Rates Of Positive Selection Research Articles

Evolution of chemosensory genes in Colorado potato beetle, Leptinotarsa decemlineata.

Associating with plant hosts is thought to have elevated the diversification of insect herbivores, which comprise the majority of global species diversity. In particular, there is considerable interest in understanding the genetic changes that allow host-plant shifts to occur in pest insects and in determining what aspects of functional genomic diversity impact host-plant breadth. Insect chemoreceptors play a central role in mediating insect-plant interactions, as they directly influence plant detection and sensory stimuli during feeding. Although chemosensory genes evolve rapidly, it is unclear how they evolve in response to host shifts and host specialization. We investigate whether selection at chemosensory genes is linked to host-plant expansion from the buffalo burr, Solanum rostratum, to potato, Solanum tuberosum, in the super-pest Colorado potato beetle (CPB), Leptinotarsa decemlineata (Coleoptera: Chrysomelidae). First, to refine our knowledge of CPB chemosensory genes, we developed novel gene expression data for the antennae and maxillary-labial palps. We then examine patterns of selection at these loci within CPB, as well as compare whether rates of selection vary with respect to 9 closely related, non-pest Leptinotarsa species that vary in diet breadth. We find that rates of positive selection on olfactory receptors are higher in host-plant generalists, and this signal is particularly strong in CPB. These results provide strong candidates for further research on the genetic basis of variation in insect chemosensory performance and novel targets for pest control of a notorious super-pest.

DNA methylation is associated with codon degeneracy in a species of bumblebee

Social insects display extreme phenotypic differences between sexes and castes even though the underlying genome can be almost identical. Epigenetic processes have been proposed as a possible mechanism for mediating these phenotypic differences. Using whole genome bisulfite sequencing of queens, males, and reproductive female workers we have characterised the sex- and caste-specific methylome of the bumblebee Bombus terrestris. We have identified a potential role for DNA methylation in histone modification processes which may influence sex and caste phenotypic differences. We also find differentially methylated genes generally show low levels of DNA methylation which may suggest a separate function for lowly methylated genes in mediating transcriptional plasticity, unlike highly methylated genes which are usually involved in housekeeping functions. We also examined the relationship between the underlying genome and the methylome using whole genome re-sequencing of the same queens and males. We find DNA methylation is enriched at zero-fold degenerate sites. We suggest DNA methylation may be acting as a targeted mutagen at these sites, providing substrate for selection via non-synonymous changes in the underlying genome. However, we did not see any relationship between DNA methylation and rates of positive selection in our samples. In order to fully assess a possible role for DNA methylation in adaptive processes a specifically designed study using natural population data is needed.

Elevated rates of positive selection drive the evolution of pestiferousness in the Colorado potato beetle (Leptinotarsa decemlineata, Say)

Contextualizing evolutionary history and identifying genomic features of an insect that might contribute to its pest status is important in developing early detection and control tactics. In order to understand the evolution of pestiferousness, which we define as the accumulation of traits that contribute to an insect population's success in an agroecosystem, we tested the importance of known genomic properties associated with rapid adaptation in the Colorado potato beetle (CPB), Leptinotarsa decemlineata Say. Within the leaf beetle genus Leptinotarsa, only CPB, and a few populations therein, has risen to pest status on cultivated nightshades, Solanum. Using whole genomes from ten closely related Leptinotarsa species native to the United States, we reconstructed a high-quality species tree and used this phylogenetic framework to assess evolutionary patterns in four genomic features of rapid adaptation: standing genetic variation, gene family expansion and contraction, transposable element abundance and location, and positive selection at protein-coding genes. Throughout approximately 20 million years of history, Leptinotarsa species show little evidence of gene family turnover and transposable element variation. However, there is a clear pattern of CPB experiencing higher rates of positive selection on protein-coding genes. We determine that these rates are associated with greater standing genetic variation due to larger effective population size, which supports the theory that the demographic history contributes to rates of protein evolution. Furthermore, we identify a suite of coding genes under positive selection that are putatively associated with pestiferousness in the Colorado potato beetle lineage. They are involved in the biological processes of xenobiotic detoxification, chemosensation and hormone function.

Comparison of methods for the detection of outliers and associated biomarkers in mislabeled omics data

BackgroundPrevious studies have reported that labeling errors are not uncommon in omics data. Potential outliers may severely undermine the correct classification of patients and the identification of reliable biomarkers for a particular disease. Three methods have been proposed to address the problem: sparse label-noise-robust logistic regression (Rlogreg), robust elastic net based on the least trimmed square (enetLTS), and Ensemble. Ensemble is an ensembled classification based on distinct feature selection and modeling strategies. The accuracy of biomarker selection and outlier detection of these methods needs to be evaluated and compared so that the appropriate method can be chosen.ResultsThe accuracy of variable selection, outlier identification, and prediction of three methods (Ensemble, enetLTS, Rlogreg) were compared for simulated and an RNA-seq dataset. On simulated datasets, Ensemble had the highest variable selection accuracy, as measured by a comprehensive index, and lowest false discovery rate among the three methods. When the sample size was large and the proportion of outliers was ≤5%, the positive selection rate of Ensemble was similar to that of enetLTS. However, when the proportion of outliers was 10% or 15%, Ensemble missed some variables that affected the response variables.Overall, enetLTS had the best outlier detection accuracy with false positive rates < 0.05 and high sensitivity, and enetLTS still performed well when the proportion of outliers was relatively large. With 1% or 2% outliers, Ensemble showed high outlier detection accuracy, but with higher proportions of outliers Ensemble missed many mislabeled samples. Rlogreg and Ensemble were less accurate in identifying outliers than enetLTS. The prediction accuracy of enetLTS was better than that of Rlogreg. Running Ensemble on a subset of data after removing the outliers identified by enetLTS improved the variable selection accuracy of Ensemble.ConclusionsWhen the proportion of outliers is ≤5%, Ensemble can be used for variable selection. When the proportion of outliers is > 5%, Ensemble can be used for variable selection on a subset after removing outliers identified by enetLTS. For outlier identification, enetLTS is the recommended method. In practice, the proportion of outliers can be estimated according to the inaccuracy of the diagnostic methods used.

Inconsistency of Classical Penalized Likelihood Approaches under Endogeneity

With the high speed development of information technology, contemporary data from a variety of fields becomes extremely large. The number of features in many datasets is well above the sample size and is called high dimensional data. In statistics, variable selection approaches are required to extract the efficacious information from high dimensional data. The most popular approach is to add a penalty function coupled with a tuning parameter to the log likelihood function, which is called penalized likelihood method. However, almost all of penalized likelihood approaches only consider noise accumulation and supurious correlation whereas ignoring the endogeneity which also appeared frequently in high dimensional space. In this paper, we explore the cause of endogeneity and its influence on penalized likelihood approaches. Simulations based on five classical pe-nalized approaches are provided to vindicate their inconsistency under endogeneity. The results show that the positive selection rate of all five approaches increased gradually but the false selection rate does not consistently decrease when endogenous variables exist, that is, they do not satisfy the selection consistency.

Exceptionally high rates of positive selection on the rbcL gene in the genus Ilex (Aquifoliaceae)

BackgroundThe genus Ilex (Aquifoliaceae) has a near-cosmopolitan distribution in mesic habitats from tropical to temperate lowlands and in alpine forests. It has a high rate of hybridization and plastid capture, and comprises four geographically structured plastid groups. A previous study showed that the plastid rbcL gene, coding for the large subunit of Rubisco, has a particularly high rate of non-synonymous substitutions in Ilex, when compared with other plant lineages. This suggests a strong positive selection on rbcL, involved in yet unknown adaptations. We therefore investigated positive selection on rbcL in 240 Ilex sequences from across the global range.ResultsThe rbcL gene shows a much higher rate of positive selection in Ilex than in any other plant lineage studied so far (> 3000 species) by tests in both PAML and SLR. Most positively selected residues are on the surface of the folded large subunit, suggesting interaction with other subunits and associated chaperones, and coevolution between positively selected residues is prevalent, indicating compensatory mutations to recover molecular stability. Coevolution between positively selected sites to restore global stability is common.ConclusionsThis study has confirmed the predicted high incidence of positively selected residues in rbcL in Ilex, and shown that this is higher than in any other plant lineage studied so far. The causes and consequences of this high incidence are unclear, but it is probably associated with the similarly high incidence of hybridization and introgression in Ilex, even between distantly related lineages, resulting in large cytonuclear discordance in the phylogenies.

An update of the suicide plasmid-mediated genome editing system in Corynebacterium glutamicum.

Summary Corynebacterium glutamicum is an important industrial microorganism, but the availability of tools for its genetic modification has lagged compared to other model microorganisms such as Escherichia coli. Despite great progress in CRISPR‐based technologies, the most feasible genome editing method in C. glutamicum is suicide plasmid‐mediated, the editing efficiency of which is low due to high false‐positive rates of sacB counter selection, and the requirement for tedious two‐round selection and verification of rare double‐cross‐over events. In this study, an rpsL mutant conferring streptomycin resistance was harnessed for counter selection, significantly increasing the positive selection rate. More importantly, with the aid of high selection efficiencies through the use of antibiotics, namely kanamycin and streptomycin, the two‐step verification strategy can be simplified to just one‐step verification of the final edited strain. As proof of concept, a 2.5‐kb DNA fragment comprising aroG fbr pheA fbr expressing cassettes was integrated into the genome of C. glutamicum, with an efficiency of 20% out of the theoretical 50%. The resulting strain produced 110 mg l−1 l‐tyrosine in shake‐flask fermentation. This updated suicide plasmid‐mediated genome editing system will greatly facilitate genetic manipulations including single nucleotide mutation, gene deletion and gene insertion in C. glutamicum and can be easily applied to other microbes.

Stopping rules for mutual information-based feature selection

In recent years feature selection methods based on mutual information have attracted a significant attention. Most of the proposed methods are based on sequential forward search which add at each step a feature which is most relevant in explaining a class variable when considered together with the already chosen features. Such procedures produce ranking of features ordered according to their relevance. However significant limitation of all existing methods is lack of stopping rules which separate relevant features placed on the top of the ranking list from irrelevant ones. Finding an appropriate stopping rule is particularly important in domains where one wants to precisely determine the set of features affecting the class variable and discard the irrelevant ones (e.g. in genome-wide association studies the goal is to precisely determine mutations in DNA affecting the disease). In this work we propose stopping rules which are based on distribution of approximation of conditional mutual information given that all relevant features have been already selected. We show that the distribution is approximately chi square with appropriate number of degrees of freedom provided features are discretized into moderate number of bins. The proposed stopping rules are based on quantiles of the distribution and related p-values which are compared with thresholds used in multiple hypothesis testing. Importantly the proposed methods do not require additional validation data and are independent from the classifier. The extensive simulation experiments indicate that the rules separate relevant features from the irrelevant ones. We show experimentally that Positive Selection Rate (fraction of features correctly selected as relevant with respect to all relevant features) approaches 1, when sample size increases. At the same time, False Discovery Rate (fraction of irrelevant features selected with respect to all selected features) is controlled. The experiments on 17 benchmark datasets indicate that the classification models, built on features selected by the proposed methods, in 13 cases achieve significantly higher accuracy than the models based on all available features.

Analysis of natural recombination and host-related evolutionary dynamics of avian avulavirus 1 isolates based on positive and negative selection from 1948 to 2017.

Adaptation and evolution of avian avulavirus 1, or Newcastle disease virus (NDV), has led to tremendous economic losses worldwide. The occurrence of natural recombination and selection pressure has been traced for NDV based on a few recent reports, but a dominant pattern based on genomic characteristics is lacking. Here, we used bioinformatics tools to search for evidence of recombination in all of the available complete genome sequence of NDV (462 sequences) using RDP4 software. Geographical linkage and host cell relationships of recombinant viruses were also investigated, and a study of the adaptive evolution of avian avulavirus 1 was performed. The results revealed that recombination events could occur in any gene fragment of the NDV genome. Moreover, class I NDV isolates from wild birds could associate to generate a putative recombinant virus with a class II genome backbone. In addition, not only avirulent-virulent hybrid genotypes but also virulent-virulent natural recombinant NDV viruses were generated. Investigation of geographic relationships of recombinant isolates indicated that the highest rate of recombination occurs in Asia and the Middle East, which can be influenced by vaccination failure, evasion of the immune response, live-bird markets, and the bird trade. The M and NP genes were found to have higher negative selection rates than the other genes, which might lead to the deletion of inadaptable sequences and result in more conserved sequences. Based on our analysis, the highest rate of positive selection was observed in the L, F and HN genes, which we suggest could lead to the occurrence of evolved viruses with high pathogenicity and a better chance of survival under extreme conditions.

Hypothesis testing sure independence screening for nonparametric regression

In this paper we develop a sure independence screening method based on hypothesis testing (HT-SIS) in a general nonparametric regression model. The ranking utility is based on a powerful test statistic for the hypothesis of predictive significance of each available covariate. The sure screening property of HT-SIS is established, demonstrating that all active predictors will be retained with high probability as the sample size increases. The threshold parameter is chosen in a theoretically justified manner based on the desired false positive selection rate. Simulation results suggest that the proposed method performs competitively against procedures found in the literature of screening for several models, and outperforms them in some scenarios. A real dataset of microarray gene expressions is analyzed.

Robust exponential squared loss-based variable selection for high-dimensional single-index varying-coefficient model

Robust variable selection procedure through penalized regression has been gaining increased attention in the literature. They can be used to perform variable selection and are expected to yield robust estimates. In this article, we propose a robust variable selection procedure for high-dimensional single-index varying-coefficient model using penalized exponential squared loss. The proposed procedure simultaneously selects significant covariates with functional coefficients and local significant variables with parametric coefficients. With proper choices of penalty functions and regularization parameters, we show the asymptotic normality of the resulting estimate and further demonstrate that the proposed procedures perform as well as an oracle procedure. Our simulation studies reveal that our proposed method performs similarly to the oracle method in terms of the model error and the positive selection rate even in the presence of influential points. In contrast, other existing procedures have a much lower noncausal selection rate. Our analysis unravels the discrepancies of using our robust method versus the other penalized regression method, underscoring the importance of developing and applying robust penalized regression methods.

Respiratory Syncytial Virus whole-genome sequencing identifies convergent evolution of sequence duplication in the C-terminus of the G gene.

Respiratory Syncytial Virus (RSV) is responsible for considerable morbidity and mortality worldwide and is the most important respiratory viral pathogen in infants. Extensive sequence variability within and between RSV group A and B viruses and the ability of multiple clades and sub-clades of RSV to co-circulate are likely mechanisms contributing to the evasion of herd immunity. Surveillance and large-scale whole-genome sequencing of RSV is currently limited but would help identify its evolutionary dynamics and sites of selective immune evasion. In this study, we performed complete-genome next-generation sequencing of 92 RSV isolates from infants in central Tennessee during the 2012–2014 RSV seasons. We identified multiple co-circulating clades of RSV from both the A and B groups. Each clade is defined by signature N- and O-linked glycosylation patterns. Analyses of specific RSV genes revealed high rates of positive selection in the attachment (G) gene. We identified RSV-A viruses in circulation with and without a recently reported 72-nucleotide G gene sequence duplication. Furthermore, we show evidence of convergent evolution of G gene sequence duplication and fixation over time, which suggests a potential fitness advantage of RSV with the G sequence duplication.

Expression Divergence Is Correlated with Sequence Evolution but Not Positive Selection in Conifers.

The evolutionary and genomic determinants of sequence evolution in conifers are poorly understood, and previous studies have found only limited evidence for positive selection. Using RNAseq data, we compared gene expression profiles to patterns of divergence and polymorphism in 44 seedlings of lodgepole pine (Pinus contorta) and 39 seedlings of interior spruce (Picea glauca × engelmannii) to elucidate the evolutionary forces that shape their genomes and their plastic responses to abiotic stress. We found that rapidly diverging genes tend to have greater expression divergence, lower expression levels, reduced levels of synonymous site diversity, and longer proteins than slowly diverging genes. Similar patterns were identified for the untranslated regions, but with some exceptions. We found evidence that genes with low expression levels had a larger fraction of nearly neutral sites, suggesting a primary role for negative selection in determining the association between evolutionary rate and expression level. There was limited evidence for differences in the rate of positive selection among genes with divergent versus conserved expression profiles and some evidence supporting relaxed selection in genes diverging in expression between the species. Finally, we identified a small number of genes that showed evidence of site-specific positive selection using divergence data alone. However, estimates of the proportion of sites fixed by positive selection (α) were in the range of other plant species with large effective population sizes suggesting relatively high rates of adaptive divergence among conifers.

Biallelic T cell receptor rearrangement enhances the efficiency of thymocyte development and positive selection. (HEM4P.234)

Abstract Biallelic rearrangement of TCR loci has been proposed to increase the likelihood of productive gene rearrangement, thereby promoting thymocyte survival. When allelic exclusion is incomplete, this process also results in dual TCR-expressing T cells, which may increase repertoire diversity, but may also increase autoreactive potential. We investigated the effects of biallelic TCR gene rearrangement by comparing wildtype (wt) T cells to those from mice hemizygous for the genes encoding TCR α, TCR β, or both. Using bone marrow chimera competition assays we found that Tcrb hemizygous thymocytes were impaired in the DN1 to DN2 transition relative to wt cells. In addition, Tcrb hemizygous mice exhibited a higher frequency of T cells committed to the γδ lineage. Bone marrow chimera competition assays revealed a dramatic increase in the ratio of wt thymocytes to Tcra hemizygous thymocytes between the pre-selection and post-selection DP compartments, demonstrating that biallelic Tcra rearrangement increases the rate of positive selection. Despite their impairments in thymocyte development, mice hemizygous for both Tcra and Tcrb generated polyclonal T cell repertoires similar to wt mice as assessed by numbers of antigen-specific naïve T cells, responses to multiple self and foreign antigens, and TCRβ sequence diversity. Thus, biallelic rearrangement of Tcra and Tcrb loci enhances the efficiency of thymocyte development, but is not necessary for normal TCR repertoire diversification.

Patterns of Selection in Plant Genomes

Plants show a wide range of variation in mating system, ploidy level, and demographic history, allowing for unique opportunities to investigate the evolutionary and genetic factors affecting genome-wide patterns of positive and negative selection. In this review, we highlight recent progress in our understanding of the extent and nature of selection on plant genomes. We discuss differences in selection as they relate to variation in demography, recombination, mating system, and ploidy. We focus on the population genetic consequences of these factors and argue that, although variation in the magnitude of purifying selection is well documented, quantifying rates of positive selection and disentangling the relative importance of recombination, demography, and ploidy are ongoing challenges. Large-scale comparative studies that examine the relative and joint importance of these processes, combined with explicit models of population history and selection, are key and feasible goals for future work.

Robust Variable Selection With Exponential Squared Loss

Robust variable selection procedures through penalized regression have been gaining increased attention in the literature. They can be used to perform variable selection and are expected to yield robust estimates. However, to the best of our knowledge, the robustness of those penalized regression procedures has not been well characterized. In this article, we propose a class of penalized robust regression estimators based on exponential squared loss. The motivation for this new procedure is that it enables us to characterize its robustness in a way that has not been done for the existing procedures, while its performance is near optimal and superior to some recently developed methods. Specifically, under defined regularity conditions, our estimators are -consistent and possess the oracle property. Importantly, we show that our estimators can achieve the highest asymptotic breakdown point of 1/2 and that their influence functions are bounded with respect to the outliers in either the response or the covariate domain. We performed simulation studies to compare our proposed method with some recent methods, using the oracle method as the benchmark. We consider common sources of influential points. Our simulation studies reveal that our proposed method performs similarly to the oracle method in terms of the model error and the positive selection rate even in the presence of influential points. In contrast, other existing procedures have a much lower noncausal selection rate. Furthermore, we reanalyze the Boston Housing Price Dataset and the Plasma Beta-Carotene Level Dataset that are commonly used examples for regression diagnostics of influential points. Our analysis unravels the discrepancies of using our robust method versus the other penalized regression method, underscoring the importance of developing and applying robust penalized regression methods.

Frequent adaptation and the McDonald–Kreitman test

Population genomic studies have shown that genetic draft and background selection can profoundly affect the genome-wide patterns of molecular variation. We performed forward simulations under realistic gene-structure and selection scenarios to investigate whether such linkage effects impinge on the ability of the McDonald-Kreitman (MK) test to infer the rate of positive selection (α) from polymorphism and divergence data. We find that in the presence of slightly deleterious mutations, MK estimates of α severely underestimate the true rate of adaptation even if all polymorphisms with population frequencies under 50% are excluded. Furthermore, already under intermediate rates of adaptation, genetic draft substantially distorts the site frequency spectra at neutral and functional sites from the expectations under mutation-selection-drift balance. MK-type approaches that first infer demography from synonymous sites and then use the inferred demography to correct the estimation of α obtain almost the correct α in our simulations. However, these approaches typically infer a severe past population expansion although there was no such expansion in the simulations, casting doubt on the accuracy of methods that infer demography from synonymous polymorphism data. We propose a simple asymptotic extension of the MK test that yields accurate estimates of α in our simulations and should provide a fruitful direction for future studies.

The mode and tempo of hepatitis C virus evolution within and among hosts

BackgroundHepatitis C virus (HCV) is a rapidly-evolving RNA virus that establishes chronic infections in humans. Despite the virus' public health importance and a wealth of sequence data, basic aspects of HCV molecular evolution remain poorly understood. Here we investigate three sets of whole HCV genomes in order to directly compare the evolution of whole HCV genomes at different biological levels: within- and among-hosts. We use a powerful Bayesian inference framework that incorporates both among-lineage rate heterogeneity and phylogenetic uncertainty into estimates of evolutionary parameters.ResultsMost of the HCV genome evolves at ~0.001 substitutions/site/year, a rate typical of RNA viruses. The antigenically-important E1/E2 genome region evolves particularly quickly, with correspondingly high rates of positive selection, as inferred using two related measures. Crucially, in this region an exceptionally higher rate was observed for within-host evolution compared to among-host evolution. Conversely, higher rates of evolution were seen among-hosts for functionally relevant parts of the NS5A gene. There was also evidence for slightly higher evolutionary rate for HCV subtype 1a compared to subtype 1b.ConclusionsUsing new statistical methods and comparable whole genome datasets we have quantified, for the first time, the variation in HCV evolutionary dynamics at different scales of organisation. This confirms that differences in molecular evolution between biological scales are not restricted to HIV and may represent a common feature of chronic RNA viral infection. We conclude that the elevated rate observed in the E1/E2 region during within-host evolution more likely results from the reversion of host-specific adaptations (resulting in slower long-term among-host evolution) than from the preferential transmission of slowly-evolving lineages.

Tournament screening cum EBIC for feature selection with high-dimensional feature spaces

The feature selection characterized by relatively small sample size and extremely high-dimensional feature space is common in many areas of contemporary statistics. The high dimensionality of the feature space causes serious difficulties: (i) the sample correlations between features become high even if the features are stochastically independent; (ii) the computation becomes intractable. These difficulties make conventional approaches either inapplicable or inefficient. The reduction of dimensionality of the feature space followed by low dimensional approaches appears the only feasible way to tackle the problem. Along this line, we develop in this article a tournament screening cum EBIC approach for feature selection with high dimensional feature space. The procedure of tournament screening mimics that of a tournament. It is shown theoretically that the tournament screening has the sure screening property, a necessary property which should be satisfied by any valid screening procedure. It is demonstrated by numerical studies that the tournament screening cum EBIC approach enjoys desirable properties such as having higher positive selection rate and lower false discovery rate than other approaches.

Multiple-Interval Mapping for Quantitative Trait Loci With a Spike in the Trait Distribution

For phenotypic distributions where many individuals share a common value-such as survival time following a pathogenic infection-a spike occurs at that common value. This spike affects quantitative trait loci (QTL) mapping methodologies and causes standard approaches to perform suboptimally. In this article, we develop a multiple-interval mapping (MIM) procedure based on mixture generalized linear models (GLIMs). An extended Bayesian information criterion (EBIC) is used for model selection. To demonstrate its utility, this new approach is compared to single-QTL models that appropriately handle the phenotypic distribution. The method is applied to data from Listeria infection as well as data from simulation studies. Compared to the single-QTL model, the findings demonstrate that the MIM procedure greatly improves the efficiency in terms of positive selection rate and false discovery rate. The method developed has been implemented using functions in R and is freely available to download and use.