Mutations can change their frequency in a population because of selection or luck, and peering back in time to figure out why specific polymorphisms persisted has proven to be a particularly difficult scientific challenge. Now, research published September 13 in Cell Reports describes a tool that will likely make it easier for scientists, especially those studying the genomic roots of adaptation and disease, to do just that.
The tool, a deep learning algorithm called DeepFavored, simultaneously runs several statistical tests on existing genome-wide association study (GWAS) datasets to distinguish favored mutations—those that were the result of selection—from hitchhiking mutations that weren’t selected for but occurred alongside the favored ones. In validating the tool on three separate human populations, the researchers behind the paper, who are based at Southern Medical University in China, say they’ve identified genomic tradeoffs: mutations adaptive for specific environments that also made people more susceptible to certain ...
