A geographic history of human genetic ancestry

Spatial patterns in genetic diversity are shaped by individuals dispersing from their parents and larger-scale population movements. It has long been appreciated that these patterns of movement shape the underlying genealogies along the genome leading to geographic patterns of isolation by distance in contemporary population genetic data. However, extracting the enormous amount of information contained in genealogies along recombining sequences has, up till recently, not been computational feasible. Here we capitalize on important recent advances in gene-genealogy reconstruction and develop methods to use thousands of trees to estimate time-varying per-generation dispersal rates and to locate the genetic ancestors of a sample back through time. We take a likelihood approach in continuous space using a simple approximate model (branching Brownian motion) as our prior distribution of spatial genealogies. After testing our method with simulations we apply it to the 1001 Genomes dataset of over one thousand Arabidopsis thaliana genomes sampled across a wide geographic extent. We detect a very high dispersal rate in the recent past, especially longitudinally, and use inferred ancestor locations to visualize many examples of recent long-distance dispersal and admixture. We also use inferred ancestor locations to identify the origin and ancestry of the North American expansion and to depict alternative geographic ancestries stemming from multiple glacial refugia. Our method highlights the huge amount of information about past dispersal events and population movements contained in genome-wide genealogies.

show abstract

A general and efficient representation of ancestral recombination graphs

Wong,

Ignatieva,

Koskela

et al. 2023

Preprint

View full text Add to dashboard Cite

As a result of recombination, adjacent nucleotides can have different paths of genetic inheri-tance and therefore the genealogical trees for a sample of DNA sequences vary along the genome. The structure capturing the details of these intricately interwoven paths of inheritance is referred to as an ancestral recombination graph (ARG). New developments have made it possible to infer ARGs at scale, enabling many new applications in population and statistical genetics. This rapid progress, however, has led to a substantial gap opening between theory and practice. Standard mathematical formalisms, based on exhaustively detailing the “events” that occur in the history of a sample, are insufficient to describe the outputs of current methods. Moreover, we argue that the underlying assumption that all events can be known and precisely estimated is fundamentally unsuited to the realities of modern, population-scale datasets. We propose an alternative mathematical formulation that encompasses the outputs of recent methods and can capture the full richness of modern large-scale datasets. By defining this ARG encoding in terms of specific genomes and their intervals of genetic inheritance, we avoid the need to exhaustively list (and estimate)allevents. The effects of multiple events can be aggregated in different ways, providing a natural way to express many forms of approximate and partial knowledge about the recombinant ancestry of a sample.

show abstract

A geographic history of human genetic ancestry

Cited by 6 publications

References 44 publications

Inference and applications of ancestral recombination graphs

Inference and applications of ancestral recombination graphs

Estimating dispersal rates and locating genetic ancestors with genome-wide genealogies

A general and efficient representation of ancestral recombination graphs

Contact Info

Product

Resources

About