Relatively independent and complementary roles of family history and polygenic risk score in age at onset and incident cases of 12 common diseases.

Soc Sci Med

Department of Biostatistics, School of Public Health, Xuzhou Medical University, Xuzhou, Jiangsu, 221004, China; Jiangsu Engineering Research Center of Biological Data Mining and Healthcare Transformation, Xuzhou Medical University, Xuzhou, Jiangsu, 221004, China. Electronic address:

Published: March 2025

Few studies have systematically compared the overlap and complementarity of family history (FH) and polygenic risk score (PRS) in terms of disease risk. We here investigated the impacts of FH and PRS on the risk of incident diseases or age at disease onset, as well as their clinical value in risk prediction. We analyzed 12 diseases in the prospective cohort study of UK Biobank (N = 461,220). First, restricted mean survival time analysis was performed to evaluate the influences of FH and PRS on age at onset. Then, Cox proportional hazards model was employed to estimate the effects of FH and PRS on the incident risk. Finally, prediction models were constructed to examine the clinical value of FH and PRS in the incident disease risk. Compared to negative FH, positive FH led to an earlier onset, with an average of 2.29 years earlier between the top and bottom 2.5% PRSs and high blood pressure showing the greatest difference of 6.01 years earlier. Both FH and PRS were related to higher incident risk; but they only exhibited weak interactions on high blood pressure and Alzheimer's disease/dementia, and provided relatively independent and partially complementary information on disease susceptibility, with PRS explaining 7.0% of the FH effect but FH accounting for only 1.1% of the PRS effect for incident cases. Further, FH and PRS showed additional predictive value in risk evaluation, with breast cancer showing the greatest improvement (31.3%). FH and PRS significantly affect a variety of diseases, and they are not interchangeable measures of genetic susceptibility, but instead offer largely independent and partially complementary information. Incorporating FH, PRS, and clinical risk factors simultaneously leads to the greatest predictive value for disease risk assessment.

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http://dx.doi.org/10.1016/j.socscimed.2025.117942DOI Listing

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