Lifetime risk for disease follows distinct trajectories

New research shows that the genetic risk for many diseases is affected by age and sex: a framework developed by researchers at FIMM provides an estimate of a person’s lifetime risk of disease based on age, sex, country, and polygenic burden.

The INTERVENE consortium, which is a Horizon 2020 project led by researchers at the Institute for Molecular Medicine Finland (FIMM) at the University of Helsinki, has developed a new framework for estimating cumulative disease incidences based on polygenic  scores (PGS). The framework, which considers both country specific differences and age and sex in making risk estimates based on PGS, was published in Nature Communications on June 12th, 2024 

Polygenic (risk) scores combine the many small effects of alleles across the  genome to produce an estimate of the risk of a disease or disease-related trait for an individual. PGS can be used to  provide information to guide risk prediction models, which can then be used for counseling individual patients in the clinic on their risk for developing a given disease as well as in a societal setting as part of public health decision making. 

To produce risk estimates that are relevant for these purposes variation in effects due to common risk factors such as age and sex need to be considered. Moreover, disease incidence may differ between countries, which means that understanding how PGS can be generalized across countries and health systems, is important when implementing them as a tool to provide lifetime risk estimates.

INTERVENE is an international and interdisciplinary consortium, which integrates data science and human genetics to develop tools for disease prevention, diagnosis, and personalised treatments. INTERVENE uses AI-facilitated analyses of complex medical data derived from biobanks to develop genetic risk scores. Its goal is to produce validated risk scores with predictive value for complex and rare diseases, which are applicable for disease screening, and can be understood by clinicians and citizens.

The consortium has now published its flagship paper describing a new framework for estimating cumulative disease incidences over the life course. The framework considers both country-specific differences in disease incidence as well as how risk estimation of PGS varies by both age and sex.  

Integrating PGS associations in almost 1,2 million individuals from seven studies in four countries with disease incidences from the Global Burden of Disease demonstrated that PGSs had a significant sex-specific effect on asthma, hip osteoarthritis, gout, coronary heart disease and type 2 diabetes with all but type 2 diabetes exhibiting a larger effect in men than women.

The effect of PGS was larger in younger individuals for 13 of 18 diseases studied with the effects decreasing linearly with age. To put the results into context the researchers demonstrate how PGS stratifies individuals and can impact risk-based screening practices: For type 2 diabetes, men and women in the top 1% reached the risk threshold at ages 24.8 and 22.3 respectively. Individuals in the bottom 1% of PGS did not reach the risk threshold by age 80. This highlights the potential of PGS as a screening tool for common diseases.

The data used came from biobank studies including FinnGen, UK Biobank, Genomics England, Trøndelag Health Study, Generation Scotland, Estonian Biobank and Mass General Brigham Biobank. By appropriately accounting for age and sex-specific PGS effects, the extendable framework increases the generalizability of results from biobank studies and the accuracy of absolute risk estimates. 

Professor Samuli Ripatti discusses the impact of the research: "Our study highlights the power of combining 1.2 million biobanked samples across Europe. This allowed us for the first time to show that in many diseases the effects of polygenic risk scores are much higher in young individuals compared to older participants and some effects are also different for males and females. These observations may have implications when implementing the risk scores to clinical practice."

Original publication:
Jermy, B., Läll, K., Wolford, B.N. et al. A unified framework for estimating country-specific cumulative incidence for 18 diseases stratified by polygenic risk. Nat Commun 15, 5007 (2024).