Biology subjects

Hahn, M. W.

Publications and source records attributed to Hahn, M. W..

At least 37 records · Page 2Linked to original sources

bioRxiv2023

Robust inference of historical human generation times

Ragsdale and Thornton (2023) raise concerns about our recent estimates of historical human generation times, concluding that our results were "predominantly driven by nonbiological artifacts." While we believe these authors have pointed out several important sources of uncertainty, we show here that their main concerns are either not relevant to our study or support our conclusions as much as they cast doubt on them. In particular, the demographic simulations carried out by Ragsdale and Thornton assume all individuals with recent African ancestry are from West Africa, which is not appropriate for our sample. In contrast to the lack of visual concordance between predictions and data cited by these authors as evidence for a lack of fit, we demonstrate that our model provides a good statistical fit to data on the overall historical mutation spectrum, though one particular mutation type is an outlier. Furthermore, we show that the historical generation times inferred when using alternative methods for estimating the ages of individual alleles are largely in agreement with our results, particularly so when using results from Relate. Importantly, these analyses, as well as recent work from an independent group, confirm the idea that a model built on de novo mutations and applied to polymorphism data provides useful and reliable estimates of generation times in widely distant mammals.

evolutionary biology↗

bioRxiv2023

Geographic population structure and distinct population dynamics of globally abundant freshwater bacteria

Geographic separation is a principal factor for structuring populations of macroorganisms, with important consequences for evolution, by means of processes such as allopatric speciation. For free-living prokaryotes, implications of geographic separation on their evolution are more unclear. The limited phylogenetic resolution of commonly used markers such as 16S rRNA gene sequences have since long impeded prokaryotic population genetics. However, the vast amount of metagenome sequencing data generated during the last decades from various habitats around the world, now provides an excellent opportunity for such investigations. Here we exploited publicly available and new freshwater metagenomes in combination with genomes of abundant freshwater bacteria to study the impact of geographic separation on population structure. We focused on species that were detected across broad geographic ranges at high enough sequence coverage for meaningful population genomic analyses, i.e. members of the predominant freshwater taxa acI, LD12, Polynucleobacter and Ca. Methylopumilus. Population differentiation increased significantly with spatial distance in all species, but notable dispersal barriers (e.g. oceanic) were not apparent. Yet, the different species showed contrasting rates of geographic divergence and strikingly different population dynamics in time series within individual lakes. While certain populations hardly diverged over several years, others displayed high divergence after merely a few months, similar in scale to populations separated by thousands of kilometers. We speculate that populations with higher strain diversity evolve more monotonously, while low strain diversity enables more drastic clonal expansion of genotypes which will be reflected in strong but transient differentiation between temporally or spatially adjacent populations.

evolutionary biology↗

bioRxiv2022

Phylogenetic inference using Generative Adversarial Networks

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. phy-loGAN 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 fifteen taxa in the concatenation case and six 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. AvailabilityphyloGAN is available on github: https://github.com/meganlsmith/phyloGAN/. Contactmls16@indiana.edu

evolutionary biology↗

bioRxiv2022

CAGEE: computational analysis of gene expression evolution

Despite the increasing abundance of whole transcriptome data, few methods are available to analyze global gene expression across phylogenies. Here, we present a new software package (CAGEE) for inferring patterns of increases and decreases in gene expression across a phylogenetic tree, as well as the rate at which these changes occur. In contrast to previous methods that treat each gene independently, CAGEE can calculate genome-wide rates of gene expression, along with ancestral states for each gene. The statistical approach developed here makes it possible to infer lineage-specific shifts in rates of evolution across the genome, in addition to possible differences in rates among multiple tissues sampled from the same species. We demonstrate the accuracy and robustness of our method on simulated data, and apply it to a dataset of ovule gene expression collected from multiple self-compatible and self-incompatible species in the genus Solanum to test hypotheses about the evolutionary forces acting during mating system shifts. These comparisons allow us to highlight the power of CAGEE, demonstrating its utility for use in any empirical system and for the analysis of most morphological traits. Our software is available at https://github.com/hahnlab/CAGEE/.

bioinformatics↗

bioRxiv2022

Phylogenomic comparative methods: accurate evolutionary inferences in the presence of gene tree discordance

Phylogenetic comparative methods have long been a mainstay of evolutionary biology, allowing for inferences of the tempo and mode of trait evolution across species while accounting for their common ancestry. These analyses typically assume a single, bifurcating phylogenetic tree that describes the shared history among species. However, modern phylogenomic analyses have shown that genomes are often composed of a mosaic of different histories that can disagree both with the species tree and with each other--so-called discordant gene trees. These gene trees describe shared histories that are not captured by the species tree, and therefore that are unaccounted for in classic comparative approaches. The application of standard phylogenetic comparative methods to species histories containing discordance leads to incorrect inferences about the timing, direction, and rate of evolution. Here, we develop two approaches for incorporating gene tree histories into comparative methods: one involves constructing a fuller phylogenetic variance-covariance matrix that includes relationships not found in the species tree, and another that applies Felsensteins pruning algorithm over a set of gene trees to calculate trait histories and likelihoods. Both approaches are agnostic to the biological causes of gene tree discordance, which may include incomplete lineage sorting and introgression. Using simulation, we demonstrate that our new approaches generate much more accurate estimates of tree-wide rates of trait evolution than standard methods. We apply our methods to two clades of the wild tomato genus Solanum with varying rates of discordance, demonstrating the contribution of gene tree discordance to variation in a set of floral traits and the ability of our approaches to provide more accurate inferences. Our new approaches have the potential to be applied to a broad range of classic inference problems in phylogenetics, including ancestral state reconstruction and the inference of lineage-specific rate shifts.

evolutionary biology↗

bioRxiv2022

Accurate Detection of Incomplete Lineage Sorting via Supervised Machine Learning

Gene tree discordance due to incomplete lineage sorting or introgression has been described in numerous genomic datasets. Among distantly related taxa, however, it is difficult to differentiate these biological sources of discordance from discordance due to errors in gene tree reconstruction, even when supervised machine learning techniques are used to infer individual gene trees. Here, rather than applying machine learning to the problem of inferring single tree topologies, we develop a model to infer important properties of a particular internal branch of the species tree via genome-scale summary statistics extracted from individual alignments and inferred gene trees. We show that our model can effectively predict the presence/absence of discordance, estimate the probability of discordance, and infer the correct species tree topology in the presence of multiple, common sources of error. While gene tree topology counts are the most salient predictors of discordance at short time scales, other genomic features become relevant for distantly related species. We validate our approach through simulation, and apply it to data from the deepest splits among metazoans. Our results suggest that the base of Metazoa experienced significant gene tree discordance, implying that discordant traits among current taxa can be explained without invoking homoplasy. In addition, we find support for Porifera as the sister clade to the rest of Metazoa. Overall, these results demonstrate how machine learning can be used to answer important phylogenetic questions, while marginalizing over individual gene tree--and even species tree--topologies.

bioinformatics↗

bioRxiv2022

MAST: Phylogenetic Inference with Mixtures Across Sites and Trees

Hundreds or thousands of loci are now routinely used in modern phylogenomic studies. Concatenation approaches to tree inference assume that there is a single topology for the entire dataset, but different loci may have different evolutionary histories due to incomplete lineage sorting, introgression, and/or horizontal gene transfer; even single loci may not be treelike due to recombination. To overcome this shortcoming, we introduce the mixture across sites and trees (MAST) model, which uses a mixture of bifurcating trees to represent multiple histories in a single concatenated alignment. The MAST model allows each tree to have its own topology, branch lengths, substitution model, nucleotide or amino acid frequencies, and model of rate heterogeneity across sites. We implemented the MAST model in a maximum-likelihood framework in the popular phylogenetic software, IQ-TREE. Simulations show that we can accurately recover the true model parameters, including branch lengths and tree weights (i.e. frequencies) for a given set of tree topologies. We also show that we can use standard statistical inference approaches to reject a single-tree model when data are simulated under multiple trees (and vice versa). We applied the MAST model to multiple primate datasets and found that it can recover the signal of incomplete lineage sorting in the Great Apes, as well as the asymmetry in minor trees caused by introgression among several macaque species. When applied to a dataset of four Platyrrhine species for which standard concatenated maximum likelihood and gene tree approaches disagree, we find that MAST gives the highest weight to the tree favored by gene tree approaches. These results suggest that the MAST model is able to analyse a concatenated alignment using maximum likelihood, while avoiding some of the biases that come with assuming there is only a single tree. The MAST model can therefore offer unique biological insights when applied to datasets with multiple evolutionary histories. We discuss how it can be extended in the future.

bioinformatics↗

bioRxiv2022

Updated site concordance factors minimize effects of homoplasy and taxon sampling

MotivationSite concordance factors (sCFs) have become a widely used way to summarize discordance in phylogenomic datasets. However, the original version of sCFs were calculated by sampling a quartet of tip taxa and then applying parsimony-based criteria for discordance. This approach has the potential to be strongly affected by multiple hits at a site (homoplasy), especially when substitution rates are high or taxa are not closely related. ResultsHere, we introduce a new method for calculating site concordance factors. The updated version uses likelihood to generate probability distributions of ancestral states at internal nodes of the phylogeny. By sampling from the states at internal nodes adjacent to a given branch, this approach substantially reduces--but does not completely abolish--the effects of homoplasy and taxon sampling. Availability and implementationUpdated sCFs are implemented in IQ-TREE 2.2.2. The software is freely available at https://github.com/iqtree/iqtree2/releases. Contactmoyu@iu.edu

bioinformatics↗

bioRxiv2022

Distinguishing between histories of speciation and introgression using genomic data

Introgression creates complex, non-bifurcating relationships among species. At individual loci and across the genome, both introgression and incomplete lineage sorting interact to produce a wide range of different gene tree topologies. These processes can obscure the history of speciation among lineages, and, as a result, identifying the history of speciation vs. introgression remains a challenge. Here, we use theory and simulation to investigate how introgression can mislead multiple approaches to species tree inference. We find that arbitrarily low amounts of introgression can mislead both gene tree methods and parsimony methods if the rate of incomplete lineage sorting is sufficiently high. We also show that an alternative approach based on minimum gene tree node heights is inconsistent and depends on the rate of introgression across the genome. To distinguish between speciation and introgression, we apply supervised machine learning models to a set of features that can easily be obtained from phylogenomic datasets. We find that multiple of these models are highly accurate in classifying the species history in simulated datasets. We also show that, if the histories of speciation and introgression can be identified, PhyloNet will return highly accurate estimates of the contribution of each history to the data (i.e. edge weights). Overall, our results highlight the promise of supervised machine learning as a potentially powerful complement to phylogenetic methods in the analysis of introgression from genomic data.

evolutionary biology↗

bioRxiv2022

Evaluating methods for estimating the proportion of adaptive amino acid substitutions

A long-standing debate in molecular evolution concerns the role of adaptation in shaping divergence between species. A number of approaches have been developed to estimate the proportion of amino acid substitutions between species () that are driven by adaptive natural selection. These methods vary in the type of data they use and in the modeling strategies they employ in their inference. In this study, we evaluate the accuracy of nine different methods for estimating , using data simulated in the presence of linked selection. We find that methods that model the distribution of fitness effect (DFE) of both deleterious (as a gamma distribution) and beneficial mutations (as a gamma or exponential distribution) are the most accurate. We applied these methods to whole-genome data, finding that the most accurate methods gave average values of =0.25 in Arabidopsis thaliana, 0.5 in Drosophila melanogaster, and 0.1 in Homo sapiens. We also applied these methods to analyze subsets of tissue-specific genes in A. thaliana that are believed to be under different selective pressures and on genes found on the X vs. autosomes in D. melanogaster. We find estimates of to be higher in the seeds than in other specialized organs, supporting inferences of conflict-driven adaptive evolution in genes expressed in the seed; we also find to be higher on the X chromosome, supporting previous inferences of faster-X evolution. Overall, our results suggest that there are multiple methods that provide accurate estimates of , providing a guide for future estimates of adaptive evolution.

evolutionary biology↗

bioRxiv2022

Hibernation shows no apparent effect on germline mutation rates in grizzly bears

A male mutation bias is observed across vertebrates, and, where data are available, this bias is accompanied by increased per-generation mutation rates with parental age. While continuing mitotic cell division in the male germline post-puberty has been proposed as the major cellular mechanism underlying both patterns, little direct evidence for this role has been found. Understanding the evolution of the per-generation mutation rate among species requires that we identify the molecular mechanisms that change between species. Here, we study the per-generation mutation rate in an extended pedigree of the brown (grizzly) bear, Ursus arctos horribilis. Brown bears hibernate for one-third of the year, a period during which spermatogenesis slows or stops altogether. The cessation of spermatogenesis is predicted to lessen the male mutation bias and to lower the per-generation mutation rate in this species. However, using whole-genome sequencing, we find that both male bias and per-generation mutation rates are the same as expected for a non-hibernating species. We also carry out a phylogenetic comparison of substitution rates along the lineage leading to brown bear and panda (a non-hibernating species) and find no slowing of the substitution rate in the hibernator. Our results contribute to accumulating evidence that suggests that male germline cell division is not the major determinant of mutation rates and mutation biases. The results also provide a quantitative basis for improved estimates of the timing of carnivore evolution.

evolutionary biology↗

bioRxiv2021

Using all gene families vastly expands data available for phylogenomic inference in primates

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 datasets. 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 datasets, 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.

evolutionary biology↗

bioRxiv2021

Human generation times across the past 250,000 years

The generation times of our recent ancestors can tell us about both the biology and social organization of prehistoric humans, placing human evolution on an absolute timescale. We present a method for predicting historic male and female generation times based on changes in the mutation spectrum. Our analyses of whole-genome data reveal an average generation time of 26.9 years across the past 250,000 years, with fathers consistently older (30.7 years) than mothers (23.2 years). Shifts in sex-averaged generation times have been driven primarily by changes to the age of paternity rather than maternity, though we report a disproportionate increase in female generation times over the past several thousand years. We also find a large difference in generation times among populations, with samples from current African populations showing longer ancestral generation times than non-Africans for over a hundred thousand years, reaching back to a time when all humans occupied Africa.

evolutionary biology↗

bioRxiv2021

Mutationathon: towards standardization in estimates of pedigree-based germline mutation rates

In the past decade, several studies have estimated the human per-generation germline mutation rate using large pedigrees. More recently, estimates for various non-human species have been published. However, methodological differences among studies in detecting germline mutations and estimating mutation rates make direct comparisons difficult. Here, we describe the many different steps involved in estimating pedigree-based mutation rates, including sampling, sequencing, mapping, variant calling, filtering, and how to appropriately account for false-positive and false-negative rates. For each step, we review the different methods and parameter choices that have been used in the recent literature. Additionally, we present the results from a "Mutationathon", a competition organized among five research labs to compare germline mutation rate estimates for a single pedigree of rhesus macaques. We report almost a two-fold variation in the final estimated rate among groups using different post-alignment processing, calling, and filtering criteria and provide details into the sources of variation across studies. Though the difference among estimates is not statistically significant, this discrepancy emphasizes the need for standardized methods in mutation rate estimations and the difficulty in comparing rates from different studies. Finally, this work aims to provide guidelines for computational and statistical benchmarks for future studies interested in identifying germline mutations from pedigrees.

evolutionary biology↗

bioRxiv2021

The effects of introgression across thousands of quantitative traits revealed by gene expression in wild tomatoes

It is now understood that introgression can serve as powerful evolutionary force, providing genetic variation that can shape the course of trait evolution. Introgression also induces a shared evolutionary history that is not captured by the species phylogeny, potentially complicating evolutionary analyses that use a species tree. Such analyses are often carried out on gene expression data across species, where the measurement of thousands of trait values allows for powerful inferences while controlling for shared phylogeny. Here, we present a Brownian motion model for quantitative trait evolution under the multispecies network coalescent framework, demonstrating that introgression can generate apparently convergent patterns of evolution when averaged across thousands of quantitative traits. We test our theoretical predictions using whole-transcriptome expression data from ovules in the wild tomato genus Solanum. Examining two sub-clades that both have evidence for post-speciation introgression, but that differ substantially in its magnitude, we find patterns of evolution that are consistent with histories of introgression in both the sign and magnitude of ovule gene expression. Additionally, in the sub-clade with a higher rate of introgression, we observe a correlation between local gene tree topology and expression similarity, implicating a role for introgressed cis-regulatory variation in generating these broad-scale patterns. Our results have important implications for the study of gene expression evolution--and quantitative trait evolution more broadly--in systems with evidence of introgression.

evolutionary biology↗

bioRxiv2021

De novo mutations in domestic cat are consistent with an effect of reproductive longevity on both the rate and spectrum of mutations

The mutation rate is a fundamental evolutionary parameter with direct and appreciable effects on the health and function of individuals. Here, we examine this important parameter in the domestic cat, a beloved companion animal as well as a valuable biomedical model. We estimate a mutation rate of 0.86 x 10-8 per bp per generation for the domestic cat (at an average parental age of 3.8 years). We find evidence for a significant paternal age effect, with more mutations transmitted by older sires. Our analyses suggest that the cat and the human have accrued similar numbers of mutations in the germline before reaching sexual maturity. The per-generation mutation rate in the cat is 28% lower than what has been observed in humans, but is consistent with the shorter generation time in the cat. Using a model of reproductive longevity, which takes into account differences in the reproductive age and time to sexual maturity, we are able to explain much of the difference in per-generation rates between species. We further apply our reproductive longevity model in a novel analysis of mutation spectra and find that the spectrum for the cat resembles the human mutation spectrum at a younger age of reproduction. Together, these results implicate changes in life-history as a driver of mutation rate evolution between species. As the first direct observation of the paternal age effect outside of rodents and primates, our results also suggest a phenomenon that may be universal among mammals.

evolutionary biology↗

bioRxiv2021

The frequency and topology of pseudoorthologs

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 less of a problem for phylogenetics than currently believed, which should allow researchers to greatly increase the number of genes used in phylogenetic inference. Significance StatementPhylogenetics has long relied on the use of orthologs, or genes related through speciation events, to infer species relationships. However, filtering datasets to include only orthologs is both difficult and restrictive, drastically limiting the amount of data available for phylogenetic inference. Here, we introduce a model to study the probability and topologies of pseudoorthologs--duplicated genes that are mistaken for orthologs because they are present in a single copy in each sampled species. We show that pseudoorthologs are rare and that, even when they are present, they should not mislead phylogenetic inference. Our results suggest that strict filtering to remove pseudoorthologs unnecessarily limits the amount of data used in phylogenetic inference.

evolutionary biology↗

bioRxiv2020

High-Resolution Phylogenetic and Population Genetic Analysis of Microbial Communities with RoC-ITS

Microbial communities are inter-connected systems of incredible complexity and dynamism that play crucial roles in health, energy, and the environment. To better understand microbial communities and how they respond to change, it is important to know which microbes are present and their relative abundances at the greatest taxonomic resolution possible. Here, we describe a novel protocol (RoC-ITS) that uses the single-molecule Nanopore sequencing platform to assay the composition of microbial communities in unprecedented detail. This methodology produces long-read sequences including multiple copies of the same complete 16S ribosomal gene and its neighboring internally transcribed spacer (ITS) using rolling-circle amplification. The ribosomal 16S gene provides phylogenetic information down to the species-level, while the much less conserved ITS region contains strain-level information. When linked together, this combination of markers allows for the identification of individual ribosomal units within a specific organism, the assessment of their relative stoichiometry, and the ability to monitor subtle shifts in microbial community composition with a single generic assay. We applied RoC-ITS to a mock microbial community that was also sequenced using the Illumina platform, demonstrating its accuracy in quantifying the relative abundance and identity of each species.

genomics↗