Researchers discovered a mutation using an algorithm that distinguishes between selected mutations and those inherited by chance, a feat previously unsolved by scientists.

Researchers at Southern Medical University have developed a deep learning algorithm called Deep Favored to help scientists study the genomic roots of adaptation and disease. The tool uses GWAS datasets to distinguish favored mutations from hitchhiking mutations. The researchers found genomic trade offs between adaptation and disease susceptibility, which could help researchers explore further research questions.

The paper’s conclusions are reasonable, but more confidence in specific trade offs would be gained with functional experiments, as genetic trade offs are hard to find.