New AI model reconstructs human ancestry from DNA
A stretch of DNA can look static on a screen, just long rows of A, T, C and G. But buried in those letters are small changes that mark where lineages split, rejoined and drifted over time. Researchers at the University of Oregon say they have built an artificial intelligence system that can read those mutation patterns much the way a language model reads text, then use them to estimate when two genes last shared a common ancestor. The tool, called cxt, is described in the Proceedings of the National Academy of Sciences and is aimed at one of population genetics’ hardest jobs: reconstructing the hidden family history inside a genome. Instead of predicting the next word in a sentence, the model predicts what the team calls the next coalescence, an estimate of shared ancestry along a chromosome. Andrew Kern, a computational biologist in the University of Oregon College of Arts and Sciences, said the work draws on ideas behind generative AI but puts them to use in a field that has largely relied on …

