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

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.

Fig. 1. Model selection for each phenotype.

a Data are presented as the fixed-effects meta-analysis of the log hazard ratios per standard deviation of PGS stratified by sex. b Meta-analysed hazard ratios per standard deviation stratified by age. The asterisk indicates a significant interaction between PGS and sex estimated by a two-sided Wald test after Bonferroni correction for multiple testing of 18 phenotypes (p value < 2.8 × 10⁻³). The exact p values are in Supplementary Table 1. Case and control sample sizes for each phenotype in each biobank are in Supplementary Data 1.

Fig. 2. Meta-analyzed hazard ratios stratified by age and sex.

Data are presented as the fixed-effects meta-analysis of the log hazard ratios per standard deviation of PGS stratified by sex, age quartile, and PGS strata for T2D, CHD, prostate cancer, and breast cancer. Case and control sample sizes for each phenotype in each biobank are in Supplementary Data 1.

Fig. 3. Country and sex-specific cumulative incidence estimates.

Bootstrapped 95% confidence intervals reflect the uncertainty of the cumulative incidence estimates for the top, median, and bottom of the PGS distribution for a prostate cancer and b CHD.

Fig. 4. Sex-specific cumulative incidence estimates for T2D and breast cancer in Finland.

The red dashed line in each figure represents a country-specific clinically defined cumulative incidence risk threshold for screening. Bootstrapped 95% confidence intervals reflect the uncertainty of the cumulative incidence estimates for the top, median, and bottom of the PGS distribution for a T2D and b breast cancer.