qpAdm is a statistical tool that is often used for testing large sets of alternative admixture models for a target population. Despite its popularity, qpAdm remains untested on 2D stepping stone landscapes and in situations with low prestudy odds (low ratio of true to false models). We tested high-throughput qpAdm protocols with typical properties such as number of source combinations per target, model complexity, model feasibility criteria, etc. Those protocols were applied to admixture graph-shaped and stepping stone simulated histories sampled randomly or systematically. We demonstrate that false discovery rates of high-throughput qpAdm protocols exceed 50% for many parameter combinations since: (1) prestudy odds are low and fall rapidly with increasing model complexity; (2) complex migration networks violate the assumptions of the method; hence, there is poor correlation between qpAdm P-values and model optimality, contributing to low but nonzero false-positive rate and low power; and (3) although admixture fraction estimates between 0 and 1 are largely restricted to symmetric configurations of sources around a target, a small fraction of asymmetric highly nonoptimal models have estimates in the same interval, contributing to the false-positive rate. We also reinterpret large sets of qpAdm models from 2 studies in terms of source-target distance and symmetry and suggest improvements to qpAdm protocols: (1) temporal stratification of targets and proxy sources in the case of admixture graph-shaped histories, (2) focused exploration of few models for increasing prestudy odds; and (3) dense landscape sampling for increasing power and stringent conditions on estimated admixture fractions for decreasing the false-positive rate.

Department of Biological Sciences Middle East Technical University Üniversiteler Mahallesi Ankara 06800 Türkiye

Department of Biology and Ecology Faculty of Science University of Ostrava Ostrava 710 00 Czechia

Department of Biology Eberly College of Science The Pennsylvania State University University Park PA 16802 USA

Department of Human Evolutionary Biology Harvard University Cambridge MA 02138 USA

Institute of Parasitology Biology Centre of the Czech Academy of Sciences České Budějovice 370 05 Czechia

Leibniz Institute on Aging Fritz Lipmann Institute Jena 07745 Germany

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bioRxiv. 2025 Feb 03:2023.04.25.538339. doi: 10.1101/2023.04.25.538339. PubMed

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