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

作者：Olga Flegontova, Ulaş Işıldak, Eren Yüncü, Matthew P. Williams, Christian D. Huber, Ján Kočí, Leonid Vyazov, Piya Changmai, Pavel Flegontov · 发表于：Genetics · 年份：2025 · DOI：10.1093/genetics/iyaf047 · 被引用次数：9 · 研究领域：Genetic and phenotypic traits in livestock、Gene expression and cancer classification、Bayesian Methods and Mixture Models

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 o...