To a varying extent, most human characteristics are heritable. The genetic component of physical activity behavior and many related physiological phenotypes has been established as polygenic. This means that variation in these phenotypes is explained by the contributions of hundreds or thousands of genetic variants, each of which has a small effect size. Advances in high-throughput genotyping and statistical genetics have made it possible to estimate the contribution of genetic variation to polygenic traits using measured genotypes. Previously, genetic effects were estimated indirectly from family and twin studies. The measured genetic variation can be summarized into a single score based on variation in multiple genetic loci and their associated effect size weights. This score, usually called a polygenic risk score or a polygenic score, describes an individual’s genetic liability to a trait or disease. Polygenic scoring has recently become a standard method in evaluating inherited risk for common diseases, but these scores have rarely been used in exercise science.
In the July 2020 issue of Medicine & Science in Sports & Exercise® (MSSE), we published polygenic scores for both self-reported and monitored physical activity. We showed their predictive value in independent cohorts and several physical activity phenotypes. The overall predictive value of these scores, although statistically significant, was rather low, thus limiting their use to research purposes only at this time. Polygenic scores for physical activity describe an individual’s genetic liability to be physically active.
Our recent study, published in the February 2022 issue of MSSE, investigated associations between a polygenic score for monitored physical activity and cardiometabolic diseases. In this study, we used the novel Finnish biobank study FinnGen, which will soon combine the genome information of 500,000 individuals with digital health care data. At the time of our analyses, data from over 200,000 individuals were available. We simultaneously investigated dozens of validated clinical endpoints, including mortality and prescription medication use. We found that genetically less physically active persons are at a higher risk of being obese and developing several cardiometabolic diseases. In addition, genetically active persons tend to live longer, and, perhaps due to longer lifespan, it was observed that they are at higher risk of developing Alzheimer’s disease. Based on our results, it appears that the same genes affect both physical activity behavior and cardiometabolic disease risk. This result may partially explain the frequently reported associations between higher levels of physical activity and lower risk of common diseases.
Polygenic scores provide new tools for genetic studies in exercise science. The scores may be used to study gene-environment interactions and individual responses to exercise interventions with respect to disease prevention. They can also be used to control for underlying genetics in cohort studies. The scoring methods and standards are constantly evolving and currently have substantial practical limitations. The construction of polygenic scores requires large datasets and high-quality phenotypes and genotypes. To achieve powerful scores that are useful also outside research settings, exercise scientists should work together to standardize main measurements. These collaborations with pooled data would provide large-cohort datasets representing different genetic ancestries for future investigations.
Elina Sillanpää is an Academy of Finland research fellow working in the faculty of sport and health sciences at the University of Jyväskylä, Finland. She has a Ph.D. in sport sciences and a background in randomized controlled endurance and strength training interventions. Dr. Sillanpää leads a research group that focuses on genetic and molecular studies of physical activity and exercise in relation to biological aging, functional disabilities and ageing-associated diseases, using advanced statistical methods and novel bioinformatics to analyze gene-environment interactions. Dr. Sillanpää is an active member of large consortiums, including the Interplay of Genes and Environment Across Multiple Studies (IGEMS) and the Genomics and Biology of Physical Activity Consortium (GenBioPAC). Dr. Sillanpää is a member of ACSM.
Viewpoints presented in SMB commentaries reflect opinions of the authors and do not necessarily represent ACSM positions or policies. Active Voice authors who have received financial or other considerations from a commercial entity associated with their topic must disclose such relationships at the time they accept an invitation to write for SMB.
