Evolutionary dynamics of Indo-European alignment patterns

This paper employs phylogenetic modeling to reconstruct the alignment system of Indo-European. We use a data set of categorical morphosyntactic features, which take states such as ‘nominative-accusative’, ‘active-stative’, or ‘ergative’. We analyze these characters with a standard Bayesian comparative phylogenetic method, inferring transition rates between character states on the basis of a phylogenetic representation of the languages in the data. Using these rates, we then reconstruct the probability of presence of traits at the root and nodes of Indo-European. We find that the most probable alignment system for Proto-Indo-European is a nominative-accusative system, with low probabilities of neutral marking and ergativity in the categories lower in grammatical hierarchies (nouns, past). Using a test of phylogenetic signal, we find that characters pertaining to categories higher in hierarchies show greater phylogenetic stability than categories lower in hierarchies. We examine our results in relation to theories of Proto-Indo-European alignment as well as to general typology.

Novel phylogenetic methods reveal that resource-use intensification drives the evolution of “complex” societies

Explaining the rise of large, sedentary populations, with attendant expansions of socio-political hierarchy and labor specialization (collectively referred to as “societal complexity”), is a central problem for social scientists and historians. Adoption of agriculture has often been invoked to explain the rise of complex societies, but archaeological and ethnographic records contradict simple agri-centric models. Rather than a unitary phenomenon, “complexity” may be better understood as a network of interacting features, which in turn have causal relationships with subsistence. Here we use novel comparative methods and a global sample of 186 nonindustrial societies to infer the role of subsistence practices in shaping complexity. We also introduce a phylogenetic method for causal inference that generalizes beyond two binary traits, lifting a major constraint on comparative research. We found that, rather than agriculture alone, a suite of resource-use intensification variables leads to broad increases in technological and social differentiation. Our study provides evidence that resource intensification is a leader, not a follower, in the rise of complex societies worldwide.

Postscript

This postscript reflects on the role of parsimony in the future of systematics. Under the view of systematics advocated in this book, the exuberantly messy data of biological diversity are organized into a clear and coherent explanatory framework through the application of the principle of parsimony. The principle of common cause, the principle of cause and effect, and the principle of uniformitarianism are all applications of the principle of parsimony to the explanation of events unfolding in time. Thus, parsimony is not merely an old-fashioned phylogenetic method that has been superceded by purportedly more powerful and sophisticated statistical tools: it is the epistemological key to evaluating empirical evidence and discovering orderly patterns in the world to the extent that our perceptions allow. Ultimately, the success of every scientific inference and prediction relating to empirical phenomena in the world hinges upon parsimony.

Филогенетическая реконструкция начальных этапов распространения вируса SARS-CoV-2 на Евроазиатском и Американском континентах посредством анализа геномных данных (краткое сообщение)

Проанализированы 252 полных генома вируса SARS-CoV-2 первой волны (декабря 2019 - июль 2020 г.) пандемии COVID-19 из 21 страны мира, включая Россию, посредством Байесовского филогенетического метода с молекулярными часами. Используемая нами методика показала, что первые заболевшие COVID-19 в человеческой популяции появились в период с июля по ноябрь 2019 г. в Китае. Распространение SARS-CoV-2 из Китая по всем регионам мира произошло с декабря 2019 по начало февраля 2020 года. Появление вируса в России датируется второй половиной января 2020 года. Скорость эволюции кодирующей части генома SARS-CoV-2 равная в среднем 7.3×10-4 (5.95×10-4 – 8.68×10-4) нуклеотидных замен на сайт в год сопоставима со скоростями накопления замен в геномах других человеческих РНК-содержащих вирусах (Measles morbillivirus, Rubella virus, Enterovirus C). 252 complete genomes of the SARS-CoV-2 isolated during the first wave (December 2019 - July 2020) of the global COVID-19 pandemic from 21 countries of the world, including Russia, were analyzed using the Bayesian phylogenetic method with a molecular clock. Results showed that the first cases of COVID-19 in the human population appeared in the period between July and November 2019 in China. The spread of SARS-CoV-2 from China toward all regions of the world occurred from December 2019 to early February 2020. The appearance of the virus in Russia dates back to the second half of January 2020. The rate of evolution of the coding part of the SARS-CoV-2 genome equal to 7.3×10-4 (5.95×10-4 - 8.68×10-4) nucleotide substitutions per site per year is comparable to the rates of accumulation of substitutions in genomes of other human RNA viruses (Measles morbillivirus, Rubella virus, Enterovirus C).

Phylogenetic Permulations: a statistically rigorous approach to measure confidence in associations between phenotypes and genetic elements in a phylogenetic context

The wealth of high-quality genomes for numerous species has motivated many investigations into the genetic underpinnings of phenotypes. Comparative genomics methods approach this task by identifying convergent shifts at the genetic level that are associated with traits evolving convergently across independent lineages. However, these methods have complex statistical behaviors that are influenced by non-trivial and oftentimes unknown confounding factors. Consequently, using standard statistical analyses in interpreting the outputs of these methods leads to potentially inaccurate conclusions. Here, we introduce phylogenetic permulations, a novel statistical strategy that combines phylogenetic simulations and permutations to calculate accurate, unbiased p-values from phylogenetic methods. Permulations construct the null expectation for p-values from a given phylogenetic method by empirically generating null phenotypes. Subsequently, empirical p-values that capture the true statistical confidence given the correlation structure in the data are directly calculated based on the empirical null expectation. We examine the performance of permulation methods by analyzing both binary and continuous phenotypes, including marine, subterranean, and long-lived large-bodied mammal phenotypes. Our results reveal that permulations improve the statistical power of phylogenetic analyses and correctly calibrate statements of confidence in rejecting complex null distributions while maintaining or improving the enrichment of known functions related to the phenotype. We also find that permulations refine pathway enrichment analyses by correcting for non-independence in gene ranks. Our results demonstrate that permulations are a powerful tool for improving statistical confidence in the conclusions of phylogenetic analysis when the parametric null is unknown.

Is Phylotranscriptomics as Reliable as Phylogenomics?

Phylogenomics, the study of phylogenetic relationships among taxa based on their genome sequences, has emerged as the preferred phylogenetic method because of the wealth of phylogenetic information contained in genome sequences. Genome sequencing, however, can be prohibitively expensive, especially for taxa with huge genomes and when many taxa need sequencing. Consequently, the less costly phylotranscriptomics has seen an increased use in recent years. Phylotranscriptomics reconstructs phylogenies using DNA sequences derived from transcriptomes, which are often orders of magnitude smaller than genomes. However, in the absence of corresponding genome sequences, comparative analyses of transcriptomes can be challenging and it is unclear whether phylotranscriptomics is as reliable as phylogenomics. Here, we respectively compare the phylogenomic and phylotranscriptomic trees of 22 mammals and 15 plants that have both sequenced nuclear genomes and publicly available RNA sequencing data from multiple tissues. We found that phylotranscriptomic analysis can be sensitive to orthologous gene identification. When a rigorous method for identifying orthologs is employed, phylogenomic and phylotranscriptomic trees are virtually identical to each other, regardless of the tissue of origin of the transcriptomes and whether the same tissue is used across species. These findings validate phylotranscriptomics, brighten its prospect, and illustrate the criticality of reliable ortholog detection in such practices.

Reconstructing Phylogenetic Relationships Based on Repeat Sequence Similarities

A recent phylogenetic method based on genome-wide abundance of different repeat types proved to be useful in reconstructing the evolutionary history of several plant and animal groups. Here, we demonstrate that an alternative information source from the repeatome can also be employed to infer phylogenetic relationships among taxa. Specifically, this novel approach makes use of the repeat sequence similarity matrices obtained from the comparative clustering analyses of RepeatExplorer 2, which are subsequently transformed to between-taxa distance matrices. These pairwise matrices are used to construct neighbour-joining trees for each of the top most-abundant clusters and they are finally summarized in a consensus network. This methodology was tested on three groups of angiosperms and one group of insects, resulting in congruent evolutionary hypotheses compared to more standard systematic analyses based on commonly used DNA markers. We propose that the combined application of these phylogenetic approaches based on repeat abundances and repeat sequence similarities could be helpful to understand mechanisms governing genome and repeatome evolution.

Starless bias and parameter-estimation bias in the likelihood-based phylogenetic method

ABSTRACTI analyzed various site pattern combinations in a 4-OTU case to identify sources of starless bias and parameter-estimation bias in likelihood-based phylogenetic methods, and reported three significant contributions. First, the likelihood method is odd in that it may not generate a star tree with sequences that are equidistant from each other. This behaviour, dubbed starless bias, happens in a 4-OTU tree when there is an excess (i.e., more than expected from a star tree and a substitution model) of conflicting phylogenetic signals supporting the three resolved topologies equally. Special site pattern combinations leading to rejection of a star tree, when sequences are equidistant from each other, were identified. Second, fitting gamma distribution to model rate heterogeneity over sites is strongly confounded with tree topology, especially in conjunction with the starless bias. I present examples to show dramatic differences in the estimated shape parameter α between a star tree and a resolved tree. There may be no rate heterogeneity over sites (with the estimated α > 10000) when a star tree is imposed, but α < 1 (suggesting strong rate heterogeneity over sites) when an (incorrect) resolved tree is imposed. Thus, the dependence of “rate heterogeneity” on tree topology implies that “rate heterogeneity” is not a sequence-specific feature, cautioning against interpreting a small α to mean that some sites are under strong purifying selection and others not. Thirdly, because there is no existing (and working) likelihood method for evaluating a star tree with continuous gamma-distributed rate, I have implemented the method for JC69 in a self-contained R script for a four-OTU tree (star or resolved), in addition to another R script assuming a constant rate over sites. These R scripts should be useful for teaching and exploring likelihood methods in phylogenetics.

Bayesian inference of ancestral dates on bacterial phylogenetic trees

ABSTRACTThe sequencing and comparative analysis of a collection of bacterial genomes from a single species or lineage of interest can lead to key insights into its evolution, ecology or epidemiology. The tool of choice for such a study is often to build a phylogenetic tree, and more specifically when possible a dated phylogeny, in which the dates of all common ancestors are estimated. Here we propose a new Bayesian methodology to construct dated phylogenies which is specifically designed for bacterial genomics. Unlike previous Bayesian methods aimed at building dated phylogenies, we consider that the phylogenetic relationships between the genomes have been previously evaluated using a standard phylogenetic method, which makes our methodology much faster and scalable. This two-steps approach also allows us to directly exploit existing phylogenetic methods that detect bacterial recombination, and therefore to account for the effect of recombination in the construction of a dated phylogeny. We analysed many simulated datasets in order to benchmark the performance of our approach in a wide range of situations. Furthermore, we present applications to three different real datasets from recent bacterial genomic studies. Our methodology is implemented in a R package called BactDating which is freely available for download at https://github.com/xavierdidelot/BactDating.