Federal Grant Award Summary

The University of North Carolina at Chapel Hill received a $113,464 Project Grant from the National Institute of General Medical Sciences (NIGMS) under the Biomedical Research and Research Training program (CFDA 93.859), with performance from May 1, 2026, through April 30, 2028. The project develops novel computational methods for detecting positive selection in mosquitoes, addressing critical limitations in current machine learning approaches to evolutionary biology. The research delivers three key products: (1) a new method capable of detecting and distinguishing between multiple modes of natural selection while accounting for model mis-specification, applied to yellow fever mosquito populations to elucidate genomic mechanisms of insecticide resistance; (2) analysis of structural variation (genomic variations greater than 50 base pairs) using long-read sequencing data to understand its role in rapid adaptation; and (3) the first machine learning tool designed to detect adaptive tracking, an understudied form of rapid adaptation resulting from fluctuating selective pressures.

This research directly supports NIGMS's mission to advance fundamental understanding of biological processes through investigator-initiated basic research. The methodological innovations address significant gaps in population genetics by improving the accuracy of detecting genomic signatures of rapid adaptation, with direct applicability to understanding insecticide resistance mechanisms in disease vectors—findings with potential public health implications for vector-borne disease control strategies.