The PEGS DREAM Challenge: A Crowdsourcing Approach to Understanding Hypercholesterolemia with Multi- dimensional...

Crowdsourced challenges are powerful catalysts for advancing biomedical research and fostering community-driven innovation. The DREAM Challenges initiative, in collaboration with the Personalized Environment and Genes Study (PEGS), held a competition to spur the development of predictive models for hypercholesterolemia risk. Participants were tasked with integrating diverse data, including environmental exposures, whole-genome sequencing, and geospatial information, from a large and diverse cohort to classify hypercholesterolemia and generate novel insights. The top-performing models, which primarily leveraged gradient boosting and random forest classifiers, demonstrated strong predictive performance, outperforming traditional polygenic scores (PGS). In Challenge Task 1, models from the two top-performing teams substantially outperformed the Challenge benchmark dataset using only PGS (AUROC = 0.7358), with AUROCs of 0.7933 and 0.7919, respectively. Beyond prediction, these models highlight the significant value of self-reported health and environmental exposure data, revealing them as informative predictors of hypercholesterolemia risk that complement established clinical and genetic factors. This paper details the design of the challenge, presents the top-performing models, and highlights the potential of integrative, multimodal approaches for understanding and predicting complex human diseases.