ArticleGenome biology and evolution2020
Maximum Likelihood Estimation of Species Trees from Gene Trees in the Presence of Ancestral Population Structure.
Article in Genome biology and evolution, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed, 7 citations in OpenAlex.
- Robust regression rescues poor phylogenetic decisions.BMC ecology and evolution · 2025Article
- A Tale of Too Many Trees: A Conundrum for Phylogenetic Regression.Molecular biology and evolution · 2025Article
- Likelihood-Based Tests of Species Tree Hypotheses.Molecular biology and evolution · 2023Article
- The Destructive Tree PathogenJournal of fungi (Basel, Switzerland) · 2021Article
- Two new Nothophytophthora species from streams in Ireland and Northern Ireland: Nothophytophthora irlandica and N. lirii sp. nov.PloS one · 2021Article
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Authors and funding
2 authors at 2 institutions in 1 country.
Funding
Abstract
Though large multilocus genomic data sets have led to overall improvements in phylogenetic inference, they have posed the new challenge of addressing conflicting signals across the genome. In particular, ancestral population structure, which has been uncovered in a number of diverse species, can skew gene tree frequencies, thereby hindering the performance of species tree estimators. Here we develop a novel maximum likelihood method, termed TASTI (Taxa with Ancestral structure Species Tree Inference), that can infer phylogenies under such scenarios, and find that it has increasing accuracy with increasing numbers of input gene trees, contrasting with the relatively poor performances of methods not tailored for ancestral structure. Moreover, we propose a supertree approach that allows TASTI to scale computationally with increasing numbers of input taxa. We use genetic simulations to assess TASTI's performance in the three- and four-taxon settings and demonstrate the application of TASTI on a six-species Afrotropical mosquito data set. Finally, we have implemented TASTI in an open-source software package for ease of use by the scientific community.
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Registered trials
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