A unified framework for estimating country-specific cumulative incidence for 18 diseases stratified by polygenic risk

Bradley Jermy, Kristi Läll, Brooke N. Wolford, Ying Wang, Kristina Zguro, Yipeng Cheng, Masahiro Kanai, Stavroula Kanoni, Zhiyu Yang, Tuomo Hartonen, Remo Monti, Julian Wanner, Omar Youssef, Christoph Lippert, David Heel, Yukinori Okada, Daniel L. McCartney, Caroline Hayward, Riccardo E. Marioni, Simone Furini, Alessandra Renieri, Alicia R. Martin, Benjamin M. Neale, Kristian Hveem, Reedik Mägi, Aarno Palotie, Henrike Heyne, Nina Mars, Andrea Ganna () and Samuli Ripatti ()
Additional contact information
Bradley Jermy: University of Helsinki
Kristi Läll: University of Tartu
Brooke N. Wolford: Norwegian University of Science and Technology
Ying Wang: Massachusetts General Hospital
Kristina Zguro: University of Siena
Yipeng Cheng: University of Edinburgh
Masahiro Kanai: Massachusetts General Hospital
Stavroula Kanoni: Queen Mary University of London
Zhiyu Yang: University of Helsinki
Tuomo Hartonen: University of Helsinki
Remo Monti: University of Potsdam
Julian Wanner: University of Helsinki
Omar Youssef: Hospital District of Helsinki and Uusimaa (HUS)
Christoph Lippert: University of Potsdam
David Heel: Queen Mary University of London
Yukinori Okada: the University of Tokyo
Daniel L. McCartney: University of Edinburgh
Caroline Hayward: University of Edinburgh
Riccardo E. Marioni: University of Edinburgh
Simone Furini: University of Siena
Alessandra Renieri: University of Siena
Alicia R. Martin: Massachusetts General Hospital
Benjamin M. Neale: Massachusetts General Hospital
Kristian Hveem: Norwegian University of Science and Technology
Reedik Mägi: University of Tartu
Aarno Palotie: University of Helsinki
Henrike Heyne: University of Helsinki
Nina Mars: University of Helsinki
Andrea Ganna: University of Helsinki
Samuli Ripatti: University of Helsinki

Nature Communications, 2024, vol. 15, issue 1, 1-14

Abstract: Abstract Polygenic scores (PGSs) offer the ability to predict genetic risk for complex diseases across the life course; a key benefit over short-term prediction models. To produce risk estimates relevant to clinical and public health decision-making, it is important to account for varying effects due to age and sex. Here, we develop a novel framework to estimate country-, age-, and sex-specific estimates of cumulative incidence stratified by PGS for 18 high-burden diseases. We integrate PGS associations from seven studies in four countries (N = 1,197,129) with disease incidences from the Global Burden of Disease. PGS has a significant sex-specific effect for asthma, hip osteoarthritis, gout, coronary heart disease and type 2 diabetes (T2D), with all but T2D exhibiting a larger effect in men. PGS has a larger effect in younger individuals for 13 diseases, with effects decreasing linearly with age. We show for breast cancer that, relative to individuals in the bottom 20% of polygenic risk, the top 5% attain an absolute risk for screening eligibility 16.3 years earlier. Our framework increases the generalizability of results from biobank studies and the accuracy of absolute risk estimates by appropriately accounting for age- and sex-specific PGS effects. Our results highlight the potential of PGS as a screening tool which may assist in the early prevention of common diseases.

Date: 2024
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DOI: 10.1038/s41467-024-48938-2

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