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The role of balancing selection is a long-standing evolutionary puzzle. Balancing selection is a crucial evolutionary process that maintains genetic variation (polymorphism) over extended periods of time; however, detecting it poses a significant challenge. Building upon the Polymorphism-aware phylogenetic Models (PoMos) framework rooted in the Moran model, we introduce a PoMoBalance model. This novel approach is designed to disentangle the interplay of mutation, genetic drift, and directional selection (GC-biased gene conversion), along with the previously unexplored balancing selection pressures on ultra-long timescales comparable with species divergence times by analyzing multi-individual genomic and phylogenetic divergence data. Implemented in the open-source RevBayes Bayesian framework, PoMoBalance offers a versatile tool for inferring phylogenetic trees as well as quantifying various selective pressures. The novel aspect of our approach in studying balancing selection lies in polymorphism-aware phylogenetic models' ability to account for ancestral polymorphisms and incorporate parameters that measure frequency-dependent selection, allowing us to determine the strength of the effect and exact frequencies under selection. We implemented validation tests and assessed the model on the data simulated with SLiM and a custom Moran model simulator. Real sequence analysis of Drosophila populations reveals insights into the evolutionary dynamics of regions subject to frequency-dependent balancing selection, particularly in the context of sex-limited color dimorphism in Drosophila erecta., Competing Interests: Conflict of interest statement None declared., (© The Author(s) 2024. Published by Oxford University Press on behalf of Society for Molecular Biology and Evolution.)

Molecular phylogenetics has neglected polymorphisms within present and ancestral populations for a long time. Recently, multispecies coalescent based methods have increased in popularity, however, their application is limited to a small number of species and individuals. We introduced a polymorphism-aware phylogenetic model (PoMo), which overcomes this limitation and scales well with the increasing amount of sequence data whereas accounting for present and ancestral polymorphisms. PoMo circumvents handling of gene trees and directly infers species trees from allele frequency data. Here, we extend the PoMo implementation in IQ-TREE and integrate search for the statistically best-fit mutation model, the ability to infer mutation rate variation across sites, and assessment of branch support values. We exemplify an analysis of a hundred species with ten haploid individuals each, showing that PoMo can perform inference on large data sets. While PoMo is more accurate than standard substitution models applied to concatenated alignments, it is almost as fast. We also provide bmm-simulate, a software package that allows simulation of sequences evolving under PoMo. The new options consolidate the value of PoMo for phylogenetic analyses with population data., (© The Author(s) 2019. Published by Oxford University Press on behalf of the Society for Molecular Biology and Evolution.)

We present a reversible Polymorphism-Aware Phylogenetic Model (revPoMo) for species tree estimation from genome-wide data. revPoMo enables the reconstruction of large scale species trees for many within-species samples. It expands the alphabet of DNA substitution models to include polymorphic states, thereby, naturally accounting for incomplete lineage sorting. We implemented revPoMo in the maximum likelihood software IQ-TREE. A simulation study and an application to great apes data show that the runtimes of our approach and standard substitution models are comparable but that revPoMo has much better accuracy in estimating trees, divergence times and mutation rates. The advantage of revPoMo is that an increase of sample size per species improves estimations but does not increase runtime. Therefore, revPoMo is a valuable tool with several applications, from speciation dating to species tree reconstruction., (Copyright © 2016 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Incomplete lineage sorting can cause incongruencies of the overall species-level phylogenetic tree with the phylogenetic trees for individual genes or genomic segments. If these incongruencies are not accounted for, it is possible to incur several biases in species tree estimation. Here, we present a simple maximum likelihood approach that accounts for ancestral variation and incomplete lineage sorting. We use a POlymorphisms-aware phylogenetic MOdel (PoMo) that we have recently shown to efficiently estimate mutation rates and fixation biases from within and between-species variation data. We extend this model to perform efficient estimation of species trees. We test the performance of PoMo in several different scenarios of incomplete lineage sorting using simulations and compare it with existing methods both in accuracy and computational speed. In contrast to other approaches, our model does not use coalescent theory but is allele frequency based. We show that PoMo is well suited for genome-wide species tree estimation and that on such data it is more accurate than previous approaches., (© The Author(s) 2015. Published by Oxford University Press, on behalf of the Society of Systematic Biologists.)

Background: The Wnt genes encode secreted glycoprotein ligands that regulate a wide range of developmental processes, including axis elongation and segmentation. There are thirteen subfamilies of Wnt genes in metazoans and this gene diversity appeared early in animal evolution. The loss of Wnt subfamilies appears to be common in insects, but little is known about the Wnt repertoire in other arthropods, and moreover the expression and function of these genes have only been investigated in a few protostomes outside the relatively Wnt-poor model species Drosophila melanogaster and Caenorhabditis elegans. To investigate the evolution of this important gene family more broadly in protostomes, we surveyed the Wnt gene diversity in the crustacean Daphnia pulex, the chelicerates Ixodes scapularis and Achaearanea tepidariorum, the myriapod Glomeris marginata and the annelid Platynereis dumerilii. We also characterised Wnt gene expression in the latter three species, and further investigated expression of these genes in the beetle Tribolium castaneum., Results: We found that Daphnia and Platynereis both contain twelve Wnt subfamilies demonstrating that the common ancestors of arthropods, ecdysozoans and protostomes possessed all members of all Wnt subfamilies except Wnt3. Furthermore, although there is striking loss of Wnt genes in insects, other arthropods have maintained greater Wnt gene diversity. The expression of many Wnt genes overlap in segmentally reiterated patterns and in the segment addition zone, and while these patterns can be relatively conserved among arthropods and the annelid, there have also been changes in the expression of some Wnt genes in the course of protostome evolution. Nevertheless, our results strongly support the parasegment as the primary segmental unit in arthropods, and suggest further similarities between segmental and parasegmental regulation by Wnt genes in annelids and arthropods respectively., Conclusions: Despite frequent losses of Wnt gene subfamilies in lineages such as insects, nematodes and leeches, most protostomes have probably maintained much of their ancestral repertoire of twelve Wnt genes. The maintenance of a large set of these ligands could be in part due to their combinatorial activity in various tissues rather than functional redundancy. The activity of such Wnt 'landscapes' as opposed to the function of individual ligands could explain the patterns of conservation and redeployment of these genes in important developmental processes across metazoans. This requires further analysis of the expression and function of these genes in a wider range of taxa.

Molecular evolutionary studies provide a means of investigating how cells function and how organisms adapt to their environment. The products of evolutionary studies provide medically important insights to the source of major diseases, such as HIV, and hold the key to understand the developing immunity of pathogenic bacteria to antibiotics. They have also helped mankind understand its place in nature, casting light on the selective forces and environmental conditions that resulted in modern humans. The use of likelihood as a framework for statistical modeling in phylogenetics has played a fundamental role in studying molecular evolution, enabling rigorous and robust conclusions to be drawn from sequence data. The first half of this article is a general introduction to the likelihood method for inferring phylogenies, the properties of the models used, and how it can be used for statistical testing. The latter half of the article focuses on the emerging new generation of phylogenetic models that describe heterogeneity in the evolutionary process along sequences, including the recoding of protein coding sequence data to amino acids and codons, and various approaches for describing dependencies between sites in a sequence. We conclude with a detailed case study examining how modern modeling approaches have been successfully employed to identify adaptive evolution in proteins.

Many phylogenetic inference methods are based on Markov models of sequence evolution. These are usually expressed in terms of a matrix (Q) of instantaneous rates of change but some models of amino acid replacement, most notably the PAM model of Dayhoff and colleagues, were originally published only in terms of time-dependent probability matrices (P(t)). Previously published methods for deriving Q have used eigen-decomposition of an approximation to P(t). We show that the commonly used value of t is too large to ensure convergence of the estimates of elements of Q. We describe two simpler alternative methods for deriving Q from information such as that published by Dayhoff and colleagues. Neither of these methods requires approximation or eigen-decomposition. We identify the methods used to derive various different versions of the Dayhoff model in current software, perform a comparison of existing and new implementations, and, to facilitate agreement among scientists using supposedly identical models, recommend that one of the new methods be used as a standard.

Recent studies suggest that closely related species can accumulate substantial genetic and phenotypic differences despite ongoing gene flow, thus challenging traditional ideas regarding the genetics of speciation. Baboons (genus Papio) are Old World monkeys consisting of six readily distinguishable species. Baboon species hybridize in the wild, and prior data imply a complex history of differentiation and introgression. We produced a reference genome assembly for the olive baboon (Papio anubis) and whole-genome sequence data for all six extant species. We document multiple episodes of admixture and introgression during the radiation of Papio baboons, thus demonstrating their value as a model of complex evolutionary divergence, hybridization, and reticulation. These results help inform our understanding of similar cases, including modern humans, Neanderthals, Denisovans, and other ancient hominins.

Genome-wide scans for positively selected genes (PSGs) in mammals have provided insight into the dynamics of genome evolution, the genetic basis of differences between species, and the functions of individual genes. However, previous scans have been limited in power and accuracy owing to small numbers of available genomes. Here we present the most comprehensive examination of mammalian PSGs to date, using the six high-coverage genome assemblies now available for eutherian mammals. The increased phylogenetic depth of this dataset results in substantially improved statistical power, and permits several new lineage- and clade-specific tests to be applied. Of approximately 16,500 human genes with high-confidence orthologs in at least two other species, 400 genes showed significant evidence of positive selection (FDR

As multi-individual population-scale data become available, more complex modeling strategies are needed to quantify genome-wide patterns of nucleotide usage and associated mechanisms of evolution. Recently, the multivariate neutral Moran model was proposed. However, it was shown insufficient to explain the distribution of alleles in great apes. Here, we propose a new model that includes allelic selection. Our theoretical results constitute the basis of a new Bayesian framework to estimate mutation rates and selection coefficients from population data. We apply the new framework to a great ape dataset, where we found patterns of allelic selection that match those of genome-wide GC-biased gene conversion (gBGC). In particular, we show that great apes have patterns of allelic selection that vary in intensity--a feature that we correlated with great apes' distinct demographies. We also demonstrate that the AT/GC toggling effect decreases the probability of a substitution, promoting more polymorphisms in the base composition of great ape genomes. We further assess the impact of GC-bias in molecular analysis, and find that mutation rates and genetic distances are estimated under bias when gBGC is not properly accounted for. Our results contribute to the discussion on the tempo and mode of gBGC evolution, while stressing the need for gBGC-aware models in population genetics and phylogenetics. [ABSTRACT FROM AUTHOR]