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

Reconciling Gene Tree Discordance and Biogeography in European Crows

Reconstructing the evolutionary history of young lineages diverging with gene flow is challenging due to factors like incomplete lineage sorting, introgression, and selection causing gene tree discordance. The European crow hybrid zone between all-black carrion crows and grey-coated hooded crows exemplifies this challenge. Most of the genome in Western and Central European carrion crow populations is near-identical to hooded crows, but differs substantially from their Iberian congeners. A notable exception is a single major-effect colour-locus under sexual selection aligning with the 'species' tree. To understand the underlying evolutionary processes, we reconstructed the biogeographic history of the species complex. During the Pleistocene carrion and hooded crows took refuge in the Iberian Peninsula and the Middle East, respectively. Allele-sharing of all-black Western European populations with likewise black Iberian crows at the colour-locus represents the last trace of carrion crow ancestry, resisting gene flow from expanding hooded crow populations that have homogenised most of the genome. A model of colour-locus introgression from an Iberian ancestor into hooded crow populations near the Pyrenées was significantly less supported. We found no positive relationship between introgression and recombination rate consistent with the absence of genome-wide, polygenic barriers in this young species complex. Overall, this study portrays a scenario where few large-effect loci, subject to divergent sexual selection, resist rampant and asymmetric gene exchange. This study underscores the importance of integrating population demography and biogeography to accurately interpret patterns of gene tree discordance following population divergence.

A Tale of Too Many Trees: A Conundrum for Phylogenetic Regression

Just exactly which tree(s) should we assume when testing evolutionary hypotheses? This question has plagued comparative biologists for decades. Though all phylogenetic comparative methods require input trees, we seldom know with certainty whether even a perfectly estimated tree (if this is possible in practice) is appropriate for our studied traits. Yet, we also know that phylogenetic conflict is ubiquitous in modern comparative biology, and we are still learning about its dangers when testing evolutionary hypotheses. Here, we investigate the consequences of tree-trait mismatch for phylogenetic regression in the presence of gene tree-species tree conflict. Our simulation experiments reveal excessively high false positive rates for mismatched models with both small and large trees, simple and complex traits, and known and estimated phylogenies. In some cases, we find evidence of a directionality of error: assuming a species tree for traits that evolved according to a gene tree sometimes fares worse than the opposite. We also explored the impacts of tree choice using an expansive, cross-species gene expression dataset as an arguably "best-case" scenario in which one may have a better chance of matching tree with trait. Offering a potential path forward, we found promise in the application of a robust estimator as a potential, albeit imperfect, solution to some issues raised by tree mismatch. Collectively, our results emphasize the importance of careful study design for comparative methods, highlighting the need to fully appreciate the role of accurate and thoughtful phylogenetic modeling.

The Genomic Landscape, Causes, and Consequences of Extensive Phylogenomic Discordance in Murine Rodents

A species tree is a central concept in evolutionary biology whereby a single branching phylogeny reflects relationships among species. However, the phylogenies of different genomic regions often differ from the species tree. Although tree discordance is widespread in phylogenomic studies, we still lack a clear understanding of how variation in phylogenetic patterns is shaped by genome biology or the extent to which discordance may compromise comparative studies. We characterized patterns of phylogenomic discordance across the murine rodents-a large and ecologically diverse group that gave rise to the laboratory mouse and rat model systems. Combining recently published linked-read genome assemblies for seven murine species with other available rodent genomes, we first used ultraconserved elements (UCEs) to infer a robust time-calibrated species tree. We then used whole genomes to examine finer-scale patterns of discordance across ∼12 million years of divergence. We found that proximate chromosomal regions tended to have more similar phylogenetic histories. There was no clear relationship between local tree similarity and recombination rates in house mice, but we did observe a correlation between recombination rates and average similarity to the species tree. We also detected a strong influence of linked selection whereby purifying selection at UCEs led to appreciably less discordance. Finally, we show that assuming a single species tree can result in substantial deviation from the results with gene trees when testing for positive selection under different models. Collectively, our results highlight the complex relationship between phylogenetic inference and genome biology and underscore how failure to account for this complexity can mislead comparative genomic studies.

Convergent Evolution and Predictability of Gene Copy Numbers Associated with Diets in Mammals

Convergent evolution, the evolution of the same or similar phenotypes in phylogenetically independent lineages, is a widespread phenomenon in nature. If the genetic basis for convergent evolution is predictable to some extent, it may be possible to infer organismic phenotypes and the capability of organisms to utilize new ecological resources based on genome sequence data. While repeated amino acid changes have been studied in association with convergent evolution, relatively little is known about the potential contribution of repeated gene copy number changes. In this study, we explore whether gene copy number changes of particular gene families are linked to diet shifts in mammals and assess whether trophic ecology can be inferred from the copy numbers of a specific set of gene families. Using 86 mammalian genome sequences, we identified 24 gene families with a trend toward higher copy numbers in herbivores, carnivores, and omnivores, even after phylogenetic corrections. We were able to confirm previous findings on genes such as amylase, olfactory receptors, and xenobiotic metabolism genes, and identify novel gene families whose copy numbers correlate with dietary patterns. For example, omnivores exhibited higher copy numbers of genes encoding regulators of translation. We also established a discriminant function based on the copy numbers of 13 gene families that can help predict trophic ecology to some extent. These findings highlight a possible association between convergent evolution and repeated copy number changes in specific gene families, suggesting the potential to develop a method for predicting animal ecology from genome sequence data.

Subfamily evolution analysis using nuclear and chloroplast data from the same reads

The chloroplast (cp) genome is a widely used tool for exploring plant evolutionary relationships, yet its effectiveness in fully resolving these relationships remains uncertain. Integrating cp genome data with nuclear DNA information offers a more comprehensive view but often requires separate datasets. In response, we employed the same raw read sequencing data to construct cp genome-based trees and nuclear DNA phylogenetic trees using Read2Tree, a cost-efficient method for extracting conserved nuclear gene sequences from raw read data, focusing on the Aurantioideae subfamily, which includes Citrus and its relatives. The resulting nuclear DNA trees were consistent with existing nuclear evolutionary relationships derived from high-throughput sequencing, but diverged from cp genome-based trees. To elucidate the underlying complex evolutionary processes causing these discordances, we implemented an integrative workflow that utilized multiple alignments of each gene generated by Read2Tree, in conjunction with other phylogenomic methods. Our analysis revealed that incomplete lineage sorting predominantly drives these discordances, while introgression and ancient introgression also contribute to topological discrepancies within certain clades. This study underscores the cost-effectiveness of using the same raw sequencing data for both cp and nuclear DNA analyses in understanding plant evolutionary relationships.

The Meaning and Measure of Concordance Factors in Phylogenomics

As phylogenomic datasets have grown in size, researchers have developed new ways to measure biological variation and to assess statistical support for specific branches. Larger datasets have more sites and loci and therefore less sampling variance. While we can more accurately measure the mean signal in these datasets, lower sampling variance is often reflected in uniformly high measures of branch support-such as the bootstrap and posterior probability-limiting their utility. Larger datasets have also revealed substantial biological variation in the topologies found across individual loci, such that the single species tree inferred by most phylogenetic methods represents a limited summary of the data for many purposes. In contrast to measures of statistical support, the degree of underlying topological variation among loci should be approximately constant regardless of the size of the dataset. "Concordance factors" (CFs) and similar statistics have therefore become increasingly important tools in phylogenetics. In this review, we explain why CFs should be thought of as descriptors of topological variation rather than as measures of statistical support, and argue that they provide important information about the predictive power of the species tree not contained in measures of support. We review a growing suite of statistics for measuring concordance, compare them in a common framework that reveals their interrelationships, and demonstrate how to calculate them using an example from birds. We also discuss how measures of topological variation might change in the future as we move beyond estimating a single "tree of life" toward estimating the myriad evolutionary histories underlying genomic variation.

Unifying approaches from statistical genetics and phylogenetics for mapping phenotypes in structured populations

In both statistical genetics and phylogenetics, a major goal is to identify correlations between genetic loci or other aspects of the phenotype or environment and a focal trait. In these 2 fields, there are sophisticated but disparate statistical traditions aimed at these tasks. The disconnect between their respective approaches is becoming untenable as questions in medicine, conservation biology, and evolutionary biology increasingly rely on integrating data from within and among species, and once-clear conceptual divisions are becoming increasingly blurred. To help bridge this divide, we lay out a general model describing the covariance between the genetic contributions to the quantitative phenotypes of different individuals. Taking this approach shows that standard models in both statistical genetics (e.g., genome-wide association studies; GWAS) and phylogenetic comparative biology (e.g., phylogenetic regression) can be interpreted as special cases of this more general quantitative-genetic model. The fact that these models share the same core architecture means that we can build a unified understanding of the strengths and limitations of different methods for controlling for genetic structure when testing for associations. We develop intuition for why and when spurious correlations may occur analytically and conduct population-genetic and phylogenetic simulations of quantitative traits. The structural similarity of problems in statistical genetics and phylogenetics enables us to take methodological advances from one field and apply them in the other. We demonstrate by showing how a standard GWAS technique-including both the genetic relatedness matrix (GRM) as well as its leading eigenvectors, corresponding to the principal components of the genotype matrix, in a regression model-can mitigate spurious correlations in phylogenetic analyses. As a case study, we re-examine an analysis testing for coevolution of expression levels between genes across a fungal phylogeny and show that including eigenvectors of the covariance matrix as covariates decreases the false positive rate while simultaneously increasing the true positive rate. More generally, this work provides a foundation for more integrative approaches for understanding the genetic architecture of phenotypes and how evolutionary processes shape it.

Homoplastic versus xenoplastic evolution: exploring the emergence of key intrinsic and extrinsic traits in the montane genus Soldanella (Primulaceae)

Specific ecological conditions in the high mountain environment exert a selective pressure that often leads to convergent trait evolution. Reticulations induced by incomplete lineage sorting and introgression can lead to discordant trait patterns among gene and species trees (hemiplasy/xenoplasy), providing a false illusion that the traits under study are homoplastic. Using phylogenetic species networks, we explored the effect of gene exchange on trait evolution in Soldanella, a genus profoundly influenced by historical introgression. At least three features evolved independently multiple times: the single-flowered dwarf phenotype, dysploid cytotype, and ecological generalism. The present analyses also indicated that the recurring occurrence of stoloniferous growth might have been prompted by an introgression event between an ancestral lineage and a still extant species, although its emergence via convergent evolution cannot be completely ruled out. Phylogenetic regression suggested that the independent evolution of larger genomes in snowbells is most likely a result of the interplay between hybridization events of dysploid and euploid taxa and hostile environments at the range margins of the genus. The emergence of key intrinsic and extrinsic traits in snowbells has been significantly impacted not only by convergent evolution but also by historical and recent introgression events.

Whole-genome resequencing of Chinese indigenous sheep provides insight into the genetic basis underlying climate adaptation

BACKGROUND

Chinese indigenous sheep are valuable resources with unique features and characteristics. They are distributed across regions with different climates in mainland China; however, few reports have analyzed the environmental adaptability of sheep based on their genome. We examined the variants and signatures of selection involved in adaptation to extreme humidity, altitude, and temperature conditions in 173 sheep genomes from 41 phenotypically and geographically representative Chinese indigenous sheep breeds to characterize the genetic basis underlying environmental adaptation in these populations.

RESULTS

Based on the analysis of population structure, we inferred that Chinese indigenous sheep are divided into four groups: Kazakh (KAZ), Mongolian (MON), Tibetan (TIB), and Yunnan (YUN). We also detected a set of candidate genes that are relevant to adaptation to extreme environmental conditions, such as drought-prone regions (TBXT, TG, and HOXA1), high-altitude regions (DYSF, EPAS1, JAZF1, PDGFD, and NF1) and warm-temperature regions (TSHR, ABCD4, and TEX11). Among all these candidate genes, eight ABCD4, CNTN4, DOCK10, LOC105608545, LOC121816479, SEM3A, SVIL, and TSHR overlap between extreme environmental conditions. The TSHR gene shows a strong signature for positive selection in the warm-temperature group and harbors a single nucleotide polymorphism (SNP) missense mutation located between positions 90,600,001 and 90,650,001 on chromosome 7, which leads to a change in the protein structure of TSHR and influences its stability.

CONCLUSIONS

Analysis of the signatures of selection uncovered genes that are likely related to environmental adaptation and a SNP missense mutation in the TSHR gene that affects the protein structure and stability. It also provides information on the evolution of the phylogeographic structure of Chinese indigenous sheep populations. These results provide important genetic resources for future breeding studies and new perspectives on how animals can adapt to climate change.

Unifying approaches from statistical genetics and phylogenetics for mapping phenotypes in structured populations

In both statistical genetics and phylogenetics, a major goal is to identify correlations between genetic loci or other aspects of the phenotype or environment and a focal trait. In these two fields, there are sophisticated but disparate statistical traditions aimed at these tasks. The disconnect between their respective approaches is becoming untenable as questions in medicine, conservation biology, and evolutionary biology increasingly rely on integrating data from within and among species, and once-clear conceptual divisions are becoming increasingly blurred. To help bridge this divide, we derive a general model describing the covariance between the genetic contributions to the quantitative phenotypes of different individuals. Taking this approach shows that standard models in both statistical genetics (e.g., Genome-Wide Association Studies; GWAS) and phylogenetic comparative biology (e.g., phylogenetic regression) can be interpreted as special cases of this more general quantitative-genetic model. The fact that these models share the same core architecture means that we can build a unified understanding of the strengths and limitations of different methods for controlling for genetic structure when testing for associations. We develop intuition for why and when spurious correlations may occur using analytical theory and conduct population-genetic and phylogenetic simulations of quantitative traits. The structural similarity of problems in statistical genetics and phylogenetics enables us to take methodological advances from one field and apply them in the other. We demonstrate this by showing how a standard GWAS technique-including both the genetic relatedness matrix (GRM) as well as its leading eigenvectors, corresponding to the principal components of the genotype matrix, in a regression model-can mitigate spurious correlations in phylogenetic analyses. As a case study of this, we re-examine an analysis testing for co-evolution of expression levels between genes across a fungal phylogeny, and show that including covariance matrix eigenvectors as covariates decreases the false positive rate while simultaneously increasing the true positive rate. More generally, this work provides a foundation for more integrative approaches for understanding the genetic architecture of phenotypes and how evolutionary processes shape it.

Evaluating the Performance of Widely Used Phylogenetic Models for Gene Expression Evolution

Phylogenetic comparative methods are increasingly used to test hypotheses about the evolutionary processes that drive divergence in gene expression among species. However, it is unknown whether the distributional assumptions of phylogenetic models designed for quantitative phenotypic traits are realistic for expression data and importantly, the reliability of conclusions of phylogenetic comparative studies of gene expression may depend on whether the data is well described by the chosen model. To evaluate this, we first fit several phylogenetic models of trait evolution to 8 previously published comparative expression datasets, comprising a total of 54,774 genes with 145,927 unique gene-tissue combinations. Using a previously developed approach, we then assessed how well the best model of the set described the data in an absolute (not just relative) sense. First, we find that Ornstein-Uhlenbeck models, in which expression values are constrained around an optimum, were the preferred models for 66% of gene-tissue combinations. Second, we find that for 61% of gene-tissue combinations, the best-fit model of the set was found to perform well; the rest were found to be performing poorly by at least one of the test statistics we examined. Third, we find that when simple models do not perform well, this appears to be typically a consequence of failing to fully account for heterogeneity in the rate of the evolution. We advocate that assessment of model performance should become a routine component of phylogenetic comparative expression studies; doing so can improve the reliability of inferences and inspire the development of novel models.

Evaluating the Performance of Widely Used Phylogenetic Models for Gene Expression Evolution

Phylogenetic comparative methods are increasingly used to test hypotheses about the evolutionary processes that drive divergence in gene expression among species. However, it is unknown whether the distributional assumptions of phylogenetic models designed for quantitative phenotypic traits are realistic for expression data and importantly, the reliability of conclusions of phylogenetic comparative studies of gene expression may depend on whether the data is well-described by the chosen model. To evaluate this, we first fit several phylogenetic models of trait evolution to 8 previously published comparative expression datasets, comprising a total of 54,774 genes with 145,927 unique gene-tissue combinations. Using a previously developed approach, we then assessed how well the best model of the set described the data in an absolute (not just relative) sense. First, we find that Ornstein-Uhlenbeck models, in which expression values are constrained around an optimum, were the preferred model for 66% of gene-tissue combinations. Second, we find that for 61% of gene-tissue combinations, the best fit model of the set was found to perform well; the rest were found to be performing poorly by at least one of the test statistics we examined. Third, we find that when simple models do not perform well, this appears to be typically a consequence of failing to fully account for heterogeneity in the rate of the evolution. We advocate that assessment of model performance should become a routine component of phylogenetic comparative expression studies; doing so can improve the reliability of inferences and inspire the development of novel models.