Genome-scale Data Research Articles

Genomic scale data shows that Parastacus nicoleti encompasses more than one species of burrowing continental crayfishes and that lineage divergence occurred with and without gene flow

Delimiting species is a challenge, especially in scenarios of diversification with gene flow and when species are now allopatric where reproductive isolation cannot be directly tested. Continental burrowing crayfishes of the genus Parastacus present a disjoint distribution in southern South America. One of the species is P. nicoleti, which lives in underground waters in swampy and wooded areas of southern Chile. A previous assessment based on mitochondrial DNA sequences suggest that the taxon may represent a species complex. Here, using thousands of nuclear genomic single-nucleotide polymorphisms obtained via RADSeq from 81 specimens collected at 27 localities throughout the distributional range of the species, we apply an integrative species delimitation approach to test species boundaries and to investigate some aspects of the speciation process. Our analyses corroborate previous results; a scenario that we favor suggests that the P. nicoleti encompasses seven distinct species. Additionally, demographic analyses show that the distinct species have followed distinct trajectories in size change during the last 17.5 million years and that speciation in this group occurred both in strict isolation as well as in the presence of gene flow.

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.]

Prediction of New Risk Genes and Potential Drugs for Rheumatoid Arthritis from Multiomics Data.

Rheumatoid arthritis (RA) is an autoimmune and inflammatory disease for which there is a lack of therapeutic options. Genome-wide association studies (GWASs) have identified over 100 genetic loci associated with RA susceptibility; however, the most causal risk genes (RGs) associated with, and molecular mechanism underlying, RA remain unknown. In this study, we collected 95 RA-associated loci from multiple GWASs and detected 87 candidate high-confidence risk genes (HRGs) from these loci via integrated multiomics data (the genome-scale chromosome conformation capture data, enhancer-promoter linkage data, and gene expression data) using the Bayesian integrative risk gene selector (iRIGS). Analysis of these HRGs indicates that these genes were indeed, markedly associated with different aspects of RA. Among these, 36 and 46 HRGs have been reported to be related to RA and autoimmunity, respectively. Meanwhile, most novel HRGs were also involved in the significantly enriched RA-related biological functions and pathways. Furthermore, drug repositioning prediction of the HRGs revealed three potential targets (ERBB2, IL6ST, and MAPK1) and nine possible drugs for RA treatment, of which two IL-6 receptor antagonists (tocilizumab and sarilumab) have been approved for RA treatment and four drugs (trastuzumab, lapatinib, masoprocol, and arsenic trioxide) have been reported to have a high potential to ameliorate RA. In summary, we believe that this study provides new clues for understanding the pathogenesis of RA and is important for research regarding the mechanisms underlying RA and the development of therapeutics for this condition.

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.

Phylogenomic analysis of evolutionary relationships in Ranitomeya poison frogs (Family Dendrobatidae) using ultraconserved elements

The use of genome-scale data in phylogenetics has enabled recent strides in determining the relationships between taxa that are taxonomically problematic because of extensive morphological variation. Here, we employ a phylogenomic approach to infer evolutionary relationships within Ranitomeya (Anura: Dendrobatidae), an Amazonian lineage of poison frogs consisting of 16 species with remarkable diversity in color pattern, range size, and parental care behavior. We infer phylogenies with all described species of Ranitomeya from ultraconserved nuclear genomic elements (UCEs) and also estimate divergence times. Our results differ from previous analyses regarding interspecific relationships. Notably, we find that R. toraro and R. defleri are not sister species but rather distantly related, contrary to previous analyses based on smaller genetic datasets. We recover R. uakarii as paraphyletic, designate certain populations formerly assigned to R. fantastica from Peru as R. summersi, and transfer the French Guianan and eastern Brazilian R. amazonica populations to R. variabilis. By clarifying both inter- and intraspecific relationships within Ranitomeya, our study paves the way for future tests of hypotheses on color pattern evolution and historical biogeography.

A species-level timeline of mammal evolution integrating phylogenomic data.

High-throughput sequencing projects generate genome-scale sequence data for species-level phylogenies1-3. However, state-of-the-art Bayesian methods for inferring timetrees are computationally limited to small datasets and cannot exploit the growing number of available genomes4. In the case of mammals, molecular-clock analyses of limited datasets have produced conflicting estimates of clade ages with large uncertainties5,6, and thus the timescale of placental mammal evolution remains contentious7-10. Here we develop a Bayesian molecular-clock dating approach to estimate a timetree of 4,705 mammal species integrating information from 72 mammal genomes. We show that increasingly larger phylogenomic datasets produce diversification time estimates with progressively smaller uncertainties, facilitating precise tests of macroevolutionary hypotheses. For example, we confidently reject an explosive model of placental mammal origination in the Palaeogene8 and show that crown Placentalia originated in the Late Cretaceous with unambiguous ordinal diversification in the Palaeocene/Eocene. Our Bayesian methodology facilitates analysis of complete genomes and thousands of species within an integrated framework, making it possible to address hitherto intractable research questions on species diversifications. This approach can be used to address other contentious cases of animal and plant diversifications that require analysis of species-level phylogenomic datasets.

Limited Introgression between Rock-Wallabies with Extensive Chromosomal Rearrangements.

Chromosome rearrangements can result in the rapid evolution of hybrid incompatibilities. Robertsonian fusions, particularly those with monobrachial homology, can drive reproductive isolation amongst recently diverged taxa. The recent radiation of rock-wallabies (genus Petrogale) is an important model to explore the role of Robertsonian fusions in speciation. Here, we pursue that goal using an extensive sampling of populations and genomes of Petrogale from north-eastern Australia. In contrast to previous assessments using mitochondrial DNA or nuclear microsatellite loci, genomic data are able to separate the most closely related species and to resolve their divergence histories. Both phylogenetic and population genetic analyses indicate introgression between two species that differ by a single Robertsonian fusion. Based on the available data, there is also evidence for introgression between two species which share complex chromosomal rearrangements. However, the remaining results show no consistent signature of introgression amongst species pairs and where evident, indicate generally low introgression overall. X-linked loci have elevated divergence compared with autosomal loci indicating a potential role for genic evolution to produce reproductive isolation in concert with chromosome change. Our results highlight the value of genome scale data in evaluating the role of Robertsonian fusions and structural variation in divergence, speciation, and patterns of molecular evolution.

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.

Genome-Scale Data Reveal Deep Lineage Divergence and a Complex Demographic History in the Texas Horned Lizard (Phrynosoma cornutum) throughout the Southwestern and Central United States.

The southwestern and central United States serve as an ideal region to test alternative hypotheses regarding biotic diversification. Genomic data can now be combined with sophisticated computational models to quantify the impacts of paleoclimate change, geographic features, and habitat heterogeneity on spatial patterns of genetic diversity. In this study, we combine thousands of genotyping-by-sequencing (GBS) loci with mtDNA sequences (ND1) from the Texas horned lizard (Phrynosoma cornutum) to quantify relative support for different catalysts of diversification. Phylogenetic and clustering analyses of the GBS data indicate support for at least three primary populations. The spatial distribution of populations appears concordant with habitat type, with desert populations in AZ and NM showing the largest genetic divergence from the remaining populations. The mtDNA data also support a divergent desert population, but other relationships differ and suggest mtDNA introgression. Genotype–environment association with bioclimatic variables supports divergence along precipitation gradients more than along temperature gradients. Demographic analyses support a complex history, with introgression and gene flow playing an important role during diversification. Bayesian multispecies coalescent analyses with introgression (MSci) analyses also suggest that gene flow occurred between populations. Paleo-species distribution models support two southern refugia that geographically correspond to contemporary lineages. We find that divergence times are underestimated and population sizes are overestimated when introgression occurred and is ignored in coalescent analyses, and furthermore, inference of ancient introgression events and demographic history is sensitive to inclusion of a single recently admixed sample. Our analyses cannot refute the riverine barrier or glacial refugia hypotheses. Results also suggest that populations are continuing to diverge along habitat gradients. Finally, the strong evidence of admixture, gene flow, and mtDNA introgression among populations suggests that P. cornutum should be considered a single widespread species under the General Lineage Species Concept.

Ecological Divergence and the History of Gene Flow in the Nearctic Milksnakes (Lampropeltis triangulum Complex).

Many phylogeographic studies on species with large ranges have found genetic-geographic structure associated with changes in habitat and physical barriers preventing or reducing gene flow. These interactions with geographic space, contemporary and historical climate, and biogeographic barriers have complex effects on contemporary population genetic structure and processes of speciation. While allopatric speciation at biogeographic barriers is considered the primary mechanism for generating species, more recently it has been shown that parapatric modes of divergence may be equally or even more common. With genomic data and better modeling capabilities, we can more clearly define causes of speciation in relation to biogeography and migration between lineages, the location of hybrid zones with respect to the ecology of parental lineages, and differential introgression of genes between taxa. Here, we examine the origins of three Nearctic milksnakes (Lampropeltis elapsoides, Lampropeltis triangulum and Lampropeltis gentilis) using genome-scale data to better understand species diversification. Results from artificial neural networks show that a mix of a strong biogeographic barrier, environmental changes, and physical space has affected genetic structure in these taxa. These results underscore conspicuous environmental changes that occur as the sister taxa L. triangulum and L. gentilis diverged near the Great Plains into the forested regions of the Eastern Nearctic. This area has been recognized as a region for turnover for many vertebrate species, but as we show here the contemporary boundary does not isolate these sister species. These two species likely formed in the mid-Pleistocene and have remained partially reproductively isolated over much of this time, showing differential introgression of loci. We also demonstrate that when L. triangulum and L. gentilis are each in contact with the much older L. elapsoides, some limited gene flow has occurred. Given the strong agreement between nuclear and mtDNA genomes, along with estimates of ecological niche, we suggest that all three lineages should continue to be recognized as unique species. Furthermore, this work emphasizes the importance of considering complex modes of divergence and differential allelic introgression over a complex landscape when testing mechanisms of speciation. [Cline; delimitation; Eastern Nearctic; Great Plains; hybrids; introgression; speciation.].

Widespread introgression across a phylogeny of 155 Drosophila genomes

Genome-scale sequence data have invigorated the study of hybridization and introgression, particularly in animals. However, outside of a few notable cases, we lack systematic tests for introgression at a larger phylogenetic scale across entire clades. Here, we leverage 155 genome assemblies from 149 species to generate a fossil-calibrated phylogeny and conduct multilocus tests for introgression across 9 monophyletic radiations within the genus Drosophila. Using complementary phylogenomic approaches, we identify widespread introgression across the evolutionary history of Drosophila. Mapping gene-tree discordance onto the phylogeny revealed that both ancient and recent introgression has occurred across most of the 9 clades that we examined. Our results provide the first evidence of introgression occurring across the evolutionary history of Drosophila and highlight the need to continue to study the evolutionary consequences of hybridization and introgression in this genus and across the tree of life.

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.

New Method for Genome-Scale Functional Genomic Study in Bacteria with Superior Performance: CRISPR Interference Screen.

High-throughput genetic screens based on CRISPR/Cas9 technology are powerful tools to genome-wide identify gene function and genotype-phenotype association. Here, we describe a detailed protocol for conducting and evaluating pooled CRISPR screens interfering with gene expression in Escherichia coli. We provide step-by-step instructions for guide RNA library design and construction, genome-scale screening and next-generation sequencing data processing. This tool outperforms transposon sequencing (Tn-seq) with similar library sizes and short gene length. The workflow can be used in follow-up studies implemented in other bacteria systems.

Genome-scale actions of master regulators directing skeletal development.

The mammalian skeleton develops through two distinct modes of ossification: intramembranous ossification and endochondral ossification. During the process of skeletal development, SRY-box containing gene 9 (Sox9), runt-related transcription factor 2 (Runx2), and Sp7 work as master transcription factors (TFs) or transcriptional regulators, underlying cell fate specification of the two distinct populations: bone-forming osteoblasts and cartilage-forming chondrocytes. In the past two decades, core transcriptional circuits underlying skeletal development have been identified mainly through mouse genetics and biochemical approaches. Recently emerging next-generation sequencer (NGS)-based studies have provided genome-scale views on the gene regulatory landscape programmed by the master TFs/transcriptional regulators. With particular focus on Sox9, Runx2, and Sp7, this review aims to discuss the gene regulatory landscape in skeletal development, which has been identified by genome-scale data, and provide future perspectives in this field.

ORTHOSCOPE*: A Phylogenetic Pipeline to Infer Gene Histories from Genome-Wide Data.

Comparative genome-scale analyses of protein-coding gene sequences are employed to examine evidence for whole-genome duplication and horizontal gene transfer. For this purpose, an orthogroup should be delineated to infer evolutionary history regarding each gene, and results of all orthogroup analyses need to be integrated to infer a genome-scale history. An orthogroup is a set of genes descended from a single gene in the last common ancestor of all species under consideration. However, such analyses confront several problems: 1) Analytical pipelines to infer all gene histories with methods comparing species and gene trees are not fully developed, and 2) without detailed analyses within orthogroups, evolutionary events of paralogous genes in the same orthogroup cannot be distinguished for genome-wide integration of results derived from multiple orthogroup analyses. Here I present an analytical pipeline, ORTHOSCOPE* (star), to infer evolutionary histories of animal/plant genes from genome-scale data. ORTHOSCOPE* estimates a tree for a specified gene, detects speciation/gene duplication events that occurred at nodes belonging to only one lineage leading to a species of interest, and then integrates results derived from gene trees estimated for all query genes in genome-wide data. Thus, ORTHOSCOPE* can be used to detect species nodes just after whole-genome duplications as a first step of comparative genomic analyses. Moreover, by examining the presence or absence of genes belonging to species lineages with dense taxon sampling available from the ORTHOSCOPE web version, ORTHOSCOPE* can detect genes lost in specific lineages and horizontal gene transfers. This pipeline is available at https://github.com/jun-inoue/ORTHOSCOPE_STAR.

Staphylococcusaureus Transcriptome Data and Metabolic Modelling Investigate the Interplay of Ser/Thr Kinase PknB, Its Phosphatase Stp, the glmR/yvcK Regulon and the cdaA Operon for Metabolic Adaptation.

Serine/threonine kinase PknB and its corresponding phosphatase Stp are important regulators of many cell functions in the pathogen S. aureus. Genome-scale gene expression data of S. aureus strain NewHG (sigB+) elucidated their effect on physiological functions. Moreover, metabolic modelling from these data inferred metabolic adaptations. We compared wild-type to deletion strains lacking pknB, stp or both. Ser/Thr phosphorylation of target proteins by PknB switched amino acid catabolism off and gluconeogenesis on to provide the cell with sufficient components. We revealed a significant impact of PknB and Stp on peptidoglycan, nucleotide and aromatic amino acid synthesis, as well as catabolism involving aspartate transaminase. Moreover, pyrimidine synthesis was dramatically impaired by stp deletion but only slightly by functional loss of PknB. In double knockouts, higher activity concerned genes involved in peptidoglycan, purine and aromatic amino acid synthesis from glucose but lower activity of pyrimidine synthesis from glucose compared to the wild type. A second transcriptome dataset from S. aureus NCTC 8325 (sigB−) validated the predictions. For this metabolic adaptation, PknB was found to interact with CdaA and the yvcK/glmR regulon. The involved GlmR structure and the GlmS riboswitch were modelled. Furthermore, PknB phosphorylation lowered the expression of many virulence factors, and the study shed light on S. aureus infection processes.

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.

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.