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The PEGS DREAM Challenge: A Crowdsourcing Approach to Understanding Hypercholesterolemia with Multi- dimensional
Farida S Akhtari1, Johannes Falk2, Jyoti Jyoti2
1National Institute of Environmental Health Sciences.
Research Square
|May 4, 2026
Summary
Crowdsourced challenges advance biomedical research. Integrating diverse data, including environmental and genetic factors, improved hypercholesterolemia risk prediction beyond traditional methods.
Area of Science:
- Biomedical Research
- Computational Biology
- Genetics
Background:
- Crowdsourced challenges accelerate biomedical innovation.
- The Personalized Environment and Genes Study (PEGS) cohort provided diverse data for analysis.
Purpose of the Study:
- To develop predictive models for hypercholesterolemia risk.
- To integrate environmental, genetic, and geospatial data for improved disease prediction.
Main Methods:
- A crowdsourced challenge format was employed.
- Gradient boosting and random forest classifiers were utilized.
- Multimodal data integration, including whole-genome sequencing and environmental exposures, was performed.
Main Results:
- Top models significantly outperformed traditional polygenic scores (PGS) in predicting hypercholesterolemia.
- Models achieved AUROCs of 0.7933 and 0.7919, exceeding the benchmark of 0.7358.
- Self-reported health and environmental data proved valuable predictors.
Conclusions:
- Integrative, multimodal approaches enhance hypercholesterolemia risk prediction.
- Environmental and self-reported data are crucial for understanding complex diseases.
- Crowdsourced challenges effectively drive advancements in predictive modeling for human diseases.
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