Federal Grant Award Summary

The University of Pittsburgh received a $417,620 Project Grant from the National Institute of General Medical Sciences (NIGMS) under the Biomedical Research and Research Training program (CFDA 93.859) awarded on March 1, 2026, with a completion date of December 31, 2030. The grant supports the development of novel computational methods to quantify natural selection and predict evolutionary dynamics using temporal genetic data. The research team will create statistical tools that leverage mathematical methods from statistical physics to analyze sequences and phenotypes sampled over time, enabling the identification of genetic variants and phenotypes under selection while accounting for complex biological factors such as linkage disequilibrium, epistasis, and time-varying selection coefficients.

The project will pursue three primary research directions: (1) developing methods to jointly analyze selection on individual mutations and phenotypic traits by integrating population genetics, quantitative genetics, and machine learning approaches; (2) applying these methods to study rapid evolution in viral pathogens with emphasis on understanding phenotypic changes; and (3) leveraging insights from previous research on HIV-1 immune escape and SARS-CoV-2 adaptation to build predictive models of evolutionary dynamics. The deliverables include computational algorithms, analytical frameworks, and applications to viral evolution studies that advance understanding of selection mechanisms relevant to emerging pathogen threats, drug resistance development, and protein engineering.