Biology subjects

Pennell, M.

Publications and source records attributed to Pennell, M..

27 records · Page 2Linked to original sources

bioRxiv2024

The Untapped Potential of Tree Size in Reconstructing Evolutionary and Epidemiological Dynamics

A phylogenetic tree has three types of attributes: size, shape (topology), and branch lengths. Phylody-namic studies are often motivated by questions regarding the size of clades, nevertheless, nearly all of the inference methods only make use of the other two attributes. In this paper, we ask whether there is additional information if we consider tree size more explicitly in phylodynamic inference methods. To address this question, we first needed to be able to compute the expected tree size distribution under a specified phylodynamic model; perhaps surprisingly, there is not a general method for doing so -- it is known what this is under a Yule or constant rate birth-death model but not for the more complicated scenarios researchers are often interested in. We present three different solutions to this problem: using i) the deterministic limit; ii) master equations; and iii) an ensemble moment approximation. Using simulations, we evaluate the accuracy of these three approaches under a variety of scenarios and alternative measures of tree size (i.e., sampling through time or only at the present; sampling ancestors or not). We then use the most accurate measures for the situation, to investigate the added informational content of tree size. We find that for two critical phylodynamic questions -- i) is diversification diversity dependent? and, ii) can we distinguish between alternative diversification scenarios? -- knowing the expected tree size distribution under the specified scenario provides insights that could not be gleaned from considering the expected shape and branch lengths alone. The contribution of this paper is both a novel set of methods for computing tree size distributions and a path forward for richer phylodynamic inference into the evolutionary and epidemiological processes that shape lineage trees.

evolutionary biology↗

bioRxiv2024

Evaluating methods for B-cell clonal family assignment

The adaptive immune response relies on a diverse repertoire of B-cell receptors, each of which is characterized by a distinct sequence resulting from VDJ-recombination. Upon binding to an antigen, B-cells undergo clonal expansion and in a process unique to B-cells the overall binding affinity of the repertoire is further enhanced by somatic hypermutations in the receptor sequence. For B-cell repertoires it is therefore particularly important to analyze the dynamics of clonal expansion and patterns of somatic hypermutations and thus it is necessary to group the sequences into distinct clones to determine the number and identity of expanding clonal families responding to an antigen. Multiple methods are currently used to identify clones from sequences, employing distinct approaches to the problem. Until now there has not been an extensive comparison of how well these methods perform under the same conditions. Furthermore, since this is fundamentally a phylogenetics problem, we speculated that the mPTP method, which delimits species based on an analysis of changes in the underlying process of diversification, might perform as well as or better than existing methods. Here we conducted extensive simulations of B-cell repertoires under a diverse set of conditions and studied errors in clonal assignment and in downstream ancestral state reconstruction. We demonstrated that SCOPer-H consistently yielded superior results across parameters. However, this approach relies on a good reference assembly for the germline immunoglobulin genes which is lacking for many species. Using mPTP had lower error rates than tailor-made immunogenetic methods and should therefore be considered by researchers studying antibody evolution in non-model organisms without a reference genome.

immunology↗

bioRxiv2024

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.

genomics↗

bioRxiv2023

Saccharomycotina yeasts defy longstanding macroecological patterns

The Saccharomycotina yeasts ("yeasts" hereafter) are a fungal clade of scientific, economic, and medical significance. Yeasts are highly ecologically diverse, found across a broad range of environments in every biome and continent on earth1; however, little is known about what rules govern the macroecology of yeast species and their range limits in the wild2. Here, we trained machine learning models on 12,221 occurrence records and 96 environmental variables to infer global distribution maps for 186 yeast species ([~]15% of described species from 75% of orders) and to test environmental drivers of yeast biogeography and macroecology. We found that predicted yeast diversity hotspots occur in mixed montane forests in temperate climates. Diversity in vegetation type and topography were some of the greatest predictors of yeast species richness, suggesting that microhabitats and environmental clines are key to yeast diversification. We further found that range limits in yeasts are significantly influenced by carbon niche breadth and range overlap with other yeast species, with carbon specialists and species in high diversity environments exhibiting reduced geographic ranges. Finally, yeasts contravene many longstanding macroecological principles, including the latitudinal diversity gradient, temperature-dependent species richness, and latitude-dependent range size (Rapoports rule). These results unveil how the environment governs the global diversity and distribution of species in the yeast subphylum. These high-resolution models of yeast species distributions will facilitate the prediction of economically relevant and emerging pathogenic species under current and future climate scenarios.

ecology↗

bioRxiv2023

Alternative mutational architectures can produce identical M-matrices but lead to different macroevolutionary dynamics

Explaining macroevolutionary divergence in light of population genetics requires understanding the extent to which the patterns of mutational input contribute to long-term trends. In the context of quantitative traits, mutational input is typically described by the mutational variance-covariance matrix, or the M-matrix, which summarizes phenotypic variances and covariances introduced by new mutations per generation. However, as a summary statistic, the M-matrix does not fully capture all the relevant information from the underlying mutational architecture, and there exist infinitely many possible underlying mutational architectures that give rise to the same M-matrix. Using individual-based simulations, we demonstrate mutational architectures that produce the same M-matrix can lead to different levels of constraint on evolution and result in difference in within-population genetic variance, between-population divergence, and rate of adaptation. In particular, the rate of adaptation and that of neutral evolution are both reduced when a greater proportion of loci are pleiotropic. Our results reveal that aspects of mutational input not reflected by the M-matrix can have a profound impact on long-term evolution, and suggest it is important to take them into account in order to connect patterns of long-term phenotypic evolution to underlying microevolutionary mechanisms.

evolutionary biology↗

bioRxiv2023

Decoupling of evolutionary changes in mRNA and protein levels

Variation in gene expression across lineages is thought to explain much of the observed phenotypic variation and adaptation. The protein is closer to the target of natural selection but gene expression is typically measured as the amount of mRNA. The broad assumption that mRNA levels are good proxies for protein levels has been undermined by a number of studies reporting moderate or weak correlations between the two measures across species. One biological explanation for this discrepancy is that there has been compensatory evolution between the mRNA level and regulation of translation. However, we do not understand the evolutionary conditions necessary for this to occur nor the expected strength of the correlation between mRNA and protein levels. Here we develop a theoretical model for the coevolution of mRNA and protein levels and investigate the dynamics of the model over time. We find that compensatory evolution is widespread when there is stabilizing selection on the protein level, which is true across a variety of regulatory pathways. When the protein level is under directional selection, the mRNA level of a gene and its translation rate of the same gene were negatively correlated across lineages but positively correlated across genes. These findings help explain results from comparative studies of gene expression and potentially enable researchers to disentangle biological and statistical hypotheses for the mismatch between transcriptomic and proteomic studies.

evolutionary biology↗

bioRxiv2023

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.

evolutionary biology↗

bioRxiv2018

Is phylogenetic diversity a surrogate for functional diversity across clades and space?

In the face of the biodiversity crisis, it is argued that we should prioritize species in order to capture high functional diversity (FD). Because species traits often reflect shared evolutionary history, many researchers have advocated for a \"phylogenetic gambit\": maximizing phylogenetic diversity (PD) should indirectly capture FD. For the first time, we empirically test this gambit using data from >15,000 vertebrate species and ecologically-relevant traits. Maximizing PD results in an average gain of 18% of FD relative to random choice. However, this average gain hides the fact that in over 1/3 of the comparisons, maximum PD sets contain less FD than randomly chosen sets of species. These results suggest that, while maximizing PD protection can help to protect FD, it represents a risky strategy.\n\nStatement of authorshipFM, MP, MC, SD, GVDR, RG, AOM, CT and WP conceived the design of the study. FM and GVDR conducted the analysis. FM, RG, MP and WP interpreted the results and wrote the first draft of the manuscript. All authors edited the final version.\n\nData accessibility statementMost of the data is publicly available (see methods). The Fish data is available upon request.\n\nCode accessibility statementR functions developed in this paper are available at https://github.com/FloMazel/FD_PD_Max

ecology↗

bioRxiv2017

Conserving Phylogenetic Diversity Can Be A Poor Strategy For Conserving Functional Diversity

For decades, academic biologists have advocated for making conservation decisions in light of evolutionary history. Specifically, they suggest that policymakers should prioritize conserving phylogenetically diverse assemblages. The most prominent argument is that conserving phylogenetic diversity (PD) will also conserve diversity in traits and features (functional diversity; FD), which may be valuable for a number of reasons. The claim that PD-maximized ( maxPD) sets of taxa will also have high FD is often taken at face value and in cases where researchers have actually tested it, they have done so by measuring the phylogenetic signal in ecologically important functional traits. The rationale is that if traits closely mirror phylogeny, then saving the maxPD set of taxa will tend to maximize FD and if traits do not have phylogenetic structure, then saving the maxPD set of taxa will be no better at capturing FD than criteria that ignore PD. Here, we suggest that measuring the phylogenetic signal in traits is uninformative for evaluating the effectiveness of using PD in conservation. We evolve traits under several different models and, for the first time, directly compare the FD of a set of taxa that maximize PD to the FD of a random set of the same size. Under many common models of trait evolution and tree shapes, conserving the maxPD set of taxa will conserve more FD than conserving a random set of the same size. However, this result cannot be generalized to other classes of models. We find that under biologically plausible scenarios, using PD to select species can actually lead to less FD compared to a random set. Critically, this can occur even when there is phylogenetic signal in the traits. Predicting exactly when we expect using PD to be a good strategy for conserving FD is challenging, as it depends on complex interactions between tree shape and the assumptions of the evolutionary model. Nonetheless, if our goal is to maintain trait diversity, the fact that conserving taxa based on PD will not reliably conserve at least as much FD as choosing randomly raises serious concerns about the general utility of PD in conservation.

ecology↗