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Machine learning has increasingly been applied to a wide range of questions in phylogenetic inference. Supervised machine learning approaches that rely on simulated training data have been used to infer tree topologies and branch lengths, to select substitution models, and to perform downstream inferences of introgression and diversification. Here, we review how researchers have used several promising machine learning approaches to make phylogenetic inferences. Despite the promise of these methods, several barriers prevent supervised machine learning from reaching its full potential in phylogenetics. We discuss these barriers and potential paths forward. In the future, we expect that the application of careful network designs and data encodings will allow supervised machine learning to accommodate the complex processes that continue to confound traditional phylogenetic methods. 

Abstract Detecting introgression between closely related populations or species is a fundamental objective in evolutionary biology. Existing methods for detecting migration and inferring migration rates from population genetic data often assume a neutral model of evolution. Growing evidence of the pervasive impact of selection on large portions of the genome across diverse taxa suggests that this assumption is unrealistic in most empirical systems. Further, ignoring selection has previously been shown to negatively impact demographic inferences (e.g. of population size histories). However, the impacts of biologically realistic selection on inferences of migration remain poorly explored. Here, we simulate data under models of background selection, selective sweeps, balancing selection, and adaptive introgression. We show that ignoring selection sometimes leads to false inferences of migration in popularly used methods that rely on the site frequency spectrum. Specifically, balancing selection and some models of background selection result in the rejection of isolation-only models in favor of isolation-with-migration models and lead to elevated estimates of migration rates. BPP, a method that analyzes sequence data directly, showed false positives for all conditions at recent divergence times, but balancing selection also led to false positives at medium-divergence times. Our results suggest that such methods may be unreliable in some empirical systems, such that new methods that are robust to selection need to be developed. 

Abstract MotivationThe application of machine learning approaches in phylogenetics has been impeded by the vast model space associated with inference. Supervised machine learning approaches require data from across this space to train models. Because of this, previous approaches have typically been limited to inferring relationships among unrooted quartets of taxa, where there are only three possible topologies. Here, we explore the potential of generative adversarial networks (GANs) to address this limitation. GANs consist of a generator and a discriminator: at each step, the generator aims to create data that is similar to real data, while the discriminator attempts to distinguish generated and real data. By using an evolutionary model as the generator, we use GANs to make evolutionary inferences. Since a new model can be considered at each iteration, heuristic searches of complex model spaces are possible. Thus, GANs offer a potential solution to the challenges of applying machine learning in phylogenetics. ResultsWe developed phyloGAN, a GAN that infers phylogenetic relationships among species. phyloGAN takes as input a concatenated alignment, or a set of gene alignments, and infers a phylogenetic tree either considering or ignoring gene tree heterogeneity. We explored the performance of phyloGAN for up to 15 taxa in the concatenation case and 6 taxa when considering gene tree heterogeneity. Error rates are relatively low in these simple cases. However, run times are slow and performance metrics suggest issues during training. Future work should explore novel architectures that may result in more stable and efficient GANs for phylogenetics. Availability and implementationphyloGAN is available on github: https://github.com/meganlsmith/phyloGAN/. 

Abstract Traditionally, single-copy orthologs have been the gold standard in phylogenomics. Most phylogenomic studies identify putative single-copy orthologs using clustering approaches and retain families with a single sequence per species. This limits the amount of data available by excluding larger families. Recent advances have suggested several ways to include data from larger families. For instance, tree-based decomposition methods facilitate the extraction of orthologs from large families. Additionally, several methods for species tree inference are robust to the inclusion of paralogs and could use all of the data from larger families. Here, we explore the effects of using all families for phylogenetic inference by examining relationships among 26 primate species in detail and by analyzing five additional data sets. We compare single-copy families, orthologs extracted using tree-based decomposition approaches, and all families with all data. We explore several species tree inference methods, finding that identical trees are returned across nearly all subsets of the data and methods for primates. The relationships among Platyrrhini remain contentious; however, the species tree inference method matters more than the subset of data used. Using data from larger gene families drastically increases the number of genes available and leads to consistent estimates of branch lengths, nodal certainty and concordance, and inferences of introgression in primates. For the other data sets, topological inferences are consistent whether single-copy families or orthologs extracted using decomposition approaches are analyzed. Using larger gene families is a promising approach to include more data in phylogenomics without sacrificing accuracy, at least when high-quality genomes are available. 

Abstract Phylogenetics has long relied on the use of orthologs, or genes related through speciation events, to infer species relationships. However, identifying orthologs is difficult because gene duplication can obscure relationships among genes. Researchers have been particularly concerned with the insidious effects of pseudoorthologs—duplicated genes that are mistaken for orthologs because they are present in a single copy in each sampled species. Because gene tree topologies of pseudoorthologs may differ from the species tree topology, they have often been invoked as the cause of counterintuitive results in phylogenetics. Despite these perceived problems, no previous work has calculated the probabilities of pseudoortholog topologies or has been able to circumscribe the regions of parameter space in which pseudoorthologs are most likely to occur. Here, we introduce a model for calculating the probabilities and branch lengths of orthologs and pseudoorthologs, including concordant and discordant pseudoortholog topologies, on a rooted three-taxon species tree. We show that the probability of orthologs is high relative to the probability of pseudoorthologs across reasonable regions of parameter space. Furthermore, the probabilities of the two discordant topologies are equal and never exceed that of the concordant topology, generally being much lower. We describe the species tree topologies most prone to generating pseudoorthologs, finding that they are likely to present problems to phylogenetic inference irrespective of the presence of pseudoorthologs. Overall, our results suggest that pseudoorthologs are unlikely to mislead inferences of species relationships under the biological scenarios considered here.[Birth–death model; orthologs; paralogs; phylogenetics.] 

Abstract Many recent phylogenetic methods have focused on accurately inferring species trees when there is gene tree discordance due to incomplete lineage sorting (ILS). For almost all of these methods, and for phylogenetic methods in general, the data for each locus are assumed to consist of orthologous, single-copy sequences. Loci that are present in more than a single copy in any of the studied genomes are excluded from the data. These steps greatly reduce the number of loci available for analysis. The question we seek to answer in this study is: what happens if one runs such species tree inference methods on data where paralogy is present, in addition to or without ILS being present? Through simulation studies and analyses of two large biological data sets, we show that running such methods on data with paralogs can still provide accurate results. We use multiple different methods, some of which are based directly on the multispecies coalescent model, and some of which have been proven to be statistically consistent under it. We also treat the paralogous loci in multiple ways: from explicitly denoting them as paralogs, to randomly selecting one copy per species. In all cases, the inferred species trees are as accurate as equivalent analyses using single-copy orthologs. Our results have significant implications for the use of ILS-aware phylogenomic analyses, demonstrating that they do not have to be restricted to single-copy loci. This will greatly increase the amount of data that can be used for phylogenetic inference.[Gene duplication and loss; incomplete lineage sorting; multispecies coalescent; orthology; paralogy.] 

Abstract Pleistocene glacial cycles drastically changed the distributions of taxa endemic to temperate rainforests in the Pacific Northwest, with many experiencing reduced habitat suitability during glacial periods. In this study, we investigate whether glacial cycles promoted intraspecific divergence and whether subsequent range changes led to secondary contact and gene flow. For seven invertebrate species endemic to the PNW, we estimated species distribution models (SDMs) and projected them onto current and historical climate conditions to assess how habitat suitability changed during glacial cycles. Using single nucleotide polymorphism (SNP) data from these species, we assessed population genetic structure and used a machine‐learning approach to compare models with and without gene flow between populations upon secondary contact after the last glacial maximum (LGM). Finally, we estimated divergence times and rates of gene flow between populations. SDMs suggest that there was less suitable habitat in the North Cascades and Northern Rocky Mountains during glacial compared to interglacial periods, resulting in reduced habitat suitability and increased habitat fragmentation during the LGM. Our genomic data identify population structure in all taxa, and support gene flow upon secondary contact in five of the seven taxa. Parameter estimates suggest that population divergences date to the later Pleistocene for most populations. Our results support a role of refugial dynamics in driving intraspecific divergence in the Cascades Range. In these invertebrates, population structure often does not correspond to current biogeographic or environmental barriers. Rather, population structure may reflect refugial lineages that have since expanded their ranges, often leading to secondary contact between once isolated lineages.