Genome-scale Datasets Research Articles

Resolving tricky nodes in the tree of life through amino acid recoding.

Genomic data allowed a detailed resolution of the Tree of Life, but "tricky nodes" such as the root of the animals remain unresolved. Genome-scale datasets are heterogeneous as genes and species are exposed to different pressures, and this can negatively impacts phylogenetic accuracy. We use simulated genomic-scale datasets and show that recoding amino acid data improves accuracy when the model does not account for the compositional heterogeneity of the amino acid alignment. We apply our findings to three datasets addressing the root of the animal tree, where the debate centers on whether sponges (Porifera) or comb jellies (Ctenophora) represent the sister of all other animals. We show that results from empirical data follow predictions from simulations and suggest that, at the least in phylogenies inferred from amino acid sequences, a placement of the ctenophores as sister to all the other animals is best explained as a tree reconstruction artifact.

A Practical Guide to Design and Assess a Phylogenomic Study.

Over the last decade, molecular systematics has undergone a change of paradigm as high-throughput sequencing now makes it possible to reconstruct evolutionary relationships using genome-scale datasets. The advent of “big data” molecular phylogenetics provided a battery of new tools for biologists but simultaneously brought new methodological challenges. The increase in analytical complexity comes at the price of highly specific training in computational biology and molecular phylogenetics, resulting very often in a polarized accumulation of knowledge (technical on one side and biological on the other). Interpreting the robustness of genome-scale phylogenetic studies is not straightforward, particularly as new methodological developments have consistently shown that the general belief of “more genes, more robustness” often does not apply, and because there is a range of systematic errors that plague phylogenomic investigations. This is particularly problematic because phylogenomic studies are highly heterogeneous in their methodology, and best practices are often not clearly defined. The main aim of this article is to present what I consider as the ten most important points to take into consideration when planning a well-thought-out phylogenomic study and while evaluating the quality of published papers. The goal is to provide a practical step-by-step guide that can be easily followed by nonexperts and phylogenomic novices in order to assess the technical robustness of phylogenomic studies or improve the experimental design of a project.

Beetle bioluminescence outshines extant aerial predators.

We understand very little about the timing and origins of bioluminescence, particularly as a predator avoidance strategy. Understanding the timing of its origins, however, can help elucidate the evolution of this ecologically important signal. Using fireflies, a prevalent bioluminescent group where bioluminescence primarily functions as aposematic and sexual signals, we explore the origins of this signal in the context of their potential predators. Divergence time estimations were performed using genomic-scale datasets providing a robust estimate for the origin of firefly bioluminescence as both a terrestrial and as an aerial signal. Our results recover the origin of terrestrial beetle bioluminescence at 141.17 (122.63-161.17) Ma and firefly aerial bioluminescence at 133.18 (117.86-152.47) Ma using a large dataset focused on Lampyridae; and terrestrial bioluminescence at 148.03 (130.12-166.80) Ma, with the age of aerial bioluminescence at 104.97 (99.00-120.90) Ma using a complementary Elateroidea dataset. These ages pre-date the origins of all known extant aerial predators (i.e. bats and birds) and support much older terrestrial predators (assassin bugs, frogs, ground beetles, lizards, snakes, hunting spiders and harvestmen) as the drivers of terrestrial bioluminescence in beetles. These ages also support the hypothesis that sexual signalling was probably the original function of this signal in aerial fireflies.

Systematic inference of indirect transcriptional regulation by protein kinases and phosphatases.

Gene expression is controlled by pathways of regulatory factors often involving the activity of protein kinases on transcription factor proteins. Despite this well established mechanism, the number of well described pathways that include the regulatory role of protein kinases on transcription factors is surprisingly scarce in eukaryotes.To address this, PhosTF was developed to infer functional regulatory interactions and pathways in both simulated and real biological networks, based on linear cyclic causal models with latent variables. GeneNetWeaverPhos, an extension of GeneNetWeaver, was developed to allow the simulation of perturbations in known networks that included the activity of protein kinases and phosphatases on gene regulation. Over 2000 genome-wide gene expression profiles, where the loss or gain of regulatory genes could be observed to perturb gene regulation, were then used to infer the existence of regulatory interactions, and their mode of regulation in the budding yeast Saccharomyces cerevisiae.Despite the additional complexity, our inference performed comparably to the best methods that inferred transcription factor regulation assessed in the DREAM4 challenge on similar simulated networks. Inference on integrated genome-scale data sets for yeast identified ∼ 8800 protein kinase/phosphatase-transcription factor interactions and ∼ 6500 interactions among protein kinases and/or phosphatases. Both types of regulatory predictions captured statistically significant numbers of known interactions of their type. Surprisingly, kinases and phosphatases regulated transcription factors by a negative mode or regulation (deactivation) in over 70% of the predictions.

Abstract 1222: Efficient domain adaptation for cancer mutation prediction

Abstract Prediction models have been widely used for many purposes in cancer research, including calling mutation status, identifying cancer subtype, and performing prognostic analysis. A fundamental assumption in supervised machine learning is that the data to be classified is derived from the same distribution as the data used to train the classifier. However, challenges in data acquisition often mean few or no labeled examples are available for the distribution of interest. For instance, sample sizes may be insufficient to train on rare cancer types, and technological limitations can hinder label generation, for instance a lack of simultaneous profiling of gene expression and mutation information in single-cell data. For such situations where labeled target data is limited, the field of domain adaptation and transfer learning has established principled ways to develop predictors for the data of interest (target data) using labeled data from a similar but distinct distribution (source data). One recent method, weighted elastic net domain adaptation (wenda), leverages the complex interactions between features (such as genes) to optimize a model’s predictive power on both source and target datasets. It learns the dependency structure between features and prioritizes those that are similar across distributions. This has previously been shown to significantly improve accuracy on predictions from a mismatched distribution, overcoming the limitations of traditional supervised models. Unfortunately, wenda requires training a Gaussian process model for each feature separately, which is computationally expensive and resists parallelization, making it infeasible for researchers to use at genome-scale. We have developed and implemented a modified form of the underlying algorithm, called wenda_gpu, which allows for fast, efficient model training for genome-scale datasets on a single GPU-enabled computer. Our implementation exploits both quasi-Newtonian parameter optimization and the computational power of GPUs for significant speedups without sacrificing accuracy. Our implementation is able to tackle training tasks on data at the scale of The Cancer Genome Atlas (TCGA), which was infeasible without our technical advances. We demonstrate the use of wenda_gpu on a range of TCGA-scale prediction tasks, making it possible to build accurate, predictive models that generalize to target datasets where supervised models could not be trained due to the lack of labeled data. We also trained models from gene expression data for cross-cancer type mutation prediction, which outperformed a regular elastic net. We anticipate that wenda_gpu will enable researchers to build accurate predictive models in cases where supervised models were previously not possible due to lack of labeled data, including rare cancers. Citation Format: Ariel A. Hippen, Jake Crawford, Jacob R. Gardner, Casey S. Greene. Efficient domain adaptation for cancer mutation prediction [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 1222.

Categorical edge-based analyses of phylogenomic data reveal conflicting signals for difficult relationships in the avian tree

Phylogenetic analyses fail to yield a satisfactory resolution of some relationships in the tree of life even with genome-scale datasets, so the failure is unlikely to reflect limitations in the amount of data. Gene tree conflicts are particularly notable in studies focused on these contentious nodes, and taxon sampling, different analytical methods, and/or data type effects can further confound analyses. Although many efforts have been made to incorporate biological conflicts, few studies have curated individual genes for their efficiency in phylogenomic studies. Here, we conduct an edge-based analysis of Neoavian evolution, examining the phylogenetic efficacy of two recent phylogenomic bird datasets and three datatypes (ultraconserved elements [UCEs], introns, and coding regions). We assess the potential causes for biases in signal-resolution for three difficult nodes: the earliest divergence of Neoaves, the position of the enigmatic Hoatzin (Opisthocomus hoazin), and the position of owls (Strigiformes). We observed extensive conflict among genes for all data types and datasets even after meticulous curation. Edge-based analyses (EBA) increased congruence and provided information about the impact of data type, GC content variation (GCCV), and outlier genes on each of nodes we examined. First, outlier gene signals appeared to drive different patterns of support for the relationships among the earliest diverging Neoaves. Second, the placement of Hoatzin was highly variable, although our EBA did reveal a previously unappreciated data type effect with an impact on its position. It also revealed that the resolution with the most support here was Hoatzin + shorebirds. Finally, GCCV, rather than data type (i.e., coding vs non-coding) per se, was correlated with a signal that supports monophyly of owls + Accipitriformes (hawks, eagles, and vultures). Eliminating high GCCV loci increased the signal for owls + mousebirds. Categorical EBA was able to reveal the nature of each edge and provide a way to highlight especially problematic branches that warrant a further examination. The current study increases our understanding about the contentious parts of the avian tree, which show even greater conflicts than appreciated previously.

Interpreting phylogenetic conflict: Hybridization in the most speciose genus of lichen-forming fungi

While advances in sequencing technologies have been invaluable for understanding evolutionary relationships, increasingly large genomic data sets may result in conflicting evolutionary signals that are often caused by biological processes, including hybridization. Hybridization has been detected in a variety of organisms, influencing evolutionary processes such as generating reproductive barriers and mixing standing genetic variation. Here, we investigate the potential role of hybridization in the diversification of the most speciose genus of lichen-forming fungi, Xanthoparmelia. As Xanthoparmelia is projected to have gone through recent, rapid diversification, this genus is particularly suitable for investigating and interpreting the origins of phylogenomic conflict. Focusing on a clade of Xanthoparmelia largely restricted to the Holarctic region, we used a genome skimming approach to generate 962 single-copy gene regions representing over 2 Mbp of the mycobiont genome. From this genome-scale dataset, we inferred evolutionary relationships using both concatenation and coalescent-based species tree approaches. We also used three independent tests for hybridization. Although different species tree reconstruction methods recovered largely consistent and well-supported trees, there was widespread incongruence among individual gene trees. Despite challenges in differentiating hybridization from ILS in situations of recent rapid radiations, our genome-wide analyses detected multiple potential hybridization events in the Holarctic clade, suggesting one possible source of trait variability in this hyperdiverse genus. This study highlights the value in using a pluralistic approach for characterizing genome-scale conflict, even in groups with well-resolved phylogenies, while highlighting current challenges in detecting the specific impacts of hybridization.

On the Need for New Measures of Phylogenomic Support.

The scale of data sets used to infer phylogenies has grown dramatically in the last decades, providing researchers with an enormous amount of information with which to draw inferences about evolutionary history. However, standard approaches to assessing confidence in those inferences (e.g., nonparametric bootstrap proportions [BP] and Bayesian posterior probabilities [PPs]) are still deeply influenced by statistical procedures and frameworks that were developed when information was much more limited. These approaches largely quantify uncertainty caused by limited amounts of data, which is often vanishingly small with modern, genome-scale sequence data sets. As a consequence, today’s phylogenomic studies routinely report near-complete confidence in their inferences, even when different studies reach strongly conflicting conclusions and the sites and loci in a single data set contain much more heterogeneity than our methods assume or can accommodate. Therefore, we argue that BPs and marginal PPs of bipartitions have outlived their utility as the primary means of measuring phylogenetic support for modern phylogenomic data sets with large numbers of sites relative to the number of taxa. Continuing to rely on these measures will hinder progress towards understanding remaining sources of uncertainty in the most challenging portions of the Tree of Life. Instead, we encourage researchers to examine the ideas and methods presented in this special issue of Systematic Biology and to explore the area further in their own work. The papers in this special issue outline strategies for assessing confidence and uncertainty in phylogenomic data sets that move beyond stochastic error due to limited data and offer promise for more productive dialogue about the challenges that we face in reaching our shared goal of understanding the history of life on Earth.[Big data; gene tree variation; genomic era; statistical bias.]

Identifying cancer sub-types from genomic scale data sets using confidence based integration (CBI)

Precision medicine is a method involving refined diagnosis of patients and searching for causes that are unseen in their patient cohorts who otherwise have largely similar health conditions. As the technology evolved to extract features from a wide variety of sources including genetics, a large quantum of data is available to the researchers for conducting micro studies in the field of disease and cures. In cancer research, integrative methods using genomic data sets has become a major area of interest. The petabytes of data that is available at The Cancer Genome Atlas (TCGA), a program jointly under NCI and National Human Genome Research Institute, has made possible more nuanced research in cancer genomics. Our method, Confidence Based Integration (CBI) is an integration method to extract similar as well as complementing information from the genomic data sets. This information will provide insight into the status of patients and their prospects. We used the expression data sets of gene, miRNA and DNA methylation in our fusion experiments on five different cancer types. These data sets, after fusion, are clustered using 'Spectral Clustering' algorithm, which derives clusters that form the disease sub types. Survival properties of each sub type demonstrates the reasons to consider the samples inside them highly similar. The performance of CBI, we report, is better, in terms of P-value in log-rank test, than other methods like similarity network fusion or SNF in forming clusters of significance. Individual features clustered extremely poor compared to CBI in most of the experiments.

Genome-Scale Metabolic Modelling of Lifestyle Changes in Rhizobium leguminosarum.

ABSTRACTBiological nitrogen fixation in rhizobium-legume symbioses is of major importance for sustainable agricultural practices. To establish a mutualistic relationship with their plant host, rhizobia transition from free-living bacteria in soil to growth down infection threads inside plant roots and finally differentiate into nitrogen-fixing bacteroids. We reconstructed a genome-scale metabolic model for Rhizobium leguminosarum and integrated the model with transcriptome, proteome, metabolome, and gene essentiality data to investigate nutrient uptake and metabolic fluxes characteristic of these different lifestyles. Synthesis of leucine, polyphosphate, and AICAR is predicted to be important in the rhizosphere, while myo-inositol catabolism is active in undifferentiated nodule bacteria in agreement with experimental evidence. The model indicates that bacteroids utilize xylose and glycolate in addition to dicarboxylates, which could explain previously described gene expression patterns. Histidine is predicted to be actively synthesized in bacteroids, consistent with transcriptome and proteome data for several rhizobial species. These results provide the basis for targeted experimental investigation of metabolic processes specific to the different stages of the rhizobium-legume symbioses.IMPORTANCE Rhizobia are soil bacteria that induce nodule formation on plant roots and differentiate into nitrogen-fixing bacteroids. A detailed understanding of this complex symbiosis is essential for advancing ongoing efforts to engineer novel symbioses with cereal crops for sustainable agriculture. Here, we reconstruct and validate a genome-scale metabolic model for Rhizobium leguminosarum bv. viciae 3841. By integrating the model with various experimental data sets specific to different stages of symbiosis formation, we elucidate the metabolic characteristics of rhizosphere bacteria, undifferentiated bacteria inside root nodules, and nitrogen-fixing bacteroids. Our model predicts metabolic flux patterns for these three distinct lifestyles, thus providing a framework for the interpretation of genome-scale experimental data sets and identifying targets for future experimental studies.

Species boundaries in the messy middle-A genome-scale validation of species delimitation in a recently diverged lineage of coastal fog desert lichen fungi.

Species delimitation among closely related species is challenging because traditional phenotype‐based approaches, for example, using morphology, ecological, or chemical characteristics, may not coincide with natural groupings. With the advent of high‐throughput sequencing, it has become increasingly cost‐effective to acquire genome‐scale data which can resolve previously ambiguous species boundaries. As the availability of genome‐scale data has increased, numerous species delimitation analyses, such as BPP and SNAPP+Bayes factor delimitation (BFD*), have been developed to delimit species boundaries. However, even empirical molecular species delimitation approaches can be biased by confounding evolutionary factors, for example, hybridization/introgression and incomplete lineage sorting, and computational limitations. Here, we investigate species boundaries and the potential for micro‐endemism in a lineage of lichen‐forming fungi, Niebla Rundel & Bowler, in the family Ramalinaceae by analyzing single‐locus and genome‐scale data consisting of (a) single‐locus species delimitation analysis using ASAP, (b) maximum likelihood‐based phylogenetic tree inference, (c) genome‐scale species delimitation models, e.g., BPP and SNAPP+BFD, and (d) species validation using the genealogical divergence index (gdi). We specifically use these methods to cross‐validate results between genome‐scale and single‐locus datasets, differently sampled subsets of genomic data and to control for population‐level genetic divergence. Our species delimitation models tend to support more speciose groupings that were inconsistent with traditional taxonomy, supporting a hypothesis of micro‐endemism, which may include morphologically cryptic species. However, the models did not converge on robust, consistent species delimitations. While the results of our analysis are somewhat ambiguous in terms of species boundaries, they provide a valuable perspective on how to use these empirical species delimitation methods in a nonmodel system. This study thus highlights the challenges inherent in delimiting species, particularly in groups such as Niebla, with complex, relatively recent phylogeographic histories.

SLC5A3 Transports Myo-Inositol to Support the Growth of Acute Myeloid Leukemia

Metabolic reprogramming contributes to tumor development and sustains cancer cell proliferation. Like other cancers, acute myeloid leukemia (AML), a devastating hematologic malignancy with poor overall survival, has altered metabolic features, providing new possibilities for AML treatment. Since the niche can reshape the metabolic properties of cancer cells, it is critical to validate AML metabolic vulnerabilities in a proper microenvironment. To this end, we optimized a protocol for CRISPR screening in orthotopic xenograft AML models, including patient-derived-xenograft (PDX) models tractable for CRISPR-editing, to enable the systematic evaluation of the physiological relevance of top AML dependencies. We performed in vivo screens in MV4-11 and U937 cell lines and a PDX model, which converged to reveal the sodium/myo-inositol cotransporter SLC5A3 as a top-ranked in vivo gene target.We have validated the SLC5A3 dependency in additional AML cell line and PDX models; SLC5A3 deletion consistently induced apoptotic cell death in all AML models tested. We also observed cell-context dependent alterations of cell cycle and differentiation. In addition, using a PDX model with a doxycycline-inducible CRISPR system, we have confirmed that induction of SLC5A3 knockout post transplantation significantly represses AML progression and prolonged mouse survival. Next, we investigated whether the growth defect caused by SLC5A3 knockout results from the myo-inositol deficiency. Augmentation of myo-inositol concentration in the standard culture medium completely rescued the proliferation of SLC5A3-knockout cells. In accordance, depletion of myo-inositol from the culture medium largely impeded the growth of parental AML cells, causing similar phenotypes as SLC5A3-deletion, with cell-context dependent alterations of cell cycle and induction of apoptotic cell death. Together, these data reveal that myo-inositol is a critical metabolite for AML.Since a subset of AML cell lines were not dependent on SLC5A3 based on a genome-scale CRISPR screen dataset (DepMap), we explored the potential biomarkers associated with SLC5A3 essentiality in AML. Intriguingly, the low expression of Inositol-3-phosphate synthase 1 (ISYNA1) predicted a strong SLC5A3 dependency in AML cell lines. In addition to importing myo-inositol from the extracellular fluid, cells can also synthesize myo-inositol de novo from glucose 6-phosphate, and ISYNA1 encodes the rate-limiting enzyme in this myo-inositol biosynthesis pathway. We confirmed low expression of ISYNA1 protein in AML cells sensitive to SLC5A3 deletion, and importantly, overexpression of ISYNA1 can completely relieve the SLC5A3 dependency. Moreover, knockout of ISYNA1 in an ISYNA1-high cell line M07e exacerbated the growth defect associated with SLC5A3 deletion. Altogether, these results strongly demonstrate that SLC5A3 becomes essential in AML cells with insufficient myo-inositol biosynthesis capacity to support AML proliferation.Finally, we have investigated the clinical features associated with low ISYNA1 expression in genomic datasets of primary AML samples, including TCGA and Beat AML, to postulate the patient population that can benefit from a SLC5A3-directed therapy. Interestingly, low ISYNA1 expression is associated with FAB M4 and M5 AML subtypes. In accordance, a monocyte lineage gene signature is enriched in ISYNA1-low samples. In addition, AML samples with low ISYNA1 expression tend to have IDH2 or DNMT3 mutations. Collectively, our study demonstrated that SLC5A3 is a strong metabolic dependency in AML, and targeting SLC5A3 can provide a therapeutic opportunity for a subset of monocytic AML. DisclosuresDharia: Genentech: Current Employment. Piccioni: Merck Research Laboratories: Current Employment. Stegmaier: AstraZeneca: Consultancy; KronosBio: Consultancy; Bristol Myers Squibb: Consultancy; Auron Therapeutics: Consultancy, Current equity holder in publicly-traded company; Novartis: Research Funding.

Gene-tree misrooting drives conflicts in phylogenomic coalescent analyses of palaeognath birds

Phylogenomic analyses of ancient rapid radiations can produce conflicting results that are driven by differential sampling of taxa and characters as well as the limitations of alternative analytical methods. We re-examine basal relationships of palaeognath birds (ratites and tinamous) using recently published datasets of nucleotide characters from 20,850 loci as well as 4301 retroelement insertions. The original studies attributed conflicting resolutions of rheas in their inferred coalescent and concatenation trees to concatenation failing in the anomaly zone. By contrast, we find that the coalescent-based resolution of rheas is premised upon extensive gene-tree estimation errors. Furthermore, retroelement insertions contain much more conflict than originally reported and multiple insertion loci support the basal position of rheas found in concatenation trees, while none were reported in the original publication. We demonstrate how even remarkable congruence in phylogenomic studies may be driven by long-branch misplacement of a divergent outgroup, highly incongruent gene trees, differential taxon sampling that can result in gene-tree misrooting errors that bias species-tree inference, and gross homology errors. What was previously interpreted as broad, robustly supported corroboration for a single resolution in coalescent analyses may instead indicate a common bias that taints phylogenomic results across multiple genome-scale datasets. The updated retroelement dataset now supports a species tree with branch lengths that suggest an ancient anomaly zone, and both concatenation and coalescent analyses of the huge nucleotide datasets fail to yield coherent, reliable results in this challenging phylogenetic context.

New insights into the phylogeny and evolution of Podocarpaceae inferred from transcriptomic data

Phylogenies of an increasing number of taxa have been resolved with the development of phylogenomics. However, the intergeneric relationships of Podocarpaceae, the second largest family of conifers comprising 19 genera and approximately 187 species mainly distributed in the Southern Hemisphere, have not been well disentangled in previous studies, even when genome-scale data sets were used. Here we used 993 nuclear orthologous groups (OGs) and 54 chloroplast OGs (genes), which were generated from 47 transcriptomes of Podocarpaceae and its sister group Araucariaceae, to reconstruct the phylogeny of Podocarpaceae. Our study completely resolved the intergeneric relationships of Podocarpaceae represented by all extant genera and revealed that topological conflicts among phylogenetic trees could be attributed to synonymous substitutions. Moreover, we found that two morphological traits, fleshy seed cones and flattened leaves, might be important for Podocarpaceae to adapt to angiosperm-dominated forests and thus could have promoted its species diversification. In addition, our results indicate that Podocarpaceae originated in Gondwana in the late Triassic and both vicariance and dispersal have contributed to its current biogeographic patterns. Our study provides the first robust transcriptome-based phylogeny of Podocarpaceae, an evolutionary framework important for future studies of this family.

Are Genomic Updates of Well-Studied Species Worth the Investment for Conservation? A Case Study of the Critically Endangered Magdalena River Turtle.

As genomic-scale data sets become economically feasible for most organisms, a key question for conservation biology is whether the increased resolution offered by new genomic approaches justifies repeating earlier studies based on traditional markers, rather than investing those same time and monetary resources in less-known species. Genomic studies offer clear advantages when the objective is to identify adaptive loci that may be critical to conservation policy-makers. However, the answer is far less certain for the population and landscape studies based on neutral loci that dominate the conservation genetics research agenda. We used Restriction-site Associated DNA sequencing (RADseq) to revisit earlier molecular studies of the IUCN Critically Endangered Magdalena River turtle (Podocnemis lewyana), documenting the conservation insights gained by increasing the number of neutral markers by several orders of magnitude. Earlier research indicated that P. lewyana has the lowest genetic diversity known for any chelonian, and little or no population differentiation among independent rivers. In contrast, the RADseq data revealed discrete population structure with isolation-by-distance within river segments and identified precise population breaks clearly delineating management units. It also confirmed that the species does not have extremely low heterozygosity and that effective population sizes are probably sufficient to maintain long-term evolutionary potential. Contrary to earlier inferences from more limited population genetic markers, our genomic data suggest that management strategies should shift from active genetic rescue to more passive protection without extreme interventions. We conclude with a list of examples of conservation studies in other vertebrates indicating that for many systems a genomic update is worth the investment.

Contemporary Philosophy and Methods for Studying Speciation and Delimiting Species

Species delimitation is a first step for realizing the extent of biodiversity and is relevant for all downstream applications in biology. The production of large genome-scale datasets for non-model organisms combined with the development of methodological tools have allowed researchers to examine fine-scale processes of speciation such as timing of origin, degree of migration, population-size changes, selection, drift, and recombination. Studies using reptiles and amphibians have, in part, paved the way for the development and use of such methods for exploring speciation and delimitation. While these methodologies have improved our understanding of processes of diversification, researchers are far from agreeing upon a set of criteria to delimit species. In cases where genetic lineages are discovered that are unique to geographic areas, researchers usually agree that two entities exist. Disagreement about taxonomic status often centers on the degree of reproductive isolation between taxa and probability of remaining distinct. However, reproductive isolation is frequently inferred without examining gene flow, understanding the nature of hybrid zones, or determining the amount and type of introgression. Here, we review some of the vexing problems for delimiting reptiles and amphibians, which include isolation by distance, gene flow and differential allelic introgression, hybrid zone dynamics, and the nature of genomic islands of divergence. We also respond to recent literature criticizing model-based species delimitation in North American snakes in the context of these methodological advancements and address how scientists can move forward with studies on speciation.

Excluding Loci With Substitution Saturation Improves Inferences From Phylogenomic Data.

The historical signal in nucleotide sequences becomes eroded over time by substitutions occurring repeatedly at the same sites. This phenomenon, known as substitution saturation, is recognized as one of the primary obstacles to deep-time phylogenetic inference using genome-scale data sets. We present a new test of substitution saturation and demonstrate its performance in simulated and empirical data. For some of the 36 empirical phylogenomic data sets that we examined, we detect substitution saturation in around 50% of loci. We found that saturation tends to be flagged as problematic in loci with highly discordant phylogenetic signals across sites. Within each data set, the loci with smaller numbers of informative sites are more likely to be flagged as containing problematic levels of saturation. The entropy saturation test proposed here is sensitive to high evolutionary rates relative to the evolutionary timeframe, while also being sensitive to several factors known to mislead phylogenetic inference, including short internal branches relative to external branches, short nucleotide sequences, and tree imbalance. Our study demonstrates that excluding loci with substitution saturation can be an effective means of mitigating the negative impact of multiple substitutions on phylogenetic inferences. [Phylogenetic model performance; phylogenomics; substitution model; substitution saturation; test statistics.]

Pseudoreplication in genomic-scale data sets.

In genomic-scale data sets, loci are closely packed within chromosomes and hence provide correlated information. Averaging across loci as if they were independent creates pseudoreplication, which reduces the effective degrees of freedom (df') compared to the nominal degrees of freedom, df. This issue has been known for some time, but consequences have not been systematically quantified across the entire genome. Here, we measured pseudoreplication (quantified by the ratio df'/df) for a common metric of genetic differentiation (FST ) and a common measure of linkage disequilibrium between pairs of loci (r2 ). Based on data simulated using models (SLiM and msprime) that allow efficient forward-in-time and coalescent simulations while precisely controlling population pedigrees, we estimated df' and df'/df by measuring the rate of decline in the variance of mean FST and mean r2 as more loci were used. For both indices, df' increases with Ne and genome size, as expected. However, even for large Ne and large genomes, df' for mean r2 plateaus after a few thousand loci, and a variance components analysis indicates that the limiting factor is uncertainty associated with sampling individuals rather than genes. Pseudoreplication is less extreme for FST , but df'/df ≤0.01 can occur in data sets using tens of thousands of loci. Commonly-used block-jackknife methods consistently overestimated var (FST ), producing very conservative confidence intervals. Predicting df' based on our modelling results as a function of Ne , L, S, and genome size provides a robust way to quantify precision associated with genomic-scale data sets.

In vivo and in vitro human gene essentiality estimations capture contrasting functional constraints.

Gene essentiality estimation is a popular empirical approach to link genotypes to phenotypes. In humans, essentiality is estimated based on loss-of-function (LoF) mutation intolerance, either from population exome sequencing (in vivo) data or CRISPR-based in vitro perturbation experiments. Both approaches identify genes presumed to have detrimental consequences on the organism upon mutation. Are these genes constrained by having key cellular/organismal roles? Do in vivo and in vitro estimations equally recover these constraints? Insights into these questions have important implications in generalizing observations from cell models and interpreting disease risk genes. To empirically address these questions, we integrate genome-scale datasets and compare structural, functional and evolutionary features of essential genes versus genes with extremely high mutational tolerance. We found that essentiality estimates do recover functional constraints. However, the organismal or cellular context of estimation leads to functionally contrasting properties underlying the constraint. Our results suggest that depletion of LoF mutations in human populations effectively captures organismal-level functional constraints not experimentally accessible through CRISPR-based screens. Finally, we identify a set of genes (OrgEssential), which are mutationally intolerant in vivo but highly tolerant in vitro. These genes drive observed functional constraint differences and have an unexpected preference for nervous system expression.

Genetic Landscape of Rare Autoinflammatory Disease Variants in Qatar and Middle Eastern Populations Through the Integration of Whole-Genome and Exome Datasets.

Rare monogenic autoinflammatory diseases are a group of recurrent inflammatory genetic disorders caused due to genetic variants in over 37 genes. While a number of these disorders have been identified and reported in Middle Eastern populations, the carrier frequency of these genetic variants in the Middle Eastern population is not known. The availability of whole-genome and exome datasets of over 1,000 individuals from Qatar persuaded us to explore the genetic epidemiology of rare autoinflammatory genetic variants. We have systematically analyzed genetic variants in genome-scale datasets from Qatar with a compendium of variants associated with autoinflammatory diseases. The variants were systematically reclassified according to the American College of Medical Genetics and Genomics guidelines for interpretation of variant pathogenicity. Our analysis identified seven pathogenic and likely pathogenic variants with significant differences in their allele frequencies compared to the global population. The cumulative carrier frequency of these variants was found to be 2.58%. Furthermore, our analysis revealed that five genes, implicated in rare autoinflammatory diseases, were under natural selection. To the best of our knowledge, this is the first and most comprehensive study on the population-scale analysis and genetic epidemiology of genetic variants that cause rare autoinflammatory disease in Middle Eastern populations.