Clinical history shapes the predictive value of polygenic risk
- 1Institute for Genomic Medicine, Nationwide Children's Hospital, Columbus, OH, USA
Abstract
For most diseases, whether PRS improves prediction beyond clinical data remains unknown, as existing evidence is concentrated in a handful of conditions with established risk models. Using 900,000 participants from UK Biobank and FinnGen, we developed and validated models for predicting 150 diseases and all-cause mortality. Genetic information provided the largest predictive gains in metabolic, cardiovascular, autoimmune, and neurological disease, but added little for genitourinary and respiratory conditions. All significant PRS-clinical risk interactions were negative, with predictive power gains from genetic data larger at lower clinical risk, suggesting that PRS may reveal susceptibility not yet apparent clinically. For example, in atherosclerotic cardiovascular disease, PRS expanded the clinically identified high-risk subgroup by 40%, and this reclassified subgroup had 4.9-fold higher incidence during the follow-up than those remaining low-risk. Our interpretable clinical-genetic models generalized across three biobanks and are publicly available through PrognosIQ.
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BibTeX
@misc{usoltsev2026clinical,
title = {{Clinical history shapes the predictive value of polygenic risk}},
author = {D. Usoltsev and I. Molotkov and N. Kolosov and M. P. Reeve and A. Ukhatov and FinnGen and O. Rotar and A. Kostareva and A. Konradi and S. Ripatti and A. Palotie and M. J. Daly and M. Artomov},
howpublished = {medRxiv},
year = {2026},
month = sep,
doi = {10.64898/2026.09.25.26364029},
url = {https://www.medrxiv.org/content/10.64898/2026.09.25.26364029v1},
note = {CC BY-NC-ND},
}
RIS
TY - UNPB
TI - Clinical history shapes the predictive value of polygenic risk
AU - D. Usoltsev
AU - I. Molotkov
AU - N. Kolosov
AU - M. P. Reeve
AU - A. Ukhatov
AU - FinnGen
AU - O. Rotar
AU - A. Kostareva
AU - A. Konradi
AU - S. Ripatti
AU - A. Palotie
AU - M. J. Daly
AU - M. Artomov
PY - 2026
DA - 2026/09/25
PB - medRxiv
DO - 10.64898/2026.09.25.26364029
UR - https://www.medrxiv.org/content/10.64898/2026.09.25.26364029v1
AB - For most diseases, whether PRS improves prediction beyond clinical data remains unknown, as existing evidence is concentrated in a handful of conditions with established risk models. Using 900,000 participants from UK Biobank and FinnGen, we developed and validated models for predicting 150 diseases and all-cause mortality. Genetic information provided the largest predictive gains in metabolic, cardiovascular, autoimmune, and neurological disease, but added little for genitourinary and respiratory conditions. All significant PRS-clinical risk interactions were negative, with predictive power gains from genetic data larger at lower clinical risk, suggesting that PRS may reveal susceptibility not yet apparent clinically. For example, in atherosclerotic cardiovascular disease, PRS expanded the clinically identified high-risk subgroup by 40%, and this reclassified subgroup had 4.9-fold higher incidence during the follow-up than those remaining low-risk. Our interpretable clinical-genetic models generalized across three biobanks and are publicly available through PrognosIQ.
ER -