A new algorithm, the Geographic Ancestor Inference Algorithm (GAIA), reconstructs human genetic variation by tracing a pathway through geographical space and time.
Published in Science*, GAIA could improve the accuracy of genome-wide association studies by providing alternative ways to account for shared ancestry and offering information on locally adaptive loci.
Modern genomes inherit spatial patterns of genetic relatedness from ancestors who lived in different geographic locations at different times. Understanding these patterns is vital for identifying the genomic basis of observable differences and demographic history.
Traditional analyses rely on assumptions that humans chose reproductive partners uniformly within regional populations. GAIA infers the time and geographic position of each shared ancestor with fewer assumptions. It accurately recovered major population movements in Europe, Asia, and Africa, demonstrating their origin in Africa.
The researchers stated that the ability to study the geography of genealogies heralds an exciting growth in the ability of the field of population genetics to shed light on population ecological processes governing the movement, distribution, and density of individuals across space and time.
Simon Gravel, PhD, from McGill University, noted that GAIA has the potential to identify critical gaps in models of human diversity.
* Michael C. Grundler et al., A geographic history of human genetic ancestry. Science 387, 1391-1397 (2025).