{"doi":"10.1093/genetics/iyaf047","title":"Performance of <i>qpAdm</i> -based screens for genetic admixture on graph–shaped histories and stepping stone landscapes","abstract":"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.","journal":"Genetics","year":2025,"id":517236,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":8,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9543,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2025-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":1014830,"name":"Ulaş Işıldak","orcid":"0000-0001-6497-6254","position":1,"is_corresponding":false},{"id":882358,"name":"Eren Yüncü","orcid":"0000-0002-8194-0277","position":2,"is_corresponding":false},{"id":1161210,"name":"Matthew P. Williams","orcid":"0000-0001-7117-193X","position":3,"is_corresponding":false},{"id":51378,"name":"Christian D. Huber","orcid":"0000-0002-2267-2604","position":4,"is_corresponding":false},{"id":124626,"name":"Jan Kočí","orcid":"0000-0002-9036-8321","position":5,"is_corresponding":false},{"id":1161211,"name":"Leonid Vyazov","orcid":"0000-0002-2074-9276","position":6,"is_corresponding":false},{"id":882353,"name":"Piya Changmai","orcid":"0000-0003-3307-0585","position":7,"is_corresponding":false},{"id":882361,"name":"Pavel Flegontov","orcid":"0000-0001-9759-4981","position":8,"is_corresponding":false},{"id":882988,"name":"Olga Flegontova","orcid":null,"position":0,"is_corresponding":true}],"reference_count":90,"raw_metadata":null,"created_at":"2026-07-19T02:48:59.410472Z","pmid":"40169722","pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}