Traditional ancestry reports often provide a static snapshot, indicating, for example, that an individual is "50% Irish." While informative, this perspective oversimplifies the intricate tapestry of human ancestry, which is more akin to a dynamic film than a still photograph. Recognizing this complexity, researchers from the University of Michigan have developed a statistical method1 that offers a more comprehensive view of our ancestral origins and migrations over time.
The Gaia Algorithm: Mapping Ancestral Movements
The Geographic Ancestry Inference Algorithm (Gaia) is a parsimony-based method designed to infer the geographic locations of shared genetic ancestors within a tree sequence. By analyzing modern genetic sequences, Gaia estimates the locations of an individual's genetic forebears, tracking their movements across generations. This approach transforms our understanding of ancestry from a static representation to a dynamic narrative, illustrating how ancestral populations migrated and interacted over centuries.
According to the developers, Gaia works by fitting a minimum migration cost function to each genomic position in an ancestral haplotype using the generalized parsimony algorithm and the local gene genealogy relating the sampled genomes at the given position.
Listen to this episode with a 7-day free trial
Subscribe to Anthropology.net to listen to this post and get 7 days of free access to the full post archives.
