of polygenic risk scores in diverse US populations. Nat Med 2024; 30:354-355. [PMID: 38374345 DOI: 10.1038/s41591-024-02801-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/21/2024]
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21
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Casillan A, Florido ME, Galarza-Cornejo J, Bakken S, Lynch JA, Chung WK, Mittendorf KF, Berner ES, Connolly JJ, Weng C, Holm IA, Khan A, Kiryluk K, Limdi NA, Petukhova L, Sabatello M, Wynn J. Participant-guided development of bilingual genomic educational infographics for Electronic Medical Records and Genomics Phase IV study. J Am Med Inform Assoc 2024; 31:306-316. [PMID: 37860921 PMCID: PMC10797276 DOI: 10.1093/jamia/ocad207] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2023] [Revised: 09/11/2023] [Accepted: 10/06/2023] [Indexed: 10/21/2023] Open
Abstract
OBJECTIVE Developing targeted, culturally competent educational materials is critical for participant understanding of engagement in a large genomic study that uses computational pipelines to produce genome-informed risk assessments. MATERIALS AND METHODS Guided by the Smerecnik framework that theorizes understanding of multifactorial genetic disease through 3 knowledge types, we developed English and Spanish infographics for individuals enrolled in the Electronic Medical Records and Genomics Network. Infographics were developed to explain concepts in lay language and visualizations. We conducted iterative sessions using a modified "think-aloud" process with 10 participants (6 English, 4 Spanish-speaking) to explore comprehension of and attitudes towards the infographics. RESULTS We found that all but one participant had "awareness knowledge" of genetic disease risk factors upon viewing the infographics. Many participants had difficulty with "how-to" knowledge of applying genetic risk factors to specific monogenic and polygenic risks. Participant attitudes towards the iteratively-refined infographics indicated that design saturation was reached. DISCUSSION There were several elements that contributed to the participants' comprehension (or misunderstanding) of the infographics. Visualization and iconography techniques best resonated with those who could draw on prior experiences or knowledge and were absent in those without. Limited graphicacy interfered with the understanding of absolute and relative risks when presented in graph format. Notably, narrative and storytelling theory that informed the creation of a vignette infographic was most accessible to all participants. CONCLUSION Engagement with the intended audience who can identify strengths and points for improvement of the intervention is necessary to the development of effective infographics.
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Affiliation(s)
- Aimiel Casillan
- Genetic Counseling Graduate Program, Vagelos College of Physicians and Surgeons, Columbia University, New York, NY 10032, United States
| | - Michelle E Florido
- Genetic Counseling Graduate Program, Vagelos College of Physicians and Surgeons, Columbia University, New York, NY 10032, United States
- Department of Genetics and Development, Vagelos College of Physicians and Surgeons, Columbia University, New York, NY, 10032, United States
| | - Jamie Galarza-Cornejo
- Department of Pediatrics, Columbia University Irving Medical Center, New York, NY 10032, United States
| | - Suzanne Bakken
- Department of Nursing Scholarship and Research, School of Nursing, Columbia University Irving Medical Center, New York, NY 10032, United States
- Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, NY 10032, United States
| | - John A Lynch
- Department of Communication, School of Communication, Film, and Media Studies, University of Cincinnati, Cincinnati, OH 45221, United States
| | - Wendy K Chung
- Department of Pediatrics, Columbia University Irving Medical Center, New York, NY 10032, United States
| | - Kathleen F Mittendorf
- Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, TN 37232, United States
| | - Eta S Berner
- Department of Health Services Administration, School of Health Professions, University of Alabama at Birmingham, Birmingham, AL 35294, United States
| | - John J Connolly
- Center for Applied Genomics, Children’s Hospital of Philadelphia, Philadelphia, PA 19104, United States
| | - Chunhua Weng
- Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, NY 10032, United States
| | - Ingrid A Holm
- Division of Genetics and Genomics and Manton Center for Orphan Diseases Research, Boston Children’s Hospital, Boston, MA 02115, United States
- Department of Pediatrics, Harvard Medical School, Boston, MA 02115, United States
| | - Atlas Khan
- Department of Medicine, Columbia University Irving Medical Center, New York, NY 10032, United States
- Department of Medicine, Division of Nephrology, Vagelos College of Physicians & Surgeons, Columbia University, New York, NY 10032, United States
| | - Krzysztof Kiryluk
- Department of Medicine, Division of Nephrology, Vagelos College of Physicians & Surgeons, Columbia University, New York, NY 10032, United States
| | - Nita A Limdi
- Department of Neurology, Heersink School of Medicine, University of Alabama at Birmingham, Birmingham, AL 35233, United States
| | - Lynn Petukhova
- Department of Epidemiology, Columbia Mailman School of Public Health, New York, NY 10032, United States
- Department of Dermatology, Columbia University Irving Medical Center, New York, NY 10032, United States
| | - Maya Sabatello
- Department of Medicine, Center for Precision Medicine and Genomics, Columbia University Irving Medical Center, New York, NY 10032, United States
- Department of Medical Humanities and Ethics, Division of Ethics, Columbia University Irving Medical Center, New York, NY 10032, United States
| | - Julia Wynn
- Genetic Counseling Graduate Program, Vagelos College of Physicians and Surgeons, Columbia University, New York, NY 10032, United States
- Department of Pediatrics, Columbia University Irving Medical Center, New York, NY 10032, United States
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22
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Kachuri L, Chatterjee N, Hirbo J, Schaid DJ, Martin I, Kullo IJ, Kenny EE, Pasaniuc B, Witte JS, Ge T. Principles and methods for transferring polygenic risk scores across global populations. Nat Rev Genet 2024; 25:8-25. [PMID: 37620596 PMCID: PMC10961971 DOI: 10.1038/s41576-023-00637-2] [Citation(s) in RCA: 32] [Impact Index Per Article: 32.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/11/2023] [Indexed: 08/26/2023]
Abstract
Polygenic risk scores (PRSs) summarize the genetic predisposition of a complex human trait or disease and may become a valuable tool for advancing precision medicine. However, PRSs that are developed in populations of predominantly European genetic ancestries can increase health disparities due to poor predictive performance in individuals of diverse and complex genetic ancestries. We describe genetic and modifiable risk factors that limit the transferability of PRSs across populations and review the strengths and weaknesses of existing PRS construction methods for diverse ancestries. Developing PRSs that benefit global populations in research and clinical settings provides an opportunity for innovation and is essential for health equity.
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Affiliation(s)
- Linda Kachuri
- Department of Epidemiology and Population Health, Stanford University School of Medicine, Stanford, CA, USA
- Stanford Cancer Institute, Stanford University School of Medicine, Stanford, CA, USA
| | - Nilanjan Chatterjee
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Jibril Hirbo
- Department of Medicine Division of Genetic Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Daniel J Schaid
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, USA
| | - Iman Martin
- Division of Genomic Medicine, National Human Genome Research Institute, Bethesda, MD, USA
| | - Iftikhar J Kullo
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, USA
| | - Eimear E Kenny
- Institute for Genomic Health, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Bogdan Pasaniuc
- Department of Computational Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - John S Witte
- Department of Epidemiology and Population Health, Stanford University School of Medicine, Stanford, CA, USA.
- Stanford Cancer Institute, Stanford University School of Medicine, Stanford, CA, USA.
- Department of Biomedical Data Science, Stanford University, Stanford, CA, USA.
- Department of Genetics, Stanford University, Stanford, CA, USA.
| | - Tian Ge
- Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA.
- Center for Precision Psychiatry, Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
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23
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Tokutomi T, Yoshida A, Fukushima A, Nagami F, Minoura Y, Sasaki M. Stakeholder Perception of the Implementation of Genetic Risk Testing for Twelve Multifactorial Diseases. Genes (Basel) 2023; 15:49. [PMID: 38254940 PMCID: PMC10815213 DOI: 10.3390/genes15010049] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2023] [Revised: 12/22/2023] [Accepted: 12/27/2023] [Indexed: 01/24/2024] Open
Abstract
Genome-wide association studies have been employed to develop numerous risk prediction models using polygenic risk scores (PRSs) for multifactorial diseases. However, healthcare providers lack confidence in their understanding of PRS risk stratification for multifactorial diseases, which underscores the need to assess the readiness of PRSs for clinical use. To address this issue, we surveyed the perceptions of healthcare providers as stakeholders in the clinical implementation of genetic-based risk prediction for multifactorial diseases. We conducted a web-based study on the need for risk prediction based on genetic information and the appropriate timing of testing for 12 multifactorial diseases. Responses were obtained from 506 stakeholders. Positive perceptions of genetic risk testing were found for adult-onset chronic diseases. As per participant opinion, testing for adult-onset diseases should be performed after the age of 20 years, whereas testing for psychiatric and allergic disorders that manifest during childhood should be performed from birth to 19 years of age. The stakeholders recognized the need for genetic risk testing for diseases that develop in adulthood, believing that the appropriate testing time is after maturity. This study contributes to the discussion on the clinical implementation of the PRS for genetic risk prediction of multifactorial diseases.
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Affiliation(s)
- Tomoharu Tokutomi
- Iwate Tohoku Medical Megabank Organization, Iwate Medical University, Shiwa 020-3694, Japan; (A.Y.)
- Department of Clinical Genetics, School of Medicine, Iwate Medical University, Iwate 020-8505, Japan
| | - Akiko Yoshida
- Iwate Tohoku Medical Megabank Organization, Iwate Medical University, Shiwa 020-3694, Japan; (A.Y.)
- Department of Clinical Genetics, School of Medicine, Iwate Medical University, Iwate 020-8505, Japan
| | - Akimune Fukushima
- Iwate Tohoku Medical Megabank Organization, Iwate Medical University, Shiwa 020-3694, Japan; (A.Y.)
- Department of Clinical Genetics, School of Medicine, Iwate Medical University, Iwate 020-8505, Japan
| | - Fuji Nagami
- Tohoku Medical Megabank Organization, Tohoku University, Sendai 980-0872, Japan
| | - Yuko Minoura
- Departments of Medical Genetics and Genomics, School of Medicine, Sapporo Medical University, Sapporo 060-8556, Japan
| | - Makoto Sasaki
- Iwate Tohoku Medical Megabank Organization, Iwate Medical University, Shiwa 020-3694, Japan; (A.Y.)
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24
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Linder JE, Tao R, Chung WK, Kiryluk K, Liu C, Weng C, Connolly JJ, Hakonarson H, Harr M, Leppig KA, Jarvik GP, Veenstra DL, Aufox S, Chisholm RL, Gordon AS, Hoell C, Rasmussen-Torvik LJ, Smith ME, Holm IA, Miller EM, Prows CA, Elskeally O, Kullo IJ, Lee C, Jose S, Manolio TA, Rowley R, Padi-Adjirackor NA, Wilmayani NK, City B, Wei WQ, Wiesner GL, Rahm AK, Williams JL, Williams MS, Peterson JF. Prospective, multi-site study of healthcare utilization after actionable monogenic findings from clinical sequencing. Am J Hum Genet 2023; 110:1950-1958. [PMID: 37883979 PMCID: PMC10645563 DOI: 10.1016/j.ajhg.2023.10.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2023] [Revised: 10/05/2023] [Accepted: 10/08/2023] [Indexed: 10/28/2023] Open
Abstract
As large-scale genomic screening becomes increasingly prevalent, understanding the influence of actionable results on healthcare utilization is key to estimating the potential long-term clinical impact. The eMERGE network sequenced individuals for actionable genes in multiple genetic conditions and returned results to individuals, providers, and the electronic health record. Differences in recommended health services (laboratory, imaging, and procedural testing) delivered within 12 months of return were compared among individuals with pathogenic or likely pathogenic (P/LP) findings to matched individuals with negative findings before and after return of results. Of 16,218 adults, 477 unselected individuals were found to have a monogenic risk for arrhythmia (n = 95), breast cancer (n = 96), cardiomyopathy (n = 95), colorectal cancer (n = 105), or familial hypercholesterolemia (n = 86). Individuals with P/LP results more frequently received services after return (43.8%) compared to before return (25.6%) of results and compared to individuals with negative findings (24.9%; p < 0.0001). The annual cost of qualifying healthcare services increased from an average of $162 before return to $343 after return of results among the P/LP group (p < 0.0001); differences in the negative group were non-significant. The mean difference-in-differences was $149 (p < 0.0001), which describes the increased cost within the P/LP group corrected for cost changes in the negative group. When stratified by individual conditions, significant cost differences were observed for arrhythmia, breast cancer, and cardiomyopathy. In conclusion, less than half of individuals received billed health services after monogenic return, which modestly increased healthcare costs for payors in the year following return.
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Affiliation(s)
- Jodell E Linder
- Vanderbilt University Medical Center, Nashville, TN 37203, USA.
| | - Ran Tao
- Vanderbilt University Medical Center, Nashville, TN 37203, USA
| | | | | | - Cong Liu
- Columbia University, New York, NY 10032, USA
| | | | - John J Connolly
- Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA
| | - Hakon Hakonarson
- Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA; Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Margaret Harr
- Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA
| | - Kathleen A Leppig
- Genetic Services, Kaiser Permanente of Washington, Seattle, WA 98195, USA
| | - Gail P Jarvik
- University of Washington Medical Center, Departments of Medicine (Medical Genetics) and Genome Sciences, Seattle, WA 98195, USA
| | - David L Veenstra
- University of Washington, Department of Pharmacy, Seattle, WA 98195, USA
| | - Sharon Aufox
- Northwestern University, Center for Genetic Medicine, Chicago, IL 60611, USA
| | - Rex L Chisholm
- Northwestern University, Center for Genetic Medicine, Chicago, IL 60611, USA
| | - Adam S Gordon
- Northwestern University, Center for Genetic Medicine, Chicago, IL 60611, USA
| | - Christin Hoell
- Northwestern University, Center for Genetic Medicine, Chicago, IL 60611, USA
| | | | - Maureen E Smith
- Northwestern University, Center for Genetic Medicine, Chicago, IL 60611, USA
| | | | - Erin M Miller
- Division of Cardiology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH 45229, USA; Department of Pediatrics, University of Cincinnati, College of Medicine, Cincinnati, OH, USA
| | - Cynthia A Prows
- Division of Human Genetics, Cincinnati Children's Hospital Medical Center, Cincinnati, OH 45229, USA
| | | | | | | | - Sheethal Jose
- National Human Genome Research Institute, Bethesda, MD 20892, USA
| | - Teri A Manolio
- National Human Genome Research Institute, Bethesda, MD 20892, USA
| | - Robb Rowley
- National Human Genome Research Institute, Bethesda, MD 20892, USA
| | | | | | - Brittany City
- Vanderbilt University Medical Center, Nashville, TN 37203, USA
| | - Wei-Qi Wei
- Vanderbilt University Medical Center, Nashville, TN 37203, USA
| | | | | | - Janet L Williams
- Department of Genomic Health, Geisinger, Danville, PA 17822, USA
| | - Marc S Williams
- Department of Genomic Health, Geisinger, Danville, PA 17822, USA
| | - Josh F Peterson
- Vanderbilt University Medical Center, Nashville, TN 37203, USA
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25
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Vassy JL, Brunette CA, Lebo MS, MacIsaac K, Yi T, Danowski ME, Alexander NVJ, Cardellino MP, Christensen KD, Gala M, Green RC, Harris E, Jones NE, Kerman BJ, Kraft P, Kulkarni P, Lewis ACF, Lubitz SA, Natarajan P, Antwi AA. The GenoVA study: Equitable implementation of a pragmatic randomized trial of polygenic-risk scoring in primary care. Am J Hum Genet 2023; 110:1841-1852. [PMID: 37922883 PMCID: PMC10645559 DOI: 10.1016/j.ajhg.2023.10.001] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2023] [Revised: 10/03/2023] [Accepted: 10/03/2023] [Indexed: 11/07/2023] Open
Abstract
Polygenic risk scores (PRSs) hold promise for disease risk assessment and prevention. The Genomic Medicine at Veterans Affairs (GenoVA) Study is addressing three main challenges to the clinical implementation of PRSs in preventive care: defining and determining their clinical utility, implementing them in time-constrained primary care settings, and countering their potential to exacerbate healthcare disparities. The study processes used to test patients, report their PRS results to them and their primary care providers (PCPs), and promote the use of those results in clinical decision-making are modeled on common practices in primary care. The following diseases were chosen for their prevalence and familiarity to PCPs: coronary artery disease; type 2 diabetes; atrial fibrillation; and breast, colorectal, and prostate cancers. A randomized clinical trial (RCT) design and primary outcome of time-to-new-diagnosis of a target disease bring methodological rigor to the question of the clinical utility of PRS implementation. The study's pragmatic RCT design enhances its relevance to how PRS might reasonably be implemented in primary care. Steps the study has taken to promote health equity include the thoughtful handling of genetic ancestry in PRS construction and reporting and enhanced recruitment strategies to address underrepresentation in research participation. To date, enhanced recruitment efforts have been both necessary and successful: participants of underrepresented race and ethnicity groups have been less likely to enroll in the study than expected but ultimately achieved proportional representation through targeted efforts. The GenoVA Study experience to date offers insights for evaluating the clinical utility of equitable PRS implementation in adult primary care.
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Affiliation(s)
- Jason L Vassy
- VA Boston Healthcare System, Boston, MA, USA; Division of General Internal Medicine and Primary Care, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA; Harvard Medical School, Boston, MA, USA; Broad Institute of Massachusetts Institute of Technology and Harvard University, Cambridge, MA, USA; Ariadne Labs, Boston, MA, USA.
| | - Charles A Brunette
- VA Boston Healthcare System, Boston, MA, USA; Harvard Medical School, Boston, MA, USA
| | - Matthew S Lebo
- Harvard Medical School, Boston, MA, USA; Laboratory for Molecular Medicine, Mass General Brigham, Boston, MA, USA; Department of Pathology, Brigham and Women's Hospital, Boston, MA, USA
| | | | - Thomas Yi
- VA Boston Healthcare System, Boston, MA, USA
| | | | - Nicholas V J Alexander
- VA Boston Healthcare System, Boston, MA, USA; Bucharest University Emergency Hospital, Bucharest, Romania; Bucharest University of Economic Studies, Bucharest, Romania
| | | | - Kurt D Christensen
- Harvard Medical School, Boston, MA, USA; Department of Population Medicine, Harvard Pilgrim Health Care Institute, Boston, MA, USA
| | - Manish Gala
- Harvard Medical School, Boston, MA, USA; Division of Gastroenterology and Clinical and Translational Epidemiology Unit, Massachusetts General Hospital, Boston, MA, USA
| | - Robert C Green
- Harvard Medical School, Boston, MA, USA; Broad Institute of Massachusetts Institute of Technology and Harvard University, Cambridge, MA, USA; Ariadne Labs, Boston, MA, USA; Department of Medicine (Genetics), Mass General Brigham, Boston, MA, USA
| | | | - Natalie E Jones
- VA Boston Healthcare System, Boston, MA, USA; Harvard Medical School, Boston, MA, USA
| | - Benjamin J Kerman
- Division of General Internal Medicine and Primary Care, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA; Harvard Medical School, Boston, MA, USA
| | - Peter Kraft
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD, USA
| | | | - Anna C F Lewis
- Department of Medicine (Genetics), Mass General Brigham, Boston, MA, USA; Edmond and Lily Safra Center for Ethics, Harvard University, Boston, MA, USA
| | - Steven A Lubitz
- Demoulas Center for Cardiac Arrhythmias, Massachusetts General Hospital, Boston, MA, USA; Novartis Institutes for BioMedical Research, Novartis, Basel, Basel-Stadt, Switzerland
| | - Pradeep Natarajan
- Harvard Medical School, Boston, MA, USA; Broad Institute of Massachusetts Institute of Technology and Harvard University, Cambridge, MA, USA; Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA; Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA, USA
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Forsyth JK, Bearden CE. Rethinking the First Episode of Schizophrenia: Identifying Convergent Mechanisms During Development and Moving Toward Prediction. Am J Psychiatry 2023; 180:792-804. [PMID: 37908094 DOI: 10.1176/appi.ajp.20230736] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/02/2023]
Affiliation(s)
- Jennifer K Forsyth
- Department of Psychology, University of Washington, Seattle (Forsyth); Semel Institute for Neuroscience and Human Behavior, Department of Psychiatry and Behavioral Sciences, and Department of Psychology, University of California, Los Angeles (Bearden)
| | - Carrie E Bearden
- Department of Psychology, University of Washington, Seattle (Forsyth); Semel Institute for Neuroscience and Human Behavior, Department of Psychiatry and Behavioral Sciences, and Department of Psychology, University of California, Los Angeles (Bearden)
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27
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Abstract
Since the publication of the first genome-wide association study for cancer in 2007, thousands of common alleles that are associated with the risk of cancer have been identified. The relative risk associated with individual variants is small and of limited clinical significance. However, the combined effect of multiple risk variants as captured by polygenic scores (PGSs) may be much greater and therefore provide risk discrimination that is clinically useful. We review the considerable research efforts over the past 15 years for developing statistical methods for PGSs and their application in large-scale genome-wide association studies to develop PGSs for various cancers. We review the predictive performance of these PGSs and the multiple challenges currently limiting the clinical application of PGSs. Despite this, PGSs are beginning to be incorporated into clinical multifactorial risk prediction models to stratify risk in both clinical trials and clinical implementation studies.
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Affiliation(s)
- Xin Yang
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Siddhartha Kar
- MRC Integrative Epidemiology Unit, Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
- Early Cancer Institute, Department of Oncology, University of Cambridge, Cambridge, UK
| | - Antonis C Antoniou
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Paul D P Pharoah
- Department of Computational Biomedicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
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28
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Saadatagah S, Naderian M, Dikilitas O, Hamed ME, Bangash H, Kullo IJ. Polygenic Risk, Rare Variants, and Family History: Independent and Additive Effects on Coronary Heart Disease. JACC. ADVANCES 2023; 2:100567. [PMID: 38939477 PMCID: PMC11198423 DOI: 10.1016/j.jacadv.2023.100567] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/03/2023] [Revised: 05/30/2023] [Accepted: 06/09/2023] [Indexed: 06/29/2024]
Abstract
Background Genetic factors are not included in prediction models for coronary heart disease (CHD). Objectives The authors assessed the predictive utility of a polygenic risk score (PRS) for CHD (defined as myocardial infarction, coronary revascularization, or cardiovascular death) and whether the risks due to monogenic familial hypercholesterolemia (FH) and family history (FamHx) are independent of and additive to the PRS. Methods In UK-biobank participants, PRSCHD was calculated using metaGRS, and 10-year risk for incident CHD was estimated using the pooled cohort equations (PCE). The area under the curve (AUC) of the receiver operator curve and net reclassification improvement (NRI) were assessed. FH was defined as the presence of a pathogenic or likely pathogenic variant in LDLR, APOB, or PCSK9. FamHx was defined as a diagnosis of CHD in first-degree relatives. Independent and additive effects of PRSCHD, FH, and FamHx were evaluated in stratified analyses. Results In 323,373 participants with genotype data, the addition of PRSCHD to PCE increased the AUC from 0.759 (95% CI: 0.755-0.763) to 0.773 (95% CI: 0.769-0.777). The AUC and NRIEvent for PRSCHD were higher before the age of 55 years. Of 199,997 participants with exome sequence data, 10,000 had a PRSCHD ≥95th percentile (PRSP95), 673 had FH, and 46,163 had FamHx. The CHD risk associated with PRSP95 was independent of FH and FamHx. The risks associated with combinations of PRSCHD, FH, and FamHx were additive and comprehensive estimates could be obtained by multiplying the risk from each genetic factor. Conclusions Incorporating PRSCHD into the PCE improves risk prediction for CHD, especially at younger ages. The associations of PRSCHD, FH, and FamHx with CHD were independent and additive.
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Affiliation(s)
| | | | - Ozan Dikilitas
- Departments of Internal Medicine and Cardiovascular Medicine, and Mayo Clinician-Investigator Training Program, Mayo Clinic, Rochester, Minnesota, USA
| | - Marwan E. Hamed
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, Minnesota, USA
| | - Hana Bangash
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, Minnesota, USA
| | - Iftikhar J. Kullo
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, Minnesota, USA
- Gonda Vascular Center, Mayo Clinic, Rochester, Minnesota, USA
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29
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Guo B, Knerr S, Kauffman TL, Mittendorf KF, Keast E, Gilmore MJ, Feigelson HS, Lynch FL, Muessig KR, Okuyama S, Zepp JM, Veenstra DL, Hsu L, Phipps AI, Lindström S, Leo MC, Goddard KAB, Wilfond BS, Devine B. Risk management actions following genetic testing in the Cancer Health Assessments Reaching Many (CHARM) Study: A prospective cohort study. Cancer Med 2023; 12:19112-19125. [PMID: 37644850 PMCID: PMC10557878 DOI: 10.1002/cam4.6485] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2023] [Revised: 08/07/2023] [Accepted: 08/18/2023] [Indexed: 08/31/2023] Open
Abstract
BACKGROUND Genetic testing can identify cancer risk early, enabling prevention and early detection. We describe use of risk management interventions following genetic testing in the Cancer Health Assessment Reaching Many (CHARM) study. CHARM assessed risk and provided genetic testing to low income, low literacy, and other underserved populations that historically face barriers to accessing cancer genetic services. METHODS CHARM was implemented in Kaiser Permanente Northwest (KPNW) and Denver Health (DH) between 2018 and 2020. We identified post-testing screening (mammography, breast MRI, colonoscopy) and surgical (mastectomy, oophorectomy) procedures using electronic health records. We examined utilization in participants who did and did not receive actionable risk management recommendations from study genetic counselors following national guidelines. RESULTS CHARM participants were followed for an average of 15.4 months (range: 0.4-27.8 months) after results disclosure. Less than 2% (11/680) received actionable risk management recommendations (i.e., could be completed in the initial years following testing) based on their test result. Among those who received actionable recommendations, risk management utilization was moderate (54.5%, 6/11 completed any procedure) and varied by procedure (mammogram: 0/3; MRI: 2/4; colonoscopy: 4/5; mastectomy: 1/5; oophorectomy: 0/3). Cancer screening and surgery procedures were rare in participants without actionable recommendations. CONCLUSION Though the number of participants who received actionable risk management recommendations was small, our results suggest that implementing CHARM's risk assessment and testing model increased access to evidence-based genetic services and provided opportunities for patients to engage in recommended preventive care, without encouraging risk management overuse.
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Affiliation(s)
- Boya Guo
- School of Public HealthUniversity of WashingtonSeattleWashingtonUSA
| | - Sarah Knerr
- School of Public HealthUniversity of WashingtonSeattleWashingtonUSA
| | - Tia L. Kauffman
- Center for Health Research, Kaiser Permanente NorthwestPortlandOregonUSA
| | - Kathleen F. Mittendorf
- Vanderbilt‐Ingram Cancer CenterVanderbilt University Medical CenterNashvilleTennesseeUSA
| | - Erin Keast
- Center for Health Research, Kaiser Permanente NorthwestPortlandOregonUSA
| | - Marian J. Gilmore
- Department of Translational and Applied GenomicsCenter for Health ResearchPortlandOregonUSA
| | | | - Frances L. Lynch
- Center for Health Research, Kaiser Permanente NorthwestPortlandOregonUSA
| | - Kristin R. Muessig
- Department of Translational and Applied GenomicsCenter for Health ResearchPortlandOregonUSA
| | - Sonia Okuyama
- Division of Oncology, Denver Health and Hospital AuthorityDenverColoradoUSA
| | - Jamilyn M. Zepp
- Department of Translational and Applied GenomicsCenter for Health ResearchPortlandOregonUSA
| | - David L. Veenstra
- The Comparative Health Outcomes, Policy, and Economics (CHOICE) Institute, School of PharmacyUniversity of WashingtonSeattleWashingtonUSA
| | - Li Hsu
- School of Public HealthUniversity of WashingtonSeattleWashingtonUSA
- Division of Public Health SciencesFred Hutchinson Cancer CenterSeattleWashingtonUSA
| | - Amanda I. Phipps
- School of Public HealthUniversity of WashingtonSeattleWashingtonUSA
- Division of Public Health SciencesFred Hutchinson Cancer CenterSeattleWashingtonUSA
| | - Sara Lindström
- School of Public HealthUniversity of WashingtonSeattleWashingtonUSA
- Division of Public Health SciencesFred Hutchinson Cancer CenterSeattleWashingtonUSA
| | - Michael C. Leo
- Center for Health Research, Kaiser Permanente NorthwestPortlandOregonUSA
| | - Katrina A. B. Goddard
- Department of Translational and Applied GenomicsCenter for Health ResearchPortlandOregonUSA
| | - Benjamin S. Wilfond
- Treuman Katz Center for Pediatric BioethicsSeattle Children's Research InstituteSeattleWashingtonUSA
- Department of Pediatrics, Division of Bioethics and Palliative CareUniversity of WashingtonSeattleWashingtonUSA
| | - Beth Devine
- School of Public HealthUniversity of WashingtonSeattleWashingtonUSA
- The Comparative Health Outcomes, Policy, and Economics (CHOICE) Institute, School of PharmacyUniversity of WashingtonSeattleWashingtonUSA
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30
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Connolly JJ, Berner ES, Smith M, Levy S, Terek S, Harr M, Karavite D, Suckiel S, Holm IA, Dufendach K, Nelson C, Khan A, Chisholm RL, Allworth A, Wei WQ, Bland HT, Clayton EW, Soper ER, Linder JE, Limdi NA, Miller A, Nigbur S, Bangash H, Hamed M, Sherafati A, Lewis ACF, Perez E, Orlando LA, Rakhra-Burris TK, Al-Dulaimi M, Cifric S, Scherr CL, Wynn J, Hakonarson H, Sabatello M. Education and electronic medical records and genomics network, challenges, and lessons learned from a large-scale clinical trial using polygenic risk scores. Genet Med 2023; 25:100906. [PMID: 37246632 PMCID: PMC10527667 DOI: 10.1016/j.gim.2023.100906] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2023] [Revised: 05/17/2023] [Accepted: 05/18/2023] [Indexed: 05/30/2023] Open
Abstract
Polygenic risk scores (PRS) have potential to improve health care by identifying individuals that have elevated risk for common complex conditions. Use of PRS in clinical practice, however, requires careful assessment of the needs and capabilities of patients, providers, and health care systems. The electronic Medical Records and Genomics (eMERGE) network is conducting a collaborative study which will return PRS to 25,000 pediatric and adult participants. All participants will receive a risk report, potentially classifying them as high risk (∼2-10% per condition) for 1 or more of 10 conditions based on PRS. The study population is enriched by participants from racial and ethnic minority populations, underserved populations, and populations who experience poorer medical outcomes. All 10 eMERGE clinical sites conducted focus groups, interviews, and/or surveys to understand educational needs among key stakeholders-participants, providers, and/or study staff. Together, these studies highlighted the need for tools that address the perceived benefit/value of PRS, types of education/support needed, accessibility, and PRS-related knowledge and understanding. Based on findings from these preliminary studies, the network harmonized training initiatives and formal/informal educational resources. This paper summarizes eMERGE's collective approach to assessing educational needs and developing educational approaches for primary stakeholders. It discusses challenges encountered and solutions provided.
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Affiliation(s)
- John J Connolly
- Center for Applied Genomics, Children's Hospital of Philadelphia, PA.
| | - Eta S Berner
- Department of Health Services Administration, University of Alabama at Birmingham, Birmingham, AL
| | - Maureen Smith
- Center for Genetic Medicine, Department of Medicine, Northwestern University, Chicago, IL
| | - Samuel Levy
- Center for Applied Genomics, Children's Hospital of Philadelphia, PA
| | - Shannon Terek
- Center for Applied Genomics, Children's Hospital of Philadelphia, PA
| | - Margaret Harr
- Center for Applied Genomics, Children's Hospital of Philadelphia, PA
| | - Dean Karavite
- Department of Biomedical and Health Informatics, Children's Hospital of Philadelphia, PA
| | - Sabrina Suckiel
- The Institute for Genomic Health, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY
| | - Ingrid A Holm
- Division of Genetics and Genomics, Boston Children's Hospital; Department of Pediatrics, Harvard Medical School, Boston, MA
| | - Kevin Dufendach
- Department of Pediatrics, University of Cincinnati, Cincinnati, OH
| | - Catrina Nelson
- Center for Autoimmune Genomics and Etiology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH
| | - Atlas Khan
- Division of Nephrology, Dept of Medicine, Vagelos College of Physicians & Surgeons, Columbia University, New York, NY
| | - Rex L Chisholm
- Center for Genetic Medicine, Feinberg School of Medicine, Northwestern University, Chicago, IL
| | - Aimee Allworth
- Department of Medical Genetics, University of Washington, Seattle, WA
| | - Wei-Qi Wei
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN
| | - Harris T Bland
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN
| | - Ellen Wright Clayton
- Division of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN; Center for Biomedical Ethics and Society, Vanderbilt University, Nashville, TN; Vanderbilt University Law School, Nashville, TN
| | - Emily R Soper
- The Institute for Genomic Health, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY; Division of Genomic Medicine, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY
| | - Jodell E Linder
- Vanderbilt Institute for Clinical and Translational Research, Vanderbilt University Medical Center, Nashville, TN
| | - Nita A Limdi
- Department of Neurology, Heersink School of Medicine, University of Alabama at Birmingham, Birmingham, AL
| | - Alexandra Miller
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN; Department of Clinical Genomics, Mayo Clinic, Rochester, MN
| | - Scott Nigbur
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN
| | - Hana Bangash
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN
| | - Marwan Hamed
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN
| | - Alborz Sherafati
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN
| | - Anna C F Lewis
- Edmond and Lily Safra Center for Ethics, Harvard, MA; Brigham and Women's Hospital, Boston, MA
| | - Emma Perez
- Mass General Brigham Personalized Medicine, Brigham and Women's Hospital, Boston, MA
| | | | | | | | - Selma Cifric
- Department of Biology, The College of Idaho, Caldwell, ID
| | - Courtney Lynam Scherr
- School of Communication | Department of Communication Studies, Northwestern University, Chicago, IL
| | - Julia Wynn
- Department of Pediatrics, Columbia University Irving Medical Center, New York, NY
| | - Hakon Hakonarson
- Center for Applied Genomics, Children's Hospital of Philadelphia, PA; Department of Pediatrics, The Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA; Division of Human Genetics, Children's Hospital of Philadelphia, Philadelphia, PA; Division of Pulmonary Medicine, Children's Hospital of Philadelphia, Philadelphia, PA
| | - Maya Sabatello
- Center for Precision Medicine & Genomics, Department of Medicine, Columbia University Irving Medical Center, New York, NY; Division of Ethics, Department of Medical Humanities & Ethics, Columbia University Irving Medical Center, New York, NY.
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31
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Clayton EW, Smith ME, Anderson KC, Chung WK, Connolly JJ, Fullerton SM, McGowan ML, Peterson JF, Prows CA, Sabatello M, Holm IA. Studying the impact of translational genomic research: Lessons from eMERGE. Am J Hum Genet 2023; 110:1021-1033. [PMID: 37343562 PMCID: PMC10357472 DOI: 10.1016/j.ajhg.2023.05.011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2023] [Revised: 05/22/2023] [Accepted: 05/23/2023] [Indexed: 06/23/2023] Open
Abstract
Two major goals of the Electronic Medical Record and Genomics (eMERGE) Network are to learn how best to return research results to patient/participants and the clinicians who care for them and also to assess the impact of placing these results in clinical care. Yet since its inception, the Network has confronted a host of challenges in achieving these goals, many of which had ethical, legal, or social implications (ELSIs) that required consideration. Here, we share impediments we encountered in recruiting participants, returning results, and assessing their impact, all of which affected our ability to achieve the goals of eMERGE, as well as the steps we took to attempt to address these obstacles. We divide the domains in which we experienced challenges into four broad categories: (1) study design, including recruitment of more diverse groups; (2) consent; (3) returning results to participants and their health care providers (HCPs); and (4) assessment of follow-up care of participants and measuring the impact of research on participants and their families. Since most phases of eMERGE have included children as well as adults, we also address the particular ELSI posed by including pediatric populations in this research. We make specific suggestions for improving translational genomic research to ensure that future projects can effectively return results and assess their impact on patient/participants and providers if the goals of genomic-informed medicine are to be achieved.
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Affiliation(s)
- Ellen Wright Clayton
- Center for Biomedical Ethics and Society, Departments of Pediatrics and Health Policy, Vanderbilt University Medical Center, Nashville, TN 37203, USA.
| | - Maureen E Smith
- Department of Medicine, Center for Genetic Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA
| | - Katherine C Anderson
- Department of Medicine, Vanderbilt University Medical Center, Nashville, TN 37232, USA
| | - Wendy K Chung
- Departments of Pediatrics and Medicine, Columbia University, New York, NY 10032, USA
| | - John J Connolly
- Center for Applied Genomics, Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA
| | - Stephanie M Fullerton
- Department of Bioethics & Humanities, University of Washington School of Medicine, Seattle, WA 98195, USA
| | - Michelle L McGowan
- Biomedical Ethics Research Program, Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN 55905, USA; Department of Women's, Gender, and Sexuality Studies, University of Cincinnati, Cincinnati, OH 45221, USA
| | - Josh F Peterson
- Center for Precision Medicine, Department of Biomedical Informatics, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN 37203, USA
| | - Cynthia A Prows
- Divisions of Human Genetics and Patient Services, Cincinnati Children's Hospital, Cincinnati, OH 45229, USA
| | - Maya Sabatello
- Center for Precision Medicine & Genomics, Department of Medicine, and Division of Ethics, Department of Medical Humanities & Ethics Columbia University Vagelos College of Physicians and Surgeons, NY, NY 10032, USA
| | - Ingrid A Holm
- Division of Genetics and Genomics, Boston Children's Hospital; Department of Pediatrics, Harvard Medical School, Boston, MA 02115, USA
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32
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Patel AP, Wang M, Ruan Y, Koyama S, Clarke SL, Yang X, Tcheandjieu C, Agrawal S, Fahed AC, Ellinor PT, Tsao PS, Sun YV, Cho K, Wilson PWF, Assimes TL, van Heel DA, Butterworth AS, Aragam KG, Natarajan P, Khera AV. A multi-ancestry polygenic risk score improves risk prediction for coronary artery disease. Nat Med 2023; 29:1793-1803. [PMID: 37414900 PMCID: PMC10353935 DOI: 10.1038/s41591-023-02429-x] [Citation(s) in RCA: 50] [Impact Index Per Article: 50.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2022] [Accepted: 05/30/2023] [Indexed: 07/08/2023]
Abstract
Identification of individuals at highest risk of coronary artery disease (CAD)-ideally before onset-remains an important public health need. Prior studies have developed genome-wide polygenic scores to enable risk stratification, reflecting the substantial inherited component to CAD risk. Here we develop a new and significantly improved polygenic score for CAD, termed GPSMult, that incorporates genome-wide association data across five ancestries for CAD (>269,000 cases and >1,178,000 controls) and ten CAD risk factors. GPSMult strongly associated with prevalent CAD (odds ratio per standard deviation 2.14, 95% confidence interval 2.10-2.19, P < 0.001) in UK Biobank participants of European ancestry, identifying 20.0% of the population with 3-fold increased risk and conversely 13.9% with 3-fold decreased risk as compared with those in the middle quintile. GPSMult was also associated with incident CAD events (hazard ratio per standard deviation 1.73, 95% confidence interval 1.70-1.76, P < 0.001), identifying 3% of healthy individuals with risk of future CAD events equivalent to those with existing disease and significantly improving risk discrimination and reclassification. Across multiethnic, external validation datasets inclusive of 33,096, 124,467, 16,433 and 16,874 participants of African, European, Hispanic and South Asian ancestry, respectively, GPSMult demonstrated increased strength of associations across all ancestries and outperformed all available previously published CAD polygenic scores. These data contribute a new GPSMult for CAD to the field and provide a generalizable framework for how large-scale integration of genetic association data for CAD and related traits from diverse populations can meaningfully improve polygenic risk prediction.
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Affiliation(s)
- Aniruddh P Patel
- Division of Cardiology, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA
- Center for Genomic Medicine, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA
- Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA, USA
| | - Minxian Wang
- CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing, China.
| | - Yunfeng Ruan
- Center for Genomic Medicine, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA
- Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Satoshi Koyama
- Center for Genomic Medicine, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA
- Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Veteran Affairs Boston Healthcare System, Boston, MA, USA
| | - Shoa L Clarke
- Stanford University School of Medicine, Palo Alto, CA, USA
- Veterans Affairs Palo Alto Healthcare System, Palo Alto, CA, USA
| | - Xiong Yang
- CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing, China
| | | | - Saaket Agrawal
- Center for Genomic Medicine, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA
- Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
| | - Akl C Fahed
- Division of Cardiology, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA
- Center for Genomic Medicine, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA
- Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA, USA
| | - Patrick T Ellinor
- Division of Cardiology, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA
- Center for Genomic Medicine, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA
- Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA, USA
| | - Philip S Tsao
- Stanford University School of Medicine, Palo Alto, CA, USA
- Veterans Affairs Palo Alto Healthcare System, Palo Alto, CA, USA
| | - Yan V Sun
- Veteran Affairs Atlanta Healthcare System, Decatur, GA, USA
| | - Kelly Cho
- Veteran Affairs Boston Healthcare System, Boston, MA, USA
| | | | - Themistocles L Assimes
- Stanford University School of Medicine, Palo Alto, CA, USA
- Veterans Affairs Palo Alto Healthcare System, Palo Alto, CA, USA
| | - David A van Heel
- Blizard Institute, Barts and the London School of Medicine and Dentistry, Queen Mary University of London, London, UK
| | - Adam S Butterworth
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, and Centre of Research Excellence, University of Cambridge, Cambridge, UK
| | - Krishna G Aragam
- Division of Cardiology, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA
- Center for Genomic Medicine, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA
- Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA, USA
| | - Pradeep Natarajan
- Division of Cardiology, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA
- Center for Genomic Medicine, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA
- Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA, USA
| | - Amit V Khera
- Center for Genomic Medicine, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA.
- Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
- Department of Medicine, Harvard Medical School, Boston, MA, USA.
- Verve Therapeutics, Boston, MA, USA.
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33
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Smith JL, Tcheandjieu C, Dikilitas O, lyer K, Miyazawa K, Hilliard A, Lynch J, Rotter JI, Chen YDI, Sheu WHH, Chang KM, Kanoni S, Tsao P, Ito K, Kosel M, Clarke SL, Schaid DJ, Assimes TL, Kullo IJ. A Multi-Ancestry Polygenic Risk Score for Coronary Heart Disease Based on an Ancestrally Diverse Genome-Wide Association Study and Population-Specific Optimization. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.06.02.23290896. [PMID: 37609230 PMCID: PMC10441485 DOI: 10.1101/2023.06.02.23290896] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/24/2023]
Abstract
Background Predictive performance of polygenic risk scores (PRS) varies across populations. To facilitate equitable clinical use, we developed PRS for coronary heart disease (PRSCHD) for 5 genetic ancestry groups. Methods We derived ancestry-specific and multi-ancestry PRSCHD based on pruning and thresholding (PRSP+T) and continuous shrinkage priors (PRSCSx) applied on summary statistics from the largest multi-ancestry genome-wide meta-analysis for CHD to date, including 1.1 million participants from 5 continental populations. Following training and optimization of PRSCHD in the Million Veteran Program, we evaluated predictive performance of the best performing PRSCHD in 176,988 individuals across 9 cohorts of diverse genetic ancestry. Results Multi-ancestry PRSP+T outperformed ancestry specific PRSP+T across a range of tuning values. In training stage, for all ancestry groups, PRSCSx performed better than PRSP+T and multi-ancestry PRS outperformed ancestry-specific PRS. In independent validation cohorts, the selected multi-ancestry PRSP+T demonstrated the strongest association with CHD in individuals of South Asian (SAS) and European (EUR) ancestry (OR per 1SD[95% CI]; 2.75[2.41-3.14], 1.65[1.59-1.72]), followed by East Asian (EAS) (1.56[1.50-1.61]), Hispanic/Latino (HIS) (1.38[1.24-1.54]), and weakest in African (AFR) ancestry (1.16[1.11-1.21]). The selected multi-ancestry PRSCSx showed stronger associacion with CHD in comparison within each ancestry group where the association was strongest in SAS (2.67[2.38-3.00]) and EUR (1.65[1.59-1.71]), progressively decreasing in EAS (1.59[1.54-1.64]), HIS (1.51[1.35-1.69]), and lowest in AFR (1.20[1.15-1.26]). Conclusions Utilizing diverse summary statistics from a large multi-ancestry genome-wide meta-analysis led to improved performance of PRSCHD in most ancestry groups compared to single-ancestry methods. Improvement of predictive performance was limited, specifically in AFR and HIS, despite use of one of the largest and most diverse set of training and validation cohorts to date. This highlights the need for larger GWAS datasets of AFR and HIS individuals to enhance performance of PRSCHD.
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Affiliation(s)
- Johanna L. Smith
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, USA
| | - Catherine Tcheandjieu
- Department of Epidemiology and Biostatistics, University of California San Francisco, San Francisco, CA, USA
- Gladstone Institute of Data Science and Biotechnology, Gladstone Institutes, San Francisco, CA, USA
- VA Palo Alto Health Care System, Palo Alto, CA, USA
- Department of Medicine, Division of Cardiovascular Medicine, Stanford University School of Medicine, Stanford, CA, USA
| | - Ozan Dikilitas
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, USA
| | - Kruthika lyer
- Stanford University School of Medicine, Palo Alto, CA, USA
| | - Kazuo Miyazawa
- Riken Ctr. for Integrative Medical Sciences, Yokohama City, Japan
| | - Austin Hilliard
- VA Palo Alto Health Care System, Palo Alto, CA, USA
- Stanford University School of Medicine, Palo Alto, CA, USA
| | - Julie Lynch
- Salt Lake City VA Met CTR., Salt Lake City, UT, USA
| | - Jerome I. Rotter
- Institute for Translational Genomics and Population Sciences, Department of Pediatrics, Lundquist Institute at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Yii-Der Ida Chen
- Institute for Translational Genomics and Population Sciences, Department of Pediatrics, Lundquist Institute at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Wayne Huey-Herng Sheu
- Institute of Molecular and Genomic Medicine, National Health Research Institutes, Taiwan
- Division of Endocrinology and Metabolism, Department of Internal Medicine, Taipei Veterans General Hospital, Taipei, Taiwan
- Division of Endocrinology and Metabolism, Department of Internal Medicine, Taichung Veterans General Hospital, Taichung, Taiwan
| | - Kyong-Mi Chang
- Corporal Michael J Crescenz VA Medical Ctr. Philadelphia, PA, USA
| | | | - Phil Tsao
- VA Palo Alto Health Care System, Palo Alto, CA, USA
- Stanford University, Stanford, CA, USA
| | - Kaoru Ito
- Riken Ctr. for Integrative Medical Sciences, Yokohama City, Japan
| | - Matthew Kosel
- Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA
| | - Shoa L. Clarke
- VA Palo Alto Health Care System, Palo Alto, CA, USA
- Stanford University, Stanford, CA, USA
| | - Daniel J. Schaid
- Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA
| | | | - Iftikhar J. Kullo
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, USA
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Lennon NJ, Kottyan LC, Kachulis C, Abul-Husn N, Arias J, Belbin G, Below JE, Berndt S, Chung W, Cimino JJ, Clayton EW, Connolly JJ, Crosslin D, Dikilitas O, Velez Edwards DR, Feng Q, Fisher M, Freimuth R, Ge T, Glessner JT, Gordon A, Guiducci C, Hakonarson H, Harden M, Harr M, Hirschhorn J, Hoggart C, Hsu L, Irvin R, Jarvik GP, Karlson EW, Khan A, Khera A, Kiryluk K, Kullo I, Larkin K, Limdi N, Linder JE, Loos R, Luo Y, Malolepsza E, Manolio T, Martin LJ, McCarthy L, Meigs JB, Mersha TB, Mosley J, Namjou B, Pai N, Pesce LL, Peters U, Peterson J, Prows CA, Puckelwartz MJ, Rehm H, Roden D, Rosenthal EA, Rowley R, Sawicki KT, Schaid D, Schmidlen T, Smit R, Smith J, Smoller JW, Thomas M, Tiwari H, Toledo D, Vaitinadin NS, Veenstra D, Walunas T, Wang Z, Wei WQ, Weng C, Wiesner G, Xianyong Y, Kenny E. Selection, optimization, and validation of ten chronic disease polygenic risk scores for clinical implementation in diverse populations. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.05.25.23290535. [PMID: 37333246 PMCID: PMC10275001 DOI: 10.1101/2023.05.25.23290535] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/20/2023]
Abstract
Polygenic risk scores (PRS) have improved in predictive performance supporting their use in clinical practice. Reduced predictive performance of PRS in diverse populations can exacerbate existing health disparities. The NHGRI-funded eMERGE Network is returning a PRS-based genome-informed risk assessment to 25,000 diverse adults and children. We assessed PRS performance, medical actionability, and potential clinical utility for 23 conditions. Standardized metrics were considered in the selection process with additional consideration given to strength of evidence in African and Hispanic populations. Ten conditions were selected with a range of high-risk thresholds: atrial fibrillation, breast cancer, chronic kidney disease, coronary heart disease, hypercholesterolemia, prostate cancer, asthma, type 1 diabetes, obesity, and type 2 diabetes. We developed a pipeline for clinical PRS implementation, used genetic ancestry to calibrate PRS mean and variance, created a framework for regulatory compliance, and developed a PRS clinical report. eMERGE's experience informs the infrastructure needed to implement PRS-based implementation in diverse clinical settings.
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Affiliation(s)
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | - Li Hsu
- Fred Hutchinson Cancer Center and University of Washington
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | - Ulrike Peters
- Fred Hutchinson Cancer Center and University of Washington
| | | | | | | | | | - Dan Roden
- Vanderbilt University Medical Center
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Smith JL, Schaid DJ, Kullo IJ. Implementing Reporting Standards for Polygenic Risk Scores for Atherosclerotic Cardiovascular Disease. Curr Atheroscler Rep 2023; 25:323-330. [PMID: 37223852 PMCID: PMC10495216 DOI: 10.1007/s11883-023-01104-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/13/2023] [Indexed: 05/25/2023]
Abstract
PURPOSE OF REVIEW There is considerable interest in using polygenic risk scores (PRSs) for assessing risk of atherosclerotic cardiovascular disease (ASCVD). A barrier to the clinical use of PRSs is heterogeneity in how PRS studies are reported. In this review, we summarize approaches to establish a uniform reporting framework for PRSs for coronary heart disease (CHD), the most common form of ASCVD. RECENT FINDINGS Reporting standards for PRSs need to be contextualized for disease specific applications. In addition to metrics of predictive performance, reporting standards for PRSs for CHD should include how cases/control were ascertained, degree of adjustment for conventional CHD risk factors, portability to diverse genetic ancestry groups and admixed individuals, and quality control measures for clinical deployment. Such a framework will enable PRSs to be optimized and benchmarked for clinical use.
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Affiliation(s)
- Johanna L Smith
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, USA
| | - Daniel J Schaid
- Department of Quantitative Health Sciences, Rochester, MN, USA
| | - Iftikhar J Kullo
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, USA.
- Gonda Vascular Center, Rochester, MN, USA.
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