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      Performance of polygenic risk scores in screening, prediction, and risk stratification: secondary analysis of data in the Polygenic Score Catalog

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          Abstract

          Objective

          To clarify the performance of polygenic risk scores in population screening, individual risk prediction, and population risk stratification.

          Design

          Secondary analysis of data in the Polygenic Score Catalog.

          Setting

          Polygenic Score Catalog, April 2022. Secondary analysis of 3915 performance metric estimates for 926 polygenic risk scores for 310 diseases to generate estimates of performance in population screening, individual risk, and population risk stratification.

          Participants

          Individuals contributing to the published studies in the Polygenic Score Catalog.

          Main outcome measures

          Detection rate for a 5% false positive rate (DR 5) and the population odds of becoming affected given a positive result; individual odds of becoming affected for a person with a particular polygenic score; and odds of becoming affected for groups of individuals in different portions of a polygenic risk score distribution. Coronary artery disease and breast cancer were used as illustrative examples.

          Results

          For performance in population screening, median DR 5 for all polygenic risk scores and all diseases studied was 11% (interquartile range 8-18%). Median DR 5 was 12% (9-19%) for polygenic risk scores for coronary artery disease and 10% (9-12%) for breast cancer. The population odds of becoming affected given a positive results were 1:8 for coronary artery disease and 1:21 for breast cancer, with background 10 year odds of 1:19 and 1:41, respectively, which are typical for these diseases at age 50. For individual risk prediction, the corresponding 10 year odds of becoming affected for individuals aged 50 with a polygenic risk score at the 2.5th, 25th, 75th, and 97.5th centiles were 1:54, 1:29, 1:15, and 1:8 for coronary artery disease and 1:91, 1:56, 1:34, and 1:21 for breast cancer. In terms of population risk stratification, at age 50, the risk of coronary artery disease was divided into five groups, with 10 year odds of 1:41 and 1:11 for the lowest and highest quintile groups, respectively. The 10 year odds was 1:7 for the upper 2.5% of the polygenic risk score distribution for coronary artery disease, a group that contributed 7% of cases. The corresponding estimates for breast cancer were 1:72 and 1:26 for the lowest and highest quintile groups, and 1:19 for the upper 2.5% of the distribution, which contributed 6% of cases.

          Conclusion

          Polygenic risk scores performed poorly in population screening, individual risk prediction, and population risk stratification. Strong claims about the effect of polygenic risk scores on healthcare seem to be disproportionate to their performance.

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          Most cited references34

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          The personal and clinical utility of polygenic risk scores

          Initial expectations for genome-wide association studies were high, as such studies promised to rapidly transform personalized medicine with individualized disease risk predictions, prevention strategies and treatments. Early findings, however, revealed a more complex genetic architecture than was anticipated for most common diseases - complexity that seemed to limit the immediate utility of these findings. As a result, the practice of utilizing the DNA of an individual to predict disease has been judged to provide little to no useful information. Nevertheless, recent efforts have begun to demonstrate the utility of polygenic risk profiling to identify groups of individuals who could benefit from the knowledge of their probabilistic susceptibility to disease. In this context, we review the evidence supporting the personal and clinical utility of polygenic risk profiling.
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            2019 ACC/AHA Guideline on the Primary Prevention of Cardiovascular Disease: Executive Summary

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              Sick individuals and sick populations.

              Aetiology confronts two distinct issues: the determinants of individual cases, and the determinants of incidence rate. If exposure to a necessary agent is homogeneous within a population, then case/control and cohort methods will fail to detect it: they will only identify markers of susceptibility. The corresponding strategies in control are the 'high-risk' approach, which seeks to protect susceptible individuals, and the population approach, which seeks to control the causes of incidence. The two approaches are not usually in competition, but the prior concern should always be to discover and control the causes of incidence.
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                Author and article information

                Journal
                BMJ Med
                BMJ Med
                bmjmed
                bmjmed
                BMJ Medicine
                BMJ Publishing Group (BMA House, Tavistock Square, London, WC1H 9JR )
                2754-0413
                2023
                17 October 2023
                : 2
                : 1
                : e000554
                Affiliations
                [1 ]departmentInstitute of Cardiovascular Science , Ringgold_4919University College London , London, UK
                [2 ]departmentBritish Heart Foundation Research Accelerator , Ringgold_4919University College London , London, UK
                [3 ]departmentNational Institute of Health Research Biomedical Research Centre , Ringgold_4919University College London Hospitals , London, UK
                [4 ]Ringgold_591739Health Data Research UK , London, UK
                [5 ]Ringgold_8124University Medical Centre Utrecht , Utrecht, Netherlands
                [6 ]departmentDepartment of Pharmacology and Therapeutics , Ringgold_4591University of Liverpool , Liverpool, UK
                [7 ]departmentPrecision Healthcare University Research Institute , Queen Mary University of London , London, UK
                [8 ]departmentComputational Medicine , Berlin Institute of Health at Charite Universitatzmedizin , Berlin, Germany
                [9 ]departmentMRC Epidemiology Unit , University of Cambridge , Cambridge, UK
                [10 ]departmentInstitute of Health Informatics , Ringgold_4919University College London , London, UK
                [11 ]departmentPopulation Health Research Institute , Ringgold_4968St George's University of London , London, UK
                Author notes
                [Correspondence to ] Aroon D Hingorani, Institute of Cardiovascular Science, University College London, London WC1E 6BT, UK; a.hingorani@ 123456ucl.ac.uk
                Author information
                http://orcid.org/0000-0002-9781-9762
                Article
                bmjmed-2023-000554
                10.1136/bmjmed-2023-000554
                10582890
                37859783
                3de6c2ea-89d0-4edf-ba2e-8c8867e9663c
                © Author(s) (or their employer(s)) 2023. Re-use permitted under CC BY. Published by BMJ.

                This is an open access article distributed in accordance with the Creative Commons Attribution 4.0 Unported (CC BY 4.0) license, which permits others to copy, redistribute, remix, transform and build upon this work for any purpose, provided the original work is properly cited, a link to the licence is given, and indication of whether changes were made. See:  https://creativecommons.org/licenses/by/4.0/.

                History
                : 08 March 2023
                : 31 August 2023
                Funding
                Funded by: UKRI/NIHR funded Multimorbidity Mechanism and Therapeutics Research Collaborative;
                Award ID: MR/V033867/1
                Funded by: FundRef http://dx.doi.org/10.13039/501100000274, British Heart Foundation;
                Award ID: AA/18/6/34223
                Award ID: FS/17/70/33482
                Funded by: UCL NIHR Biomedical Research Centre;
                Award ID: NIHR203328
                Funded by: NIHR Senior Investigators;
                Award ID: NF-SI-0616-10066
                Award ID: NIHR202383
                Categories
                Original Research
                1506
                Custom metadata
                unlocked

                public health,preventive medicine
                public health, preventive medicine

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