On the limits of fitting complex models of population history to f-statistics

. 2023 Jun 29 ; 12 () : . [epub] 20230629

Jazyk angličtina Země Velká Británie, Anglie Médium electronic

Typ dokumentu časopisecké články, Research Support, N.I.H., Extramural, práce podpořená grantem

Perzistentní odkaz   https://www.medvik.cz/link/pmid37057893

Grantová podpora
R01 GM100233 NIGMS NIH HHS - United States
Howard Hughes Medical Institute - United States
HG012287 NIH HHS - United States
GM100233 NIH HHS - United States
R01 HG012287 NHGRI NIH HHS - United States

Our understanding of population history in deep time has been assisted by fitting admixture graphs (AGs) to data: models that specify the ordering of population splits and mixtures, which along with the amount of genetic drift and the proportions of mixture, is the only information needed to predict the patterns of allele frequency correlation among populations. The space of possible AGs relating populations is vast, and thus most published studies have identified fitting AGs through a manual process driven by prior hypotheses, leaving the majority of alternative models unexplored. Here, we develop a method for systematically searching the space of all AGs that can incorporate non-genetic information in the form of topology constraints. We implement this findGraphs tool within a software package, ADMIXTOOLS 2, which is a reimplementation of the ADMIXTOOLS software with new features and large performance gains. We apply this methodology to identify alternative models to AGs that played key roles in eight publications and find that in nearly all cases many alternative models fit nominally or significantly better than the published one. Our results suggest that strong claims about population history from AGs should only be made when all well-fitting and temporally plausible models share common topological features. Our re-evaluation of published data also provides insight into the population histories of humans, dogs, and horses, identifying features that are stable across the models we explored, as well as scenarios of populations relationships that differ in important ways from models that have been highlighted in the literature.

Před aktualizací

doi: 10.1101/2022.05.08.491072 PubMed

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