Unraveling the Genetic and Environmental Factors of Disease Risk: A Breakthrough Study from Penn State College of Medicine

The study conducted by a team led by Penn State College of Medicine researchers sheds light on the complex interplay between genetics and environmental factors in disease risk. Published in Nature Communications, the research utilized a large, nationally representative sample to better understand the contribution of genetics and environmental factors to disease risk.

Lead researcher Bibo Jiang, assistant professor of public health sciences at Penn State College of Medicine, emphasized the importance of accurately understanding the role of genetics and environment in disease development. By doing so, researchers can improve disease risk prediction and design more effective interventions, especially in the era of precision medicine.

The study introduced a spatial mixed linear effect (SMILE) model that incorporates genetics and geolocation data to assess disease risk. Geolocation data, representing a person’s approximate geographical location, served as a proxy for community-level environmental risk factors. By including environmental data such as climate, sociodemographic factors, and air pollution levels like particulate matter 2.5 (PM2.5) and nitrogen dioxide (NO2), the researchers were able to refine estimates of disease risk contributors.

One significant finding of the study was the recalibration of genetic contributions to disease risk. 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%. This suggests that lifestyle and environmental factors play a larger role in disease risk than previously believed.

Furthermore, the study examined the causal relationships between specific air pollutants like PM2.5 and NO2 with various health conditions. The researchers found that NO2 directly causes conditions like high cholesterol, irritable bowel syndrome, and both Type 1 and Type 2 diabetes, while PM2.5 may have a more direct effect on lung function and sleep disorders.

The research team highlighted the potential of their model to provide insights into why certain diseases are more prevalent in specific geographic locations. By understanding the complex interactions between genetics and environment, researchers can develop targeted interventions to reduce disease risk.

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. Collaborators from various departments at Penn State College of Medicine contributed to the research, showcasing the interdisciplinary nature of the study.