IMSA-Evaluate and compare different methods for constructing polygenic risk scores for prostate cancer presso Argonne

Internship Description

Background:
Polygenic risk scores (PRS) are an increasingly important tool in precision medicine, combining information from thousands of genetic variants to estimate an individual’s risk of developing disease. In prostate cancer, PRS have shown promise for risk stratification and early detection, but current approaches often perform differently across populations, with limited accuracy in men of African ancestry compared to men of European ancestry. This disparity highlights the need to evaluate PRS methods more rigorously and explore whether artificial intelligence (AI) approaches can provide more robust and equitable predictions.

Description of Student Internship:
In this internship, the student will evaluate and compare different methods for constructing polygenic risk scores for prostate cancer in men of African and European ancestry. The project will involve exploring widely used algorithms (e.g., clumping and thresholding, LDpred) alongside emerging AI-based approaches (e.g., machine-learning models that integrate genetic variants). Students will assess accuracy, generalizability, and fairness of these methods using publicly available summary statistics and synthetic datasets. The project will provide hands-on experience in genetic data analysis, AI modeling in Python, and evaluation of model performance across diverse ancestry groups. The internship will culminate in a short report and presentation contrasting traditional PRS methods with AI-driven approaches and discussing whether AI methods can serve as replacements or complements to existing techniques.
 

Education and Experience Requirements

Required Skills:
•    Interest in genetics, precision medicine, or public health
•    Familiarity with Python programming (or willingness to learn)
•    Basic understanding of statistics and data analysis
•    Curiosity about AI applications in biomedical research

Internship Family

Internship Category