Performance of qpAdm-based screens for genetic admixture on admixture-graph-shaped histories and stepping-stone landscapes

Abstract

Although a broad range of methods exists for reconstructing population history from genome-wide single nucleotide polymorphism data, just a few methods gained popularity in archaeogenetics: principal component analysis (PCA); ADMIXTURE, an algorithm that models individuals as mixtures of multiple ancestral sources represented by actual or inferred populations; formal tests for admixture such as f3-statistics and D-statistics; and qpAdm, a tool for fitting two-component and more complex admixture models to groups or individuals. Despite their popularity in archaeogenetics, which is explained by modest computational requirements and ability to analyse data of various types and qualities, protocols relying on qpAdm that screen numerous alternative models of varying complexity and find "fitting" models (often considering both estimated admixture proportions and p-values as a composite criterion of model fit) remain untested on complex simulated population histories in the form of admixture graphs of random topology. We analysed genotype data extracted from such simulations and tested various types of high-throughput qpAdm protocols ("rotating" and "non-rotating", with or without temporal stratification of target groups and proxy ancestry sources, with or without a "model competition" step). We caution that these qpAdm protocols may be inappropriate for exploratory analyses in poorly studied regions/periods since their false discovery rates varied between 12% and 68% depending on the details of the protocol and on the amount and quality of simulated data (i.e., >12% of fitting two-way admixture models imply gene flows that were not simulated), although our study has a number of limitations. We demonstrate that for reducing false discovery rates of qpAdm protocols to nearly 0% it is advisable to use large SNP sets with low missing data rates, the rotating qpAdm protocol with a strictly enforced rule that target groups do not pre-date their proxy sources, and an unsupervised ADMIXTURE analysis as a way to verify feasible qpAdm models.

High-resolution genomic ancestry reveals mobility in early medieval Europe

“…e . after the time of the ancient individual(s) 49 . The most principled approach is thus to build ancestry models where source and ‘outgroup/reference’ populations are older than, or at least contemporary with, the target genome or group which we are trying to model 49 .…”

“…Modern populations provide ample power to detect differences, but their genetic affinity to ancient individuals may be confounded by later gene flow, i.e. after the time of the ancient individual(s) 49 . The most principled approach is thus to build ancestry models where source and 'outgroup/reference' populations are older than, or at least contemporary with, the target genome or group which we are trying to model 49 .…”

“…after the time of the ancient individual(s) 49 . The most principled approach is thus to build ancestry models where source and 'outgroup/reference' populations are older than, or at least contemporary with, the target genome or group which we are trying to model 49 . The improved statistical power of time-restricted ancestry in Twigstats thus offers an opportunity to develop principled ancestry models based solely on temporally appropriate source proxy groups.…”

The genetic origins and impacts of historical Papuan migrations into Wallacea

“…Following results from our ADMIXTURE and PCA analyses, we included multiple Asian and Melanesian populations (limited to a subset of Papuan language speakers; see SI Methods) as potential ancestry sources, and added the Toalean forager and Onge population as proxies for founder ancestry. To improve qpAdm sensitivity and power, we rotated populations across 'left' and 'right' groupings to produce a large set of ancestry models for each Wallacean population that were evaluated for plausibility (see SI Methods; (39)).…”

Fast and reliable ancestral reconstruction on ancient genotype data with non-negative Least square and Principal Component Analysis

“…However, sifting through all possible proxy sources and the right populations for an admixture event can be overwhelming. In addition, a recent survey has shown that, depending on the approach and the quality of the genetic data analyzed, qpAdm may suffer from high false discovery rates, adding substantial uncertainty to the interpretation of the results of admixture inference (Eren Yüncü et al, 2023).…”