Impact of Ghost Introgression on Coalescent-based Species Tree Inference and Estimation of Divergence Time

The species studied in any evolutionary investigation generally constitute a very small proportion of all the species currently existing or that have gone extinct. It is therefore likely that introgression, which is widespread across the tree of life, involves "ghosts," i.e., unsampled, unknown, or extinct lineages. However, the impact of ghost introgression on estimations of species trees has been rarely studied and is thus poorly understood. In this study, we use mathematical analysis and simulations to examine the robustness of species tree methods based on a multispecies coalescent model under gene flow sourcing from an extant or ghost lineage. We found that very low levels of extant or ghost introgression can result in anomalous gene trees (AGTs) on three-taxon rooted trees if accompanied by strong incomplete lineage sorting (ILS). In contrast, even massive introgression, with more than half of the recipient genome descending from the donor lineage, may not necessarily lead to AGTs. In cases involving an ingroup lineage (defined as one that diverged no earlier than the most basal species under investigation) acting as the donor of introgression, the time of root divergence among the investigated species was either underestimated or remained unaffected, but for the cases of outgroup ghost lineages acting as donors, the divergence time was generally overestimated. Under many conditions of ingroup introgression, the stronger the ILS was, the higher was the accuracy of estimating the time of root divergence, although the topology of the species tree is more prone to be biased by the effect of introgression.

How to Tackle Phylogenetic Discordance in Recent and Rapidly Radiating Groups? Developing a Workflow Using Loricaria (Asteraceae) as an Example

A major challenge in phylogenetics and -genomics is to resolve young rapidly radiating groups. The fast succession of species increases the probability of incomplete lineage sorting (ILS), and different topologies of the gene trees are expected, leading to gene tree discordance, i.e., not all gene trees represent the species tree. Phylogenetic discordance is common in phylogenomic datasets, and apart from ILS, additional sources include hybridization, whole-genome duplication, and methodological artifacts. Despite a high degree of gene tree discordance, species trees are often well supported and the sources of discordance are not further addressed in phylogenomic studies, which can eventually lead to incorrect phylogenetic hypotheses, especially in rapidly radiating groups. We chose the high-Andean Asteraceae genus Loricaria to shed light on the potential sources of phylogenetic discordance and generated a phylogenetic hypothesis. By accounting for paralogy during gene tree inference, we generated a species tree based on hundreds of nuclear loci, using Hyb-Seq, and a plastome phylogeny obtained from off-target reads during target enrichment. We observed a high degree of gene tree discordance, which we found implausible at first sight, because the genus did not show evidence of hybridization in previous studies. We used various phylogenomic analyses (trees and networks) as well as the D-statistics to test for ILS and hybridization, which we developed into a workflow on how to tackle phylogenetic discordance in recent radiations. We found strong evidence for ILS and hybridization within the genus Loricaria. Low genetic differentiation was evident between species located in different Andean cordilleras, which could be indicative of substantial introgression between populations, promoted during Pleistocene glaciations, when alpine habitats shifted creating opportunities for secondary contact and hybridization.

Systematic review of the firefly genus Emeia Fu, Ballantyne & Lambkin, 2012 (Coleoptera: Lampyridae) from China

The Luciolinae genus Emeia Fu, Ballantyne & Lambkin, 2012 is reviewed. Firefly species tree is reconstructed based upon phylogenetic analysis of CO1 barcoding sequences from 42 fireflies and 2 outgroup species, including three main Lampyridae sub-families: Luciolinae, Photurinae and Lampyrinae. The genus Emeia belongs to Luciolinae based on morphological and DNA sequence level evidences, the diagnostic features for male adults include pink-red pronotum with a black median stripe, subparallel-sided pronotum, un-developed elytral humeral carina. In this study, a new Emeia species,Emeia pulchra sp. nov. is described and illustrated from the wetland of Lishui, Zhejiang, China. The new species is sister to E. pseudosauteri in the Emeia genus which is supported by characteristic morphological features and DNA barcoding data. The two species are separated geographically as shown on the distribution map. A key to the males of the species of Emeia is also provided.

Polymorphism-aware estimation of species trees and evolutionary forces from genomic sequences with RevBayes

The availability of population genomic data through new sequencing technologies gives unprecedented opportunities for estimating important evolutionary forces such as genetic drift, selection, and mutation biases across organisms. Yet, analytical methods that can handle polymorphisms jointly with sequence divergence across species are rare and not easily accessible to empiricists. We implemented polymorphism-aware phylogenetic models (PoMos), an alternative approach for species tree estimation, in the Bayesian phylogenetic software RevBayes. PoMos naturally account for incomplete lineage sorting, which is known to cause difficulties for phylogenetic inference in species radiations, and scale well with genome-wide data. Simultaneously, PoMos can estimate mutation and selection biases. We have applied our methods to resolve the complex phylogenetic relationships of a young radiation of Chorthippus grasshoppers, based on coding sequences. In addition to establishing a well-supported species tree, we found a mutation bias favoring AT alleles and selection bias promoting the fixation of GC alleles, the latter consistent with GC-biased gene conversion. The selection bias is two orders of magnitude lower than genetic drift, validating the critical role of nearly neutral evolutionary processes in species radiation. PoMos offer a wide range of models to reconstruct phylogenies and can be easily combined with existing models in RevBayes - e.g., relaxed clock and divergence time estimation - offering new insights into the evolutionary processes underlying molecular evolution and, ultimately, species diversification.

Phylogenetic analysis of the Neotropical Albitarsis Complex based on mitogenome data

Background Some of the most important malaria vectors in South America belong to the Albitarsis Complex (Culicidae; Anophelinae; Anopheles). Understanding the origin, nature, and geographical distribution of species diversity in this important complex has important implications for vector incrimination, control, and management, and for modelling future responses to climate change, deforestation, and human population expansion. This study attempts to further explore species diversity and evolutionary history in the Albitarsis Complex by undertaking a characterization and phylogenetic analysis of the mitogenome of all 10 putative taxa in the Albitarsis Complex. Methods Mitogenome assembly and annotation allowed for feature comparison among Albitarsis Complex and Anopheles species. Selection analysis was conducted across all 13 protein-coding genes. Maximum likelihood and Bayesian inference methods were used to construct gene and species trees, respectively. Bayesian methods were also used to jointly estimate species delimitation and species trees. Results Gene composition and order were conserved across species within the complex. Unique signatures of positive selection were detected in two species—Anopheles janconnae and An. albitarsis G—which may have played a role in the recent and rapid diversification of the complex. The COI gene phylogeny does not fully recover the mitogenome phylogeny, and a multispecies coalescent-based phylogeny shows that considerable uncertainty exists through much of the mitogenome species tree. The origin of divergence in the complex dates to the Pliocene/Pleistocene boundary, and divergence within the distinct northern South American clade is estimated at approximately 1 million years ago. Neither the phylogenetic trees nor the delimitation approach rejected the 10-species hypothesis, although the analyses could not exclude the possibility that four putative species with scant a priori support (An. albitarsis G, An. albitarsis H, An. albitarsis I, and An. albitarsis J), represent population-level, rather than species-level, splits. Conclusion The lack of resolution in much of the species tree and the limitations of the delimitation analysis warrant future studies on the complex using genome-wide data and the inclusion of additional specimens, particularly from two putative species, An. albitarsis I and An. albitarsis J. Graphical Abstract

On the Effect of Intralocus Recombination on Triplet-Based Species Tree Estimation

We consider species tree estimation from multiple loci subject to intralocus recombination. We focus on R∗, a summary coalescent-based methods using rooted triplets. We demonstrate analytically that intralocus recombination gives rise to an inconsistency zone, in which correct inference is not assured even in the limit of infinite amount of data. In addition, we validate and characterize this inconsistency zone through a simulation study that suggests that differential rates of recombination between closely related taxa can amplify the effect of incomplete lineage sorting and contribute to inconsistency.

Scalable Species Tree Inference with External Constraints

Species tree inference under the multi-species coalescent (MSC) model is a basic step in biological discovery. Despite the developments in recent years of methods that are proven statistically consistent and that have high accuracy, large datasets create computational challenges. Although there is gener- ally some information available about the species trees that could be used to speed up the estimation, only one method, ASTRAL-J, a recent development in the ASTRAL family of methods, is able to use this information. Here we describe two new methods, NJst-J and FASTRAL-J, that can estimate the species tree given partial knowledge of the species tree in the form of a non-binary unrooted constraint tree.. We show that both NJst-J and FASTRAL-J are much faster than ASTRAL-J and we prove that all three methods are statistically consistent under the multi-species coalescent model subject to this constraint. Our extensive simulation study shows that both FASTRAL-J and NJst-J provide advantages over ASTRAL-J: both are faster (and NJst-J is particularly fast), and FASTRAL-J is generally at least as accurate as ASTRAL-J. An analysis of the Avian Phylogenomics project dataset with 48 species and 14,446 genes presents additional evidence of the value of FASTRAL-J over ASTRAL-J (and both over ASTRAL), with dramatic reductions in running time (20 hours for default ASTRAL, and minutes or seconds for ASTRAL-J and FASTRAL-J, respectively). Availability: FASTRAL-J and NJst-J are available in open source form at https://github.com/ RuneBlaze/FASTRAL-constrained and https://github.com/RuneBlaze/NJst-constrained. Locations of the datasets used in this study and detailed commands needed to reproduce the study are provided in the supplementary materials at http://tandy.cs.illinois.edu/baqiao-suppl.pdf.

QT-GILD: Quartet Based Gene Tree Imputation Using Deep Learning Improves Phylogenomic Analyses Despite Missing Data

Species tree estimation is frequently based on phylogenomic approaches that use multiple genes from throughout the genome. However, for a combination of reasons (ranging from sampling biases to more biological causes, as in gene birth and loss), gene trees are often incomplete, meaning that not all species of interest have a common set of genes. Incomplete gene trees can potentially impact the accuracy of phylogenomic inference. We, for the first time, introduce the problem of imputing the quartet distribution induced by a set of incomplete gene trees, which involves adding the missing quartets back to the quartet distribution. We present QT-GILD, an automated and specially tailored unsupervised deep learning technique, accompanied by cues from natural language processing (NLP), which learns the quartet distribution in a given set of incomplete gene trees and generates a complete set of quartets accordingly. QT-GILD is a general-purpose technique needing no explicit modeling of the subject system or reasons for missing data or gene tree heterogeneity. Experimental studies on a collection of simulated and empirical data sets suggest that QT-GILD can effectively impute the quartet distribution, which results in a dramatic improvement in the species tree accuracy. Remarkably, QT-GILD not only imputes the missing quartets but it can also account for gene tree estimation error. Therefore, QT-GILD advances the state-of-the-art in species tree estimation from gene trees in the face of missing data. QT-GILD is freely available in open source form at https://github.com/pythonLoader/QT-GILD .