Estimation of species divergence times in presence of cross-species gene flow
Creators: name: Tiley, George
name: Flouri, Tomas
name: Jiao, Xiyun
name: Poelstra, Jelmer
name: Xu, Bo
name: Zhu, Tianqi
name: Rannala, Bruce
name: Yoder, Anne
name: Yang, Ziheng
Publisher: name: Zenodo
Year: 2023
Subjects: multispecies coalescent
phylogenetic networks
divergence time
hybridization
introgression
Gene flow

description: Cross-species introgression can have significant impacts on phylogenomic reconstruction of species divergence events. Here, we used simulations to show how the presence of even a small amount of introgression can bias divergence time estimates when gene flow is ignored in the analysis. Using advances in analytical methods under the multispecies coalescent (MSC) model, we demonstrate that by accounting for incomplete lineage sorting and introgression using large phylogenomic data sets this problem can be avoided. The multispecies-coalescent with-introgression (MSci) model is capable of accurately estimating both divergence times and ancestral effective population sizes, even when only a single diploid individual per species is sampled. We characterize some general expectations for biases in divergence time estimation under three different scenarios: 1) introgression between sister species, 2) introgression between non-sister species, and 3) introgression from an unsampled (i.e., ghost) outgroup lineage. We also conducted simulations under the isolation-with-migration (IM) model, and found that the MSci model assuming episodic gene flow was able to accurately estimate species divergence times despite high levels of continuous gene flow. We estimated divergence times under the MSC and MSci models from two published empirical datasets with previous evidence of introgression, one of 372 target enrichment loci from baobabs (<em>Adansonia</em>), and another of 1,000 transcriptome loci from fourteen species of the tomato relative, <em>Jaltomata</em>. The empirical analyses not only confirm our findings from simulations, demonstrating that the MSci model can reliably estimate divergence times, but also show that divergence time estimation under the MSC can be robust to the presence of small amounts of introgression in empirical datasets with extensive taxon sampling.
description: Funding provided by: National Institute of General Medical Sciences<br>Crossref Funder Registry ID: http://dx.doi.org/10.13039/100000057<br>Award Number: R01GM123306Funding provided by: Biotechnology and Biological Sciences Research Council<br>Crossref Funder Registry ID: http://dx.doi.org/10.13039/501100000268<br>Award Number: BB/N000609/1Funding provided by: Biotechnology and Biological Sciences Research Council<br>Crossref Funder Registry ID: http://dx.doi.org/10.13039/501100000268<br>Award Number: BB/P006493/1Funding provided by: Biotechnology and Biological Sciences Research Council<br>Crossref Funder Registry ID: http://dx.doi.org/10.13039/501100000268<br>Award Number: BB/T003502/1Funding provided by: Duke University<br>Crossref Funder Registry ID: http://dx.doi.org/10.13039/100006510<br>Award Number: Funding provided by: Duke Kunshan University<br>Crossref Funder Registry ID: http://dx.doi.org/10.13039/501100015671<br>Award Number: Funding provided by: H2020 Marie Skłodowska-Curie Actions<br>Crossref Funder Registry ID: http://dx.doi.org/10.13039/100010665<br>Award Number: MSCA-IF-EF-ST 101026923