A team of researchers from the Penn State College of Medicine conducted a study to investigate the role of genetics and environmental factors in disease risk. They found that in some cases, lifestyle and environmental factors play a larger role in disease risk than previously believed, highlighting the potential for more opportunities to reduce disease risk through modifying environmental factors. By accurately understanding the contributions of genetics and the environment to disease risk, better prediction of disease risk and more effective interventions can be developed, particularly in the context of precision medicine.
The researchers developed a spatial mixed linear effect (SMILE) model that incorporates genetics and geolocation data to assess disease risk. Geolocation data, which provides information about a person’s approximate geographical location, was used as a measure for community-level environmental risk factors. By incorporating data from more than 50 million individuals in the United States, the researchers were able to filter out information for over 257,000 nuclear families and compile disease outcomes for 1,083 diseases. This data was augmented with environmental data, including climate and sociodemographic data, as well as levels of particulate matter 2.5 (PM2.5) and nitrogen dioxide (NO2) to refine estimates of disease risk contributors.
The analysis conducted by the team led to more accurate estimates of the contributions to disease risk, demonstrating that environmental factors may play a larger role than previously thought. For example, the estimated genetic contribution to Type 2 diabetes risk decreased from 37.7% to 28.4% when environmental factors were considered. Similarly, the estimated genetic contribution to obesity risk decreased from 53.1% to 46.3% when adjusted for environmental factors. This recalibration of disease risk factors offers hope to individuals with a family history of certain diseases, as it highlights the potential for risk reduction through lifestyle modifications.
The researchers also investigated the causal relationship between two specific pollutants, PM2.5 and NO2, and disease risks. They found that these pollutants have different and distinct causal relationships with health conditions. For example, NO2 was shown to directly cause conditions like high cholesterol, irritable bowel syndrome, and both Type 1 and Type 2 diabetes, whereas PM2.5 may have a more direct causal effect on lung function and sleep disorders. By understanding these specific relationships, interventions can be targeted to address the specific environmental factors contributing to disease risk.
This model developed by the research team allows for a more comprehensive analysis of why certain diseases may be more prevalent in certain geographic locations. By combining genetics and geolocation data, researchers can disentangle the shared disease risks among family members and more accurately reflect the genetic heritability of diseases. This approach provides a promising avenue for future research into disease risk prediction and intervention strategies. The study was supported in part by the National Institutes of Health and the Penn State College of Medicine’s artificial intelligence and biomedical informatics pilot funding program, highlighting the interdisciplinary nature of this research.
