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

被引:15
|
作者
Hingorani, Aroon D. [1 ,2 ,3 ,4 ]
Gratton, Jasmine [1 ,2 ]
Finan, Chris [1 ,2 ,3 ,4 ]
Schmidt, A. Floriaan [1 ,3 ,4 ,5 ]
Patel, Riyaz [1 ,2 ,3 ,4 ]
Sofat, Reecha [4 ,6 ]
Kuan, Valerie [1 ,2 ,3 ]
Langenberg, Claudia [7 ,8 ,9 ]
Hemingway, Harry [2 ,3 ,4 ,10 ]
Morris, Joan K. [11 ]
Wald, Nicholas J. [10 ,11 ]
机构
[1] UCL, Inst Cardiovasc Sci, London WC1E 6BT, England
[2] UCL, British Heart Fdn Res Accelerator, London, England
[3] Univ Coll London Hosp, Natl Inst Hlth Res, Biomed Res Ctr, London, England
[4] Hlth Data Res UK, London, England
[5] Univ Med Ctr Utrecht, Utrecht, Netherlands
[6] Univ Liverpool, Dept Pharmacol & Therapeut, Liverpool, Merseyside, England
[7] Queen Mary Univ London, Precis Healthcare Univ Res Inst, London, England
[8] Charite, Computat Med, Berlin Inst Hlth, Berlin, Germany
[9] Univ Cambridge, MRC Epidemiol Unit, Cambridge, England
[10] UCL, Inst Hlth Informat, London, England
[11] St Georges Univ London, Populat Hlth Res Inst, London, England
来源
BMJ MEDICINE | 2023年 / 2卷 / 01期
基金
英国科研创新办公室;
关键词
public health; preventive medicine;
D O I
10.1136/bmjmed-2023-000554
中图分类号
R5 [内科学];
学科分类号
1002 ; 100201 ;
摘要
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 (DR5) 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 DR5 for all polygenic risk scores and all diseases studied was 11% (interquartile range 8-18%). Median DR5 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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页数:12
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