Biology subjects

Ane, C.

Publications and source records attributed to Ane, C..

13 recordsLinked to original sources

bioRxiv2026

Statistical inference of the Tree of Blobs of a phylogenetic network from quartet concordance factors

A phylogenetic network represents evolutionary relationships involving hybridization, gene flow, or admixture. While the full network may not be identifiable from genomic data under common coalescent models, its tree of blobs, depicting only the tree-like portions of the network structure, is. We introduce ECToBlob (Edge Contraction for Tree of Blobs), a new statistically-consistent algorithm to estimate the tree of blobs from quartet concordance factors. Starting from a resolved tree, ECToBlob successively contracts edges which statistical tests indicate do not belong in the tree of blobs, due to reticulate or polytomous signal. We show that ASTRAL provides a valid starting tree under common assumptions, in that, asymptotically in the number of loci, trees optimizing ASTRALs criterion refine the tree of blobs. We describe several algorithm variants, differing in how evidence from multiple tests are combined to determine if the edge should be contracted, and provide software implementations. Relevance to Life SciencesHybridization, gene flow, or admixture are now recognized as important aspects of evolutionary history, but their genomic signal is confounded with that from a coalescent process, creating substantial challenges for inferring phylogenetic networks. The networks tree of blobs identifies areas where reticulation occurred, separated by tree-like branching. ECToBlob quickly estimates the tree of blobs using quartet concordance factors from gene trees, and provides a measure of statistical support for its result. Performance is illustrated through simulation and on empirical data, using an implementation in the R package MSCquartets. While the presence of a blob may be all that can be inferred in some cases, in others ECToBlob offers a robust and principled way to focus further analyses on more local reticulate structure. Mathematical ContentThis work makes contributions to mathematical phylogenetics in optimization, combinatorics, and statistics. We show that any tree maximizing quartet support (the criterion underlying ASTRAL) is a refinement of the networks tree of blobs under the coalescent model. Second, we give a concise proof that whether a network has a cut-edge corresponding to a given split is determined by information in certain subcollections of its 4-taxon subnetworks (quarnets). Finally, we propose valid statistical approaches for combining p-values across multiple quarnet hypothesis tests, proving that their use with specific decreasing test levels leads to statistically consistent inference as the number of loci grows. MSC codes05C90, 60J95, 62-04, 62F07, 92D15

evolutionary biology↗

bioRxiv2026

Substitution rate variation, not hidden paralogy, drives false hybridization signal in phylogenetic network inference

Phylogenetic network inference methods are increasingly used to detect hybridization and gene flow from genomic data, but their robustness to common sources of model violation remains poorly characterized. We conducted a simulation study to evaluate the effects of hidden paralogy and substitution rate variation on two widely used network inference methods: find_graphs from ADMIXTOOLS 2 and SNaQ. Using an eight-taxon species tree calibrated from an empirical reptile phylogeny, we simulated data under various levels of hidden paralogy (from none to strong) and three levels of rate variation (none, gene-specific, and lineage-specific). We found that hidden paralogy had limited impact on network inference under the conditions examined: both network methods correctly favored a tree without reticulation, and ASTRAL recovered the correct species tree every time. In contrast, lineage-specific rates severely biased find_graphs, inflating worst f-statistic residuals well beyond the standard acceptance threshold. SNaQ correctly selected a tree model almost always across all conditions, though its network with h = 1 reticulation displayed the true species tree with a lower probability under lineage-specific rates. We also show that the standard worst residuals threshold of 3 for find_graphs produces inflated type I error even without rate variation, and we recommend empirical calibration of this threshold within each study system.

evolutionary biology↗

bioRxiv2026

Trait evolution with incomplete lineage sorting and gene flow: the Gaussian Coalescent model

Most phylogenetic comparative methods use a species-level phylogeny, ignoring the effect of incomplete lineage sorting (ILS) and hemiplasy on the traits of interest. We consider here a trait controlled additively by one or more unknown loci. Their gene trees may differ from the species phylogeny due to ILS. The species phylogeny can be any phylogenetic tree or network or admixture graph on any number of taxa or populations. Our process of trait evolution accounts for introgression represented in the phylogeny by hybrid (or admixture) edges, which capture gene flow, admixture or hybridization events. The process models the trait in each individual, for any number of individuals from each taxon. Our model allows for polymorphism in the ancestral population at the root of the species phylogeny, and we can derive the heritable within-population variation expected from our model. Even if each locus evolves according to a Brownian motion, the joint distribution of the trait across all measured individuals is not generally Gaussian due to ILS. We provide a Gaussian approximation, named the Gaussian Coalescent (GC), and show how to compute its variance matrix efficiently using a single traversal of the species phylogeny. In simulations, this model is much more accurate than the model ignoring ILS. In simulations and on a data set of tomato floral traits, it is favored over the standard Brownian motion model with extra within-population variance. The GC model opens new avenues for various phylogenetic methods, accounting for hemiplasy and gene flow simultaneously on any general network or admixture graph. It is implemented in phylolm v2.7.0 and in PhyloTraits v1.2.0.

evolutionary biology↗

bioRxiv2025

SNaQ.jl: Improved Scalability for Phylogenetic Network Inference

Phylogenetic networks represent complex biological scenarios that are overlooked in trees, such as hybridization and horizontal gene transfer. Although numerous methods have been developed for phylogenetic network inference, their scalability is severely limited by the computational demands of likelihood optimization and the vastness of network space. Composite (or pseudo-) likelihood approaches like SNaQ have improved computational tractability for network inference, but they remain inadequate for datasets of sizes routinely handled by tree inference methods. Here, we introduce SNaQ.jl, a new standalone Julia package with the composite likelihood inference originally implemented within PhyloNetworks.jl as well as new scalability features that enhance computational efficiency through (1) parallelization of quartet likelihood calculations during composite likelihood computation, (2) weighted random selection of quartets, and (3) probabilistic decision-making during network search. Through a simulation study and empirical data analysis, we show that this new version of SNaQ.jl (version 1.1) improves average runtimes by up to 499% on average with no change in function parameters or method accuracy.

evolutionary biology↗

bioRxiv2025

Ecological constraints and evolutionary trade-offs shape nitrogen fixation across habitats

From its earliest beginnings, lifes expansion into new habitats has been profoundly shaped by its reciprocal interactions with Earths changing environments. Understanding how ancient metabolisms co-evolved with their environments requires uncovering the ecological and evolutionary processes that structured the functionally linked genes and networks underlying these metabolisms. Here, we focus on nitrogen (N2) fixation, one of lifes most critical metabolisms, and investigate the drivers of complexity in its associated gene machinery today. We used a large-scale comparative genomics framework to construct a comprehensive catalog of extant N2 fixation-associated genes and assessed their distribution across diverse microbial genomes and environmental backgrounds. Genomes enriched in N2 fixation genes generally have larger genome sizes, broader metabolic capabilities, wider habitat ranges, and are predominantly associated with mesophilic and aerobic lifestyles. Evolutionary reconstructions reveal a pattern of early gene gains in ancestral diazotrophs followed by lineage-specific gene losses in later diverging taxa, suggesting evolutionary trade-offs shaped by changing environments. These findings demonstrate that the evolution of N2 fixation has been intertwined with the composition and organization of the genes supporting the overarching N2 metabolism, driven by feedback between genome evolution and shifting environmental and ecological conditions.

microbiology↗

bioRxiv2025

Low accuracy of complex admixture graph inference from f-statistics

F-statistics are commonly used to assess hybridization, admixture or introgression between populations or deeper evolutionary lineages. Using simulations, we find that network complexity had a large impact on the accuracy to infer the network structure from f statistics. Networks recovered accurately had one reticulation, or had their reticulations in "large" cycles of at least 4 nodes in all subnetworks. But accuracy was extremely poor to infer complex networks, in which a reticulation is part of a small cycle of only 3 nodes in some subnetwork. Accuracy also decreased with increasing number of reticulations and the network level. For these networks, accuracy was low even from large data sets with low mutation rate, under a molecular clock, and retaining many top-scoring graphs. Yet in all cases, the networks major tree was recovered reliably. When the molecular clock was violated, the f4-test tended to falsely detect the presence of reticulation in large data sets or under a high mutation rate. Rate variation also impacted network inference accuracy and increased the rate of falsely rejecting 1 reticulation as being adequate. We propose that identifiability, or lack thereof, is underlying the contrasting recoverability between simple and complex networks. Our findings suggest that the major tree is one feature that might be estimable from f-statistics. In practice, we recommend evaluating a large set of top-scoring networks inferred from f-statistics, and even so, using caution in assuming that the true network is part of this set. The extent of rate variation should be assessed in the system under study, especially at deeper time scales, or when using fast-evolving loci.

evolutionary biology↗

bioRxiv2023

Anomalous networks under the multispecies coalescent: theory and prevalence

AO_SCPLOWBSTRACTC_SCPLOWReticulations in a phylogenetic network represent processes such as gene flow, admixture, recombination and hybrid speciation. Extending definitions from the tree setting, an anomalous network is one in which some unrooted tree topology displayed in the network appears in gene trees with a lower frequency than a tree not displayed in the network. We investigate anomalous networks under the Network Multispecies Coalescent Model with possible correlated inheritance at reticulations. Focusing on subsets of 4 taxa, we describe a new algorithm to calculate quartet concordance factors on networks of any level, faster than previous algorithms because of its focus on 4 taxa. We then study topological properties required for a 4-taxon network to be anomalous, uncovering the key role of 32-cycles: cycles of 3 edges parent to a sister group of 2 taxa. Under the model of common inheritance, that is, when each gene tree coalesces within a species tree displayed in the network, we prove that 4-taxon networks are never anomalous. Under independent and various levels of correlated inheritance, we use simulations under realistic parameters to quantify the prevalence of anomalous 4-taxon networks, finding that truly anomalous networks are rare. At the same time, however, we find a significant fraction of networks close enough to the anomaly zone to appear anomalous, when considering the quartet concordance factors observed from a few hundred genes. These apparent anomalies may challenge network inference methods.

evolutionary biology↗

bioRxiv2023

Summary tests of introgression are highly sensitive to rate variation across lineages

AO_SCPLOWBSTRACTC_SCPLOWThe evolutionary implications and frequency of hybridization and introgression are increasingly being recognized across the tree of life. To detect hybridization from multi-locus and genome-wide sequence data, a popular class of methods are based on summary statistics from subsets of 3 or 4 taxa. However, these methods often carry the assumption of a constant substitution rate across lineages and genes, which is commonly broken in many groups. In this work, we quantify the effects of rate variation on the D-statistic (also known as ABBA-BABA test), the D3 statistic, and HyDe. All three tests are used widely across a range of taxonomic groups, in part because they are very fast to compute. We consider rate variation across species lineages, across genes, their lineage-by-gene interaction, and rate variation across gene-tree edges. We simulated species networks according to a birth-death-hybridization process so as to capture a range of realistic species phylogenies. For all three methods tested, we found a marked increase in the false discovery of reticulation (type-1 error rate) when there is rate variation across species lineages. The D3 statistic was the most sensitive, with around 80% type-1 error, such that D3 appears to more sensitive to a departure from the clock than to the presence of reticulation. For all three tests, the power to detect hybridization events decreased as the number of hybridization events increased, indicating that multiple hybridization events can "hide" one another if they occur within a small subset of taxa. Our study highlights the need to consider rate variation when using site-based summary statistics, and points to the advantages of methods that do not require assumptions on evolutionary rates across lineages or across genes.

evolutionary biology↗

bioRxiv2023

PhyloCoalSimulations: A simulator for network multispecies coalescent models, including a new extension for the inheritance of gene flow

AO_SCPLOWBSTRACTC_SCPLOWWe consider the evolution of phylogenetic gene trees along phylogenetic species networks, according to the network multispecies coalescent process, and introduce a new network coalescent model with correlated inheritance of gene flow. This model generalizes two traditional versions of the network coalescent: with independent or common inheritance. At each reticulation, multiple lineages of a given locus are inherited from parental populations chosen at random, either independently across lineages, or with positive correlation according to a Dirichlet process. This process may account for locus-specific probabilities of inheritance, for example. We implemented the simulation of gene trees under these network coalescent models in the Julia package PhyloCoalSimulations, which depends on PhyloNetworks and its powerful network manipulation tools. Input species phylogenies can be read in extended Newick format, either in numbers of generations or in coalescent units. Simulated gene trees can be written in Newick format, and in a way that preserves information about their embedding within the species network. This embedding can be used for downstream purposes, such as to simulate species-specific processes like rate variation across species, or for other scenarios as illustrated in this note. This package should be useful for simulation studies and simulation-based inference methods. The software is available open source with documentation and a tutorial at https://github.com/cecileane/PhyloCoalSimulations.jl.

evolutionary biology↗

bioRxiv2022

Accounting for intraspecific variation in continuous trait evolution on a reticulate phylogeny

Within-species trait variation may be the result of genetic variation, environmental variation or measurement error for example. In phylogenetic comparative studies, failing to account for within-species variation has many adverse effects, such as increased error in testing hypotheses about evolutionary correlations, biased estimates of evolutionary rates, and inaccurate inference of the mode of evolution. These adverse effects were demonstrated in studies that considered a tree-like underlying phylogeny. Comparative methods on phylogenetic networks are still in their infancy. The impact of within-species variation on network-based methods has not been studied. Here, we introduce a phylogenetic linear model in which the phylogeny can be a network, to account for within-species variation in the continuous response trait assuming equal within-species variances across species. We show how inference based on the individual values can be reduced to a problem using species-level summaries, even when the within-species variance is estimated. Our method performs well under various simulation settings, and is robust when within-species variances are unequal across species. When phenotypic (within-species) correlations differ from evolutionary (between-species) correlations, estimates of evolutionary coefficients are pulled towards the phenotypic coefficients, for all methods we tested. Also, evolutionary rates are either underestimated or overestimated, depending on the mismatch between phenotypic and evolutionary relationships. We applied our method to morphological and geographical data from Polemonium. We find a strong negative correlation of leaflet size with elevation, despite a positive correlation within species. Our method can explore the role of gene flow in trait evolution, by comparing the fit of a network to that of a tree. We find marginal evidence for leaflet size being affected by gene flow, and support for previous observations on the challenges of using individual continuous traits to infer inheritance weights at reticulations. Our method is freely available in the Julia package PhyloNetworks.

evolutionary biology↗

bioRxiv2021

KIMGENS: A novel method to estimate kinship in organisms with mixed haploid diploid genetic systems robust to population structure

MotivationKinship estimation is necessary for evaluating violations of assumptions or testing certain hypotheses in many population genomic studies. However, kinship estimators are usually designed for diploid systems and cannot be used in populations with mixed haploid diploid genetic systems. The only estimators for different ploidies require datasets free of population structure, limiting their usage. ResultsWe present KIMGENS, an estimator for kinship estimation among individuals of various ploidies, that is robust to population structure. This estimator is based on the popular KING-robust estimator but uses diploid relatives of the individuals of interest as references of heterozygosity and extends its use to haploid-diploid and haploid pairs of individuals. We demonstrate that KIMGENS estimates kinship more accurately than previously developed estimators in simulated panmictic, structured and admixed populations, but has lower accuracy when the individual of interest is inbred. KIMGENS also outperforms other estimators in a honeybee dataset. Therefore, KIMGENS is a valuable addition to a population geneticists toolbox. Availability and ImplementationKIMGENS and its association simulation tool are implemented and available open-source at https://github.com/YenWenWang/HapDipKinship. ContactYen-Wen Wang Email: ywang883@wisc.edu

bioinformatics↗

bioRxiv2017

Phylogenetic Comparative Methods on Phylogenetic Networks with Reticulations

The goal of Phylogenetic Comparative Methods (PCMs) is to study the distribution of quantitative traits among related species. The observed traits are often seen as the result of a Brownian Motion (BM) along the branches of a phylogenetic tree. Reticulation events such as hybridization, gene flow or horizontal gene transfer, can substantially affect a species traits, but are not modeled by a tree. Phylogenetic networks have been designed to represent reticulate evolution. As they become available for downstream analyses, new models of trait evolution are needed, applicable to networks. One natural extension of the BM is to use a weighted average model for the trait of a hybrid, at a reticulation point. We develop here an efficient recursive algorithm to compute the phylogenetic variance matrix of a trait on a network, in only one preorder traversal of the network. We then extend the standard PCM tools to this new framework, including phylogenetic regression with covariates (or phylogenetic ANOVA), ancestral trait reconstruction, and Pagels {lambda} test of phylogenetic signal. The trait of a hybrid is sometimes outside of the range of its two parents, for instance because of hybrid vigor or hybrid depression. These two phenomena are rather commonly observed in present-day hybrids. Transgressive evolution can be modeled as a shift in the trait value following a reticulation point. We develop a general framework to handle such shifts, and take advantage of the phylogenetic regression view of the problem to design statistical tests for ancestral transgressive evolution in the evolutionary history of a group of species. We study the power of these tests in several scenarios, and show that recent events have indeed the strongest impact on the trait distribution of present-day taxa. We apply those methods to a dataset of Xiphophorus fishes, to confirm and complete previous analysis in this group. All the methods developed here are available in the Julia package PhyloNetworks.

evolutionary biology↗

bioRxiv2017

Inference of Adaptive Shifts for Multivariate Correlated Traits

To study the evolution of several quantitative traits, the classical phylogenetic comparative framework consists of a multivariate random process running along the branches of a phylogenetic tree. The Ornstein-Uhlenbeck (OU) process is sometimes preferred to the simple Brownian Motion (BM) as it models stabilizing selection toward an optimum. The optimum for each trait is likely to be changing over the long periods of time spanned by large modern phylogenies. Our goal is to automatically detect the position of these shifts on a phylogenetic tree, while accounting for correlations between traits, which might exist because of structural or evolutionary constraints. We show that, in the presence shifts, phylogenetic Principal Component Analysis (pPCA) fails to decorrelate traits efficiently, so that any method aiming at finding shift needs to deal with correlation simultaneously. We introduce here a simplification of the full multivariate OU model, named scalar OU (scOU), which allows for noncausal correlations and is still computationally tractable. We extend the equivalence between the OU and a BM on a re-scaled tree to our multivariate framework. We describe an Expectation Maximization algorithm that allows for a maximum likelihood estimation of the shift positions, associated with a new model selection criterion, accounting for the identifiability issues for the shift localization on the tree. The method, freely available as an R-package (PhylogeneticEM) is fast, and can deal with missing values. We demonstrate its efficiency and accuracy compared to another state-of-the-art method ({ell}1ou) on a wide range of simulated scenarios, and use this new framework to re-analyze recently gathered datasets on New World Monkeys and Anolis lizards.

evolutionary biology↗