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For over a decade, experimental evolution has been combined with high‐throughput sequencing techniques. In so‐called Evolve‐and‐Resequence (E&R) experiments, populations are kept in the laboratory under controlled experimental conditions where their genomes are sampled and allele frequencies monitored. However, identifying signatures of adaptation in E&R datasets is far from trivial, and it is still necessary to develop more efficient and statistically sound methods for detecting selection in genome‐wide data. Here, we present Bait‐ER – a fully Bayesian approach based on the Moran model of allele evolution to estimate selection coefficients from E&R experiments. The model has overlapping generations, a feature that describes several experimental designs found in the literature. We tested our method under several different demographic and experimental conditions to assess its accuracy and precision, and it performs well in most scenarios. Nevertheless, some care must be taken when analysing trajectories where drift largely dominates and starting frequencies are low. We compare our method with other available software and report that ours has generally high accuracy even for trajectories whose complexity goes beyond a classical sweep model. Furthermore, our approach avoids the computational burden of simulating an empirical null distribution, outperforming available software in terms of computational time and facilitating its use on genome‐wide data. We implemented and released our method in a new open‐source software package that can be accessed at https://doi.org/10.5281/zenodo.7351736. [ABSTRACT FROM AUTHOR]

Summary The phylogenetic signal, frequently used to identify signatures of adaptive evolution or important associations between genes and phenotypes, measures the tendency for recently diverged species to resemble each other more than distantly related species. An example of such a measure is the δ statistic, which uses Shannon entropy to measure the degree of phylogenetic signal between a categorical trait and a phylogeny. In this study, we refined this statistic to account for tree uncertainty, resulting in more accurate assessments of phylogenetic associations. In addition, we provided a more accessible and computationally efficient implementation of the δ statistic that will facilitate its use by the evolutionary community. Availability and implementation github.com/diogo-s-ribeiro/delta-statistic. [ABSTRACT FROM AUTHOR]

Despite the importance of natural selection in species' evolutionary history, phylogenetic methods that take into account population-level processes typically ignore selection. The assumption of neutrality is often based on the idea that selection occurs at a minority of loci in the genome and is unlikely to compromise phylogenetic inferences significantly. However, genome-wide processes like GC-bias and some variation segregating at the coding regions are known to evolve in the nearly neutral range. As we are now using genome-wide data to estimate species trees, it is natural to ask whether weak but pervasive selection is likely to blur species tree inferences. We developed a polymorphism-aware phylogenetic model tailored for measuring signatures of nucleotide usage biases to test the impact of selection in the species tree. Our analyses indicate that although the inferred relationships among species are not significantly compromised, the genetic distances are systematically underestimated in a node-height-dependent manner: that is, the deeper nodes tend to be more underestimated than the shallow ones. Such biases have implications for molecular dating. We dated the evolutionary history of 30 worldwide fruit fly populations, and we found signatures of GC-bias considerably affecting the estimated divergence times (up to 23%) in the neutral model. Our findings call for the need to account for selection when quantifying divergence or dating species evolution. [ABSTRACT FROM AUTHOR]

Typical avian eyes are phenotypically engineered for photopic vision (daylight). In contrast, the highly derived eyes of the barn owl (Tyto alba) are adapted for scotopic vision (dim light). The dramatic modifications distinguishing barn owl eyes from other birds include: 1) shifts in frontal orientation to improve binocularity, 2) rod-dominated retina, and 3) enlarged corneas and lenses. Some of thesefeaturesparallelmammalianeyepatterns,whicharehypothesizedtohaveinitiallyevolvedinnocturnalenvironments.Here,we used an integrative approach combining phylogenomics and functional phenotypes of 211 eye-development genes across 48 avian genomesrepresentingmostavianorders,includingthestemlineageofthescotopic-adaptedbarnowl.Overall,weidentified25eyedevelopment genes that coevolved under intensified or relaxed selection in the retina, lens, cornea, and optic nerves of the barn owl. The agtpbp1 gene, which is associated with the survival of photoreceptor populations, was pseudogenized in the barn owl genome. Our results further revealed that barn owl retinal genes responsible for the maintenance, proliferation, and differentiation of photoreceptors experienced an evolutionary relaxation. Signatures of relaxed selection were also observed in the lens and cornea morphology-associated genes, suggesting that adaptive evolution in these structures was essentially structural. Four eyedevelopment genes (ephb1, phactr4, prph2, and rs1) evolved in positive association with the orbit convergence in birds and under relaxedselectioninthebarnowllineage,likelycontributingtoanincreasedrelianceonbinocularvisioninthebarnowl.Moreover,we found evidence of coevolutionary interactions among genes that are expressed in the retina, lens, and optic nerve, suggesting synergetic adaptive events. Our study disentangles the genomic changes governing the binocularity and low-light perception adaptations of barn owls to nocturnal environments while revealing the molecular mechanisms contributing to the shift from the typical avian photopic vision to the more-novel scotopic-adapted eye. [ABSTRACT FROM AUTHOR]

As multi-individual population-scale data become available, more complex modeling strategies are needed to quantify genome-wide patterns of nucleotide usage and associated mechanisms of evolution. Recently, the multivariate neutral Moran model was proposed. However, it was shown insufficient to explain the distribution of alleles in great apes. Here, we propose a new model that includes allelic selection. Our theoretical results constitute the basis of a new Bayesian framework to estimate mutation rates and selection coefficients from population data. We apply the new framework to a great ape dataset, where we found patterns of allelic selection that match those of genome-wide GC-biased gene conversion (gBGC). In particular, we show that great apes have patterns of allelic selection that vary in intensity--a feature that we correlated with great apes' distinct demographies. We also demonstrate that the AT/GC toggling effect decreases the probability of a substitution, promoting more polymorphisms in the base composition of great ape genomes. We further assess the impact of GC-bias in molecular analysis, and find that mutation rates and genetic distances are estimated under bias when gBGC is not properly accounted for. Our results contribute to the discussion on the tempo and mode of gBGC evolution, while stressing the need for gBGC-aware models in population genetics and phylogenetics. [ABSTRACT FROM AUTHOR]

Motivation Determining whether a trait and phylogeny share some degree of phylogenetic signal is a flagship goal in evolutionary biology. Signatures of phylogenetic signal can assist the resolution of a broad range of evolutionary questions regarding the tempo and mode of phenotypic evolution. However, despite the considerable number of strategies to measure it, few and limited approaches exist for categorical traits. Here, we used the concept of Shannon entropy and propose the δ statistic for evaluating the degree of phylogenetic signal between a phylogeny and categorical traits. Results We validated δ as a measure of phylogenetic signal: the higher the δ -value the higher the degree of phylogenetic signal between a given tree and a trait. Based on simulated data we proposed a threshold-based classification test to pinpoint cases of phylogenetic signal. The assessment of the test's specificity and sensitivity suggested that the δ approach should only be applied to 20 or more species. We have further tested the performance of δ in scenarios of branch length and topology uncertainty, unbiased and biased trait evolution and trait saturation. Our results showed that δ may be applied in a wide range of phylogenetic contexts. Finally, we investigated our method in 14 360 mammalian gene trees and found that olfactory receptor genes are significantly associated with the mammalian activity patterns, a result that is congruent with expectations and experiments from the literature. Our application shows that δ can successfully detect molecular signatures of phenotypic evolution. We conclude that δ represents a useful measure of phylogenetic signal since many phenotypes can only be measured in categories. Availability and implementation https://github.com/mrborges23/delta%5fstatistic. Supplementary information Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]