|
1
|
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.
Collapse
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
| |
Collapse
|
|
2
|
Zirpoli GR, Pfeiffer RM, Bertrand KA, Huo D, Lunetta KL, Palmer JR. Addition of polygenic risk score to a risk calculator for prediction of breast cancer in US Black women. Breast Cancer Res 2024; 26:2. [PMID: 38167144 PMCID: PMC10763003 DOI: 10.1186/s13058-023-01748-8] [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: 08/22/2023] [Accepted: 11/30/2023] [Indexed: 01/05/2024] Open
Abstract
BACKGROUND Previous work in European ancestry populations has shown that adding a polygenic risk score (PRS) to breast cancer risk prediction models based on epidemiologic factors results in better discriminatory performance as measured by the AUC (area under the curve). Following publication of the first PRS to perform well in women of African ancestry (AA-PRS), we conducted an external validation of the AA-PRS and then evaluated the addition of the AA-PRS to a risk calculator for incident breast cancer in Black women based on epidemiologic factors (BWHS model). METHODS Data from the Black Women's Health Study, an ongoing prospective cohort study of 59,000 US Black women followed by biennial questionnaire since 1995, were used to calculate AUCs and 95% confidence intervals (CIs) for discriminatory accuracy of the BWHS model, the AA-PRS alone, and a new model that combined them. Analyses were based on data from 922 women with invasive breast cancer and 1844 age-matched controls. RESULTS AUCs were 0.577 (95% CI 0.556-0.598) for the BWHS model and 0.584 (95% CI 0.563-0.605) for the AA-PRS. For a model that combined estimates from the questionnaire-based BWHS model with the PRS, the AUC increased to 0.623 (95% CI 0.603-0.644). CONCLUSIONS This combined model represents a step forward for personalized breast cancer preventive care for US Black women, as its performance metrics are similar to those from models in other populations. Use of this new model may mitigate exacerbation of breast cancer disparities if and when it becomes feasible to include a PRS in routine health care decision-making.
Collapse
Affiliation(s)
- Gary R Zirpoli
- Slone Epidemiology Center at Boston University, Boston, MA, USA
| | - Ruth M Pfeiffer
- Biostatistics Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, USA.
- Division of Cancer Epidemiology and Biostatistics, National Cancer Institute, Bethesda, USA.
| | - Kimberly A Bertrand
- Slone Epidemiology Center at Boston University, Boston, MA, USA
- Department of Medicine, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA
| | - Dezheng Huo
- Department of Public Health Sciences, The University of Chicago, Chicago, IL, USA
- Center for Clinical Cancer Genetics & Global Health, The University of Chicago, Chicago, IL, USA
| | - Kathryn L Lunetta
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
| | - Julie R Palmer
- Slone Epidemiology Center at Boston University, Boston, MA, USA.
- Department of Medicine, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA.
| |
Collapse
|