Invited Presentation: Modeling interactions between genetics and environment to guide disease risk prediction, biological discovery, and prevention strategies
Confirmed Presenter: Alessandro Lussana, EMBL-EBI, United Kingdom
Format: In Person
Authors List: Show
- Alessandro Lussana, EMBL-EBI, United Kingdom
- Federico Marotta, EMBL, Germany
- Evangelia Petsalaki, EMBL-EBI, United Kingdom
- Peer Bork, EMBL, Germany
Presentation Overview: Show
In the context of the PEGS DREAM Challenge, our team focused on modeling the interactions between genetic and environmental factors to predict the risk of hypercholesterolemia. By integrating health and exposure survey data with genetic information, we developed a machine learning model that leverages both a random forest classifier and polygenic risk scores (PRS) to assess individual risk. The results demonstrate the importance of combining genetic and environmental data to improve disease risk prediction, with our approach showing promise in enhancing the accuracy of hypercholesterolemia classification.
Our work emphasizes the importance of identifying gene-environment (GxE) interactions beyond the aim of improving predictions. Understanding these interactions will be crucial in the future of human genetics as they can guide the formulation of hypothesis for disease etiology in complex traits. We outline a strategy that can be applied to the PEGS cohort to systematically discover GxE interactions, which will be essential to gain mechanistic insights of traits like hypercholesterolemia, potentially leading to new strategies for disease prevention and personalized medicine.
Invited Presentation: Modeling interactions between genetics and environment to guide disease risk prediction, biological discovery, and prevention strategies
Confirmed Presenter: Federico Marotta, EMBL, Germany
Format: In Person
Authors List: Show
- Federico Marotta, EMBL, Germany
- Alessandro Lussana, EMBL-EBI, United Kingdom
- Evangelia Petsalaki, EMBL-EBI, United Kingdom
- Peer Bork, EMBL, Germany
Presentation Overview: Show
In the context of the PEGS DREAM Challenge, our team focused on modeling the interactions between genetic and environmental factors to predict the risk of hypercholesterolemia. By integrating health and exposure survey data with genetic information, we developed a machine learning model that leverages both a random forest classifier and polygenic risk scores (PRS) to assess individual risk. The results demonstrate the importance of combining genetic and environmental data to improve disease risk prediction, with our approach showing promise in enhancing the accuracy of hypercholesterolemia classification.
Our work emphasizes the importance of identifying gene-environment (GxE) interactions beyond the aim of improving predictions. Understanding these interactions will be crucial in the future of human genetics as they can guide the formulation of hypothesis for disease etiology in complex traits. We outline a strategy that can be applied to the PEGS cohort to systematically discover GxE interactions, which will be essential to gain mechanistic insights of traits like hypercholesterolemia, potentially leading to new strategies for disease prevention and personalized medicine.