Federal Grant Award Summary

The University of Chicago received a $410,000 Project Grant award from the National Institute of General Medical Sciences under the Biomedical Research and Research Training program (CFDA 93.859), effective March 15, 2026, with a completion date of December 31, 2030. This five-year research initiative focuses on developing innovative computational and statistical methods to advance causal inference in genetics and genomics. The project delivers novel analytical tools and frameworks that integrate experimental data with large-scale external datasets to address critical methodological challenges in understanding how genetic variation drives disease mechanisms.

The research program produces two primary sets of deliverables: (1) advanced statistical and machine learning methods for Mendelian randomization (MR) that address confounding biases arising from family and environmental structure, improve ancestry-specific causal effect estimation, and enhance reliability of causal estimates for complex neuropsychiatric and related health outcomes; and (2) sophisticated computational models for single-cell CRISPR screening that combine in silico predictions with experimental data to improve perturbation effect accuracy and optimize experimental design. The project aims to unify population-scale genomic studies with cellular-level experimental findings to identify causal pathways linking genetic variants, cellular responses, and disease outcomes, while also addressing data heterogeneity and supporting the training and mentorship of graduate students and early-career researchers in these specialized methodological areas.