A new algorithm developed by researchers can reconstruct how human genetic variation has arisen by tracing a pathway back through geographical space and time, which could help improve the accuracy of genome-wide association studies.
The Geographic Ancestor Inference Algorithm—or GAIA—described in an article published in Science could prove useful for providing alternative ways to account for shared ancestry in genetic studies and also offer information on locally adaptive loci.
Understanding these patterns is vital in both identifying the genomic basis of observable differences and demographic history.
“Conversely, ignoring spatial demographic history can have serious implications for genome-wide association studies or the identification of loci involved in local adaptation,” noted Michael Grundler, PhD, from the University of Michigan, and co-workers.
Modern genomes are inherited from an unbroken chain of ancestors who lived in different geographic locations at different times, which creates spatial patterns of genetic relatedness. Understanding these patterns is vital for identifying the genomic basis of observable differences and demographic history.
To determine when and where these common ancestors lived, and in this way how genetic diversity came to be, most model-based analyses of population genetic data rely on certain simple assumptions.
These include the idea that humans chose their reproductive partners uniformly and at random within a few regional populations that persisted over thousands of generations. However, real populations often operate differently, choosing partners through a complex range of social and geographic actors that compass a range of characteristics.
This means that people do not form into well-defined populations that are stable over such long times and realistic models that attempt to encompass this need to include a large number of variables such as partner preferences, changes in population sizes, and migrations.
The GAIA method infers the time and geographic position of each shared ancestor, with fewer assumptions. In this way, it was able to accurately recover major population movements in Europe, Asia, and Africa and demonstrate their source in Africa.
The researchers concluded: “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.”
In an accompanying Perspective article, Simon Gravel, PhD, from McGill University, wrote of GAIA: “It has the potential to identify critical gaps in models of human diversity.”
