Mutation Rate Variation Research Articles

Regularized sequence-context mutational trees capture variation in mutation rates across the human genome.

Germline mutation is the mechanism by which genetic variation in a population is created. Inferences derived from mutation rate models are fundamental to many population genetics methods. Previous models have demonstrated that nucleotides flanking polymorphic sites-the local sequence context-explain variation in the probability that a site is polymorphic. However, limitations to these models exist as the size of the local sequence context window expands. These include a lack of robustness to data sparsity at typical sample sizes, lack of regularization to generate parsimonious models and lack of quantified uncertainty in estimated rates to facilitate comparison between models. To address these limitations, we developed Baymer, a regularized Bayesian hierarchical tree model that captures the heterogeneous effect of sequence contexts on polymorphism probabilities. Baymer implements an adaptive Metropolis-within-Gibbs Markov Chain Monte Carlo sampling scheme to estimate the posterior distributions of sequence-context based probabilities that a site is polymorphic. We show that Baymer accurately infers polymorphism probabilities and well-calibrated posterior distributions, robustly handles data sparsity, appropriately regularizes to return parsimonious models, and scales computationally at least up to 9-mer context windows. We demonstrate application of Baymer in three ways-first, identifying differences in polymorphism probabilities between continental populations in the 1000 Genomes Phase 3 dataset, second, in a sparse data setting to examine the use of polymorphism models as a proxy for de novo mutation probabilities as a function of variant age, sequence context window size, and demographic history, and third, comparing model concordance between different great ape species. We find a shared context-dependent mutation rate architecture underlying our models, enabling a transfer-learning inspired strategy for modeling germline mutations. In summary, Baymer is an accurate polymorphism probability estimation algorithm that automatically adapts to data sparsity at different sequence context levels, thereby making efficient use of the available data.

Structural underpinnings of mutation rate variations in the human genome.

Single nucleotide mutation rates have critical implications for human evolution and genetic diseases. Importantly, the rates vary substantially across the genome and the principles underlying such variations remain poorly understood. A recent model explained much of this variation by considering higher-order nucleotide interactions in the 7-mer sequence context around mutated nucleotides. This model's success implicates a connection between DNA shape and mutation rates. DNA shape, i.e. structural properties like helical twist and tilt, is known to capture interactions between nucleotides within a local context. Thus, we hypothesized that changes in DNA shape features at and around mutated positions can explain mutation rate variations in the human genome. Indeed, DNA shape-based models of mutation rates showed similar or improved performance over current nucleotide sequence-based models. These models accurately characterized mutation hotspots in the human genome and revealed the shape features whose interactions underlie mutation rate variations. DNA shape also impacts mutation rates within putative functional regions like transcription factor binding sites where we find a strong association between DNA shape and position-specific mutation rates. This work demonstrates the structural underpinnings of nucleotide mutations in the human genome and lays the groundwork for future models of genetic variations to incorporate DNA shape.

Broad-scale variation in human genetic diversity levels is predicted by purifying selection on coding and non-coding elements.

Analyses of genetic variation in many taxa have established that neutral genetic diversity is shaped by natural selection at linked sites. Whether the mode of selection is primarily the fixation of strongly beneficial alleles (selective sweeps) or purifying selection on deleterious mutations (background selection) remains unknown, however. We address this question in humans by fitting a model of the joint effects of selective sweeps and background selection to autosomal polymorphism data from the 1000 Genomes Project. After controlling for variation in mutation rates along the genome, a model of background selection alone explains ~60% of the variance in diversity levels at the megabase scale. Adding the effects of selective sweeps driven by adaptive substitutions to the model does not improve the fit, and when both modes of selection are considered jointly, selective sweeps are estimated to have had little or no effect on linked neutral diversity. The regions under purifying selection are best predicted by phylogenetic conservation, with ~80% of the deleterious mutations affecting neutral diversity occurring in non-exonic regions. Thus, background selection is the dominant mode of linked selection in humans, with marked effects on diversity levels throughout autosomes.

In host evolution of Exophiala dermatitidis in cystic fibrosis lung micro-environment.

Individuals with cystic fibrosis (CF) are susceptible to chronic lung infections that lead to inflammation and irreversible lung damage. While most respiratory infections that occur in CF are caused by bacteria, some are dominated by fungi such as the slow-growing black yeast Exophiala dermatitidis. Here, we analyze isolates of E. dermatitidis cultured from two samples, collected from a single subject 2 years apart. One isolate genome was sequenced using long-read Nanopore technology as an in-population reference to use in comparative single nucleotide polymorphism and insertion-deletion variant analyses of 23 isolates. We then used population genomics and phylo-genomics to compare the isolates to each other as well as the reference genome strain E. dermatitidis NIH/UT8656. Within the CF lung population, three E. dermatitidis clades were detected, each with varying mutation rates. Overall, the isolates were highly similar suggesting that they were recently diverged. All isolates were MAT 1-1, which was consistent with their high relatedness and the absence of evidence for mating or recombination between isolates. Phylogenetic analysis grouped sets of isolates into clades that contained isolates from both early and late time points indicating there are multiple persistent lineages. Functional assessment of variants unique to each clade identified alleles in genes that encode transporters, cytochrome P450 oxidoreductases, iron acquisition, and DNA repair processes. Consistent with the genomic heterogeneity, isolates showed some stable phenotype heterogeneity in melanin production, subtle differences in antifungal minimum inhibitory concentrations, and growth on different substrates. The persistent population heterogeneity identified in lung-derived isolates is an important factor to consider in the study of chronic fungal infections, and the analysis of changes in fungal pathogens over time may provide important insights into the physiology of black yeasts and other slow-growing fungi in vivo.

Structural Underpinnings of Mutation Rate Variations in the Human Genome

Structural Underpinnings of Mutation Rate Variations in the Human Genome

The landscape of nucleotide diversity in Drosophila melanogaster is shaped by mutation rate variation

What shapes the distribution of nucleotide diversity along the genome? Attempts to answer this question have sparked debate about the roles of neutral stochastic processes and natural selection in molecular evolution. However, the mechanisms of evolution do not act in isolation, and integrative models that simultaneously consider the influence of multiple factors on diversity are lacking; without them, confounding factors lurk in the estimates. Here we present a new statistical method that jointly infers the genomic landscapes of genealogies, recombination rates and mutation rates. In doing so, our model captures the effects of genetic drift, linked selection and local mutation rates on patterns of genomic variation. We then formalize a causal model of how these micro-evolutionary mechanisms interact, and cast it as a linear regression to estimate their individual contributions to levels of diversity along the genome. Our analyses reclaim the well-established signature of linked selection in Drosophila melanogaster, but we estimate that the mutation landscape is the major driver of the genome-wide distribution of diversity in this species. Furthermore, our simulation results suggest that in many evolutionary scenarios the mutation landscape will be a crucial factor shaping diversity, depending notably on the genomic window size. We argue that incorporating mutation rate variation into the null model of molecular evolution will lead to more realistic inferences in population genomics.

An unusual suspect: the mutation landscape as a determinant of local variation in nucleotide diversity

What shapes the distribution of nucleotide diversity along the genome? Attempts to answer this question have sparked debate about the roles of neutral stochastic processes and natural selection in molecular evolution. However, the mechanisms of evolution do not act in isolation, and integrative models that simultaneously consider the influence of multiple factors on diversity are lacking; without them, confounding factors lurk in the estimates. Here we present a new statistical method that jointly infers the genomic landscapes of genealogies, recombination rates and mutation rates. In doing so, our model captures the effects of genetic drift, linked selection and local mutation rates on patterns of genomic variation. We then formalise a causal model of how these micro-evolutionary forces interact, and cast it as a linear regression to estimate their individual contributions to levels of diversity along the genome. Our analyses reclaim the well-established signature of linked selection in Drosophila melanogaster, but we estimate that the mutation landscape is the major driver of the genome-wide distribution of diversity in this species. Furthermore, our simulation results suggest that in many evolutionary scenarios the mutation landscape will be a crucial factor shaping diversity, depending notably on the genomic window size. We argue that incorporating mutation rate variation into the null model of molecular evolution will lead to more robust inferences in population genomics.

Ancestral reproductive bias in branching processes.

Consider a branching process whose reproduction law is homogeneous. Sampling a single cell uniformly from the population at a time [Formula: see text] and looking along the sampled cell's ancestral lineage, we find that the reproduction law is heterogeneous-the expected reproductive output of ancestral cells on the lineage from time 0 to time T continuously increases with time. This 'inspection paradox' is due to sampling bias, that cells with a larger number of offspring are more likely to have one of their descendants sampled by virtue of their prolificity. The bias's strength changes with the random population size and/or the sampling time T. Our main result explicitly characterises the evolution of reproduction rates and sizes along the sampled ancestral lineage as a mixture of Poisson processes, which simplifies in special cases. The ancestral bias helps to explain recently observed variation in mutation rates along lineages of the developing human embryo.

Variation in mutation, recombination, and transposition rates in Drosophila melanogaster and Drosophila simulans.

The rates of mutation, recombination, and transposition are core parameters in models of evolution. They impact genetic diversity, responses to ongoing selection, and levels of genetic load. However, even for key evolutionary model species such as Drosophila melanogaster and Drosophila simulans, few estimates of these parameters are available, and we have little idea of how rates vary between individuals, sexes, or populations. Knowledge of this variation is fundamental for parameterizing models of genome evolution. Here, we provide direct estimates of mutation, recombination, and transposition rates and their variation in a West African and a European population of D. melanogaster and a European population of D. simulans Across 89 flies, we observe 58 single-nucleotide mutations, 286 crossovers, and 89 transposable element (TE) insertions. Compared to the European D. melanogaster, we find the West African population has a lower mutation rate (1.67 × 10-9 site-1 gen-1 vs. 4.86 × 10-9 site-1 gen-1) and a lower transposition rate (8.99 × 10-5 copy-1 gen-1 vs. 23.36 × 10-5 copy-1 gen-1), but a higher recombination rate (3.44 cM/Mb vs. 2.06 cM/Mb). The European D. simulans population has a similar mutation rate to European D. melanogaster, but a significantly higher recombination rate and a lower, but not significantly different, transposition rate. Overall, we find paternal-derived mutations are more frequent than maternal ones in both species. Our study quantifies the variation in rates of mutation, recombination, and transposition among different populations and sexes, and our direct estimates of these parameters in D. melanogaster and D. simulans will benefit future studies in population and evolutionary genetics.

Genetic Variation of Puccinia triticina Populations in Iran from 2010 to 2017 as Revealed by SSR and ISSR Markers.

Puccinia triticina is a major wheat pathogen worldwide. Although Iran is within the Fertile Crescent, which is supposed to be the center of origin of both wheat and P. triticina, the knowledge of the genetic variability of local populations of this basidiomycete is limited. We analyzed 12 inter simple sequence repeats (ISSRs) and 18 simple sequence repeats (SSRs) of 175 P. triticina isolates sampled between 2010 and 2017 from wheat and other Poaceae in 14 provinces of Iran. SSRs revealed more polymorphisms than ISSRs, indicating they were more effective in differentiating P. triticina populations. Based on a dissimilarity matrix with a variable mutation rate for SSRs and a Dice coefficient for ISSRs, the isolates were separated into three large groups, each including isolates from diverse geographic origins and hosts. The grouping of SSR genotypes in UPGMA dendrograms was consistent with the grouping inferred from the Bayesian approach. However, isolates with a common origin clustered into separate subgroups within each group. The high proportion of heterozygous alleles suggests that in Iran clonal reproduction prevails over sexual reproduction of the pathogen. A significant correlation was found between SSR and ISSR genotypes and the virulence phenotypes of the isolates, as determined in a previous study.

Chromatin structure influences rate and spectrum of spontaneous mutations in Neurospora crassa.

Although mutation rates have been extensively studied, variation in mutation rates throughout the genome is poorly understood. To understand patterns of genetic variation, it is important to understand how mutation rates vary. Chromatin modifications may be an important factor in determining variation in mutation rates in eukaryotic genomes. To study variation in mutation rates, we performed a mutation accumulation (MA) experiment in the filamentous fungus Neurospora crassa and sequenced the genomes of the 40 MA lines that had been propagated asexually for approximately 1015 [Formula: see text] mitoses. We detected 1322 mutations in total and observed that the mutation rate was higher in regions of low GC, in domains of H3K9 trimethylation, in centromeric regions, and in domains of H3K27 trimethylation. The rate of single-nucleotide mutations in euchromatin was [Formula: see text] In contrast, the mutation rate in H3K9me3 domains was 10-fold higher: 2.43 [Formula: see text] We also observed that the spectrum of single-nucleotide mutations was different between H3K9me3 and euchromatic domains. Our statistical model of mutation rate variation predicted a moderate amount of extant genetic variation, suggesting that the mutation rate is an important factor in determining levels of natural genetic variation. Furthermore, we characterized mutation rates of structural variants, complex mutations, and the effect of local sequence context on the mutation rate. Our study highlights that chromatin modifications are associated with mutation rates, and accurate evolutionary inferences should take variation in mutation rates across the genome into account.

Causes of Mutation Rate Variability in Plant Genomes.

Mutation is the source of all heritable diversity, the essential material of evolution and breeding. While mutation rates are often regarded as constant, variability in mutation rates has been observed at nearly every level-varying across mutation types, genome locations, gene functions, epigenomic contexts, environmental conditions, genotypes, and species. This mutation rate variation arises from differential rates of DNA damage, repair, and transposable element activation and insertion that together produce what is measured by DNA mutation rates. We review historical and recent investigations into the causes and consequences of mutation rate variability in plants by focusing on the mechanisms shaping this variation. Emerging mechanistic models point to the evolvability of mutation rate variation across genomes via mechanisms that target DNA repair, shaping the diversification of plants at phenotypic and genomic scales.

High-Resolution Genomic Profiling of Liver Cancer Links Etiology With Mutation and Epigenetic Signatures

Hepatocellular carcinoma (HCC) is a model of a diverse spectrum of cancers because it is induced by well-known etiologies, mainly hepatitis C virus (HCV) and hepatitis B virus. Here, we aimed to identify HCV-specific mutational signatures and explored the link between the HCV-related regional variation in mutations rates and HCV-induced alterations in genome-wide chromatin organization. To identify an HCV-specific mutational signature in HCC, we performed high-resolution targeted sequencing to detect passenger mutations on 64 HCC samples from 3 etiology groups: hepatitis B virus, HCV, or other. To explore the link between the genomic signature and genome-wide chromatin organization we performed chromatin immunoprecipitation sequencing for the transcriptionally permissive H3K4Me3, H3K9Ac, and suppressive H3K9Me3 modifications after HCV infection. Regional variation in mutation rate analysis showed significant etiology-dependent regional mutation rates in 12 genes: LRP2, KRT84, TMEM132B, DOCK2, DMD, INADL, JAK2, DNAH6, MTMR9, ATM, SLX4, and ARSD. We found an enrichment of C->T transversion mutations in the HCV-associated HCC cases. Furthermore, these cases showed regional variation in mutation rates associated with genomic intervals in which HCV infection dictated epigenetic alterations. This signature may be related to the HCV-induced decreased expression of genes encoding key enzymes in the base excision repair pathway. We identified novel distinct HCV etiology-dependent mutation signatures in HCC associated with HCV-induced alterations in histone modification. This study presents a link between cancer-causing mutagenesis and the increased predisposition to liver cancer in chronic HCV-infected individuals, and unveils novel etiology-specific mechanisms leading to HCC and cancer in general.

CDMAP/CDVIS: context-dependent mutation analysis package and visualization software.

The Context-dependent Mutation Analysis Package and Visualization Software (CDMAP/CDVIS) is an automated, modular toolkit used for the analysis and visualization of context-dependent mutation patterns (site-specific variation in mutation rate from neighboring-nucleotide effects). The CDMAP computes context-dependent mutation rates using a Variant Call File (VCF), Genbank file, and reference genome and can generate high-resolution figures to analyze variation in mutation rate across spatiotemporal scales. This algorithm has been benchmarked against mutation accumulation data but can also be used to calculate context-dependent mutation rates for polymorphism or closely related species as long as the input requirements are met. Output from CDMAP can be integrated into CDVIS, an interactive database for visualizing mutation patterns across multiple taxa simultaneously.

Minimizing transmission error with neural networks and genetic algorithm with quadratic mutation rates

Transmission error quantifies the rotation delay between driving and driven gear, caused by the disturbances of inevitable factors such as elastic deformation, manufacturing error, and mesh misalignment in assembly. A major consequence of high transmission error is the distinguishable whining noise, which indicates wear and efficiency loss. The transmission error (TE) becomes a further serious concern in case of electric vehicles where there is no background noise of internal combustion engine to diminish the gear noise and thus, gears trains become a major, if not only, source of noise in electric vehicles. Hence minimizing TE is a major concern for modern powertrain systems. This research work puts forth a novel approach for finding the optimal mutually independent microgeometry parameters which contribute to minimal transmission error. A neural network learns from the model specific generated data and gives out an approximating function which is then fed to a genetic algorithm which finds the global minima of the output function and therefore the values of the corresponding microgeometry parameters. The neural network and genetic algorithm were programmed from scratch in Python 3.7. The genetic algorithm was further tested with uniform mutation rates and variable mutation rates to find the degree of possibility of premature convergence. The former was found more vulnerable to premature convergence.

A multi-omics-based investigation of the prognostic and immunological impact of necroptosis-related mRNA in patients with cervical squamous carcinoma and adenocarcinoma

Necroptosis is a kind of programmed necrosis mode that plays a double-edged role in tumor progression. However, the role of necroptosis-related Messenger RNA (mRNA) in predicting the prognosis and immune response of cervical squamous carcinoma and adenocarcinoma (CESC) has not been fully studied. Firstly, the incidence of somatic mutation rate and copy number variation for 74 necroptosis-related mRNAs (NRmRNAs) were analyzed. Secondly, CESC patients were divided into four stable clusters based on the consensus clustering results and analyzed for correlations with a series of clinical factors. Subsequently, a total of 291 The Cancer Genome Atlas samples were randomly divided into either training or validation cohorts. A Cox proportional hazard model consisting of three NRmRNAs (CXCL8, CLEC9A, and TAB2) was constructed by univariate, least absolute shrinkage and selection operator and multivariate COX regression analysis to identify the prognosis and immune response. Its performance and stability were further validated in another testing dataset (GSE44001) from Gene Expression Omnibus database. The results of the receiver operating characteristic curve, principal component analysis, t-SNE, and nomogram indicated that the prognostic model we constructed can serve as an independent prognostic factor. The combination of the prognostic model and the classic TNM staging system could improve the performance in predicting the survival of CESC patients. In addition, differentially expressed genes from high and low-risk patients are screened by R software for functional analysis and pathway enrichment analysis. Besides, single-sample gene set enrichment analysis revealed that tumor-killing immune cells were reduced in the high-risk group. Moreover, patients in the low-risk group are more likely to benefit from immune checkpoint inhibitors. The analysis of tumor immune dysfunction and exclusion scores, M6A-related genes, stem cell correlation and Tumor mutational burden data with clinical information has quantified the expression levels of NRmRNAs between the two risk subgroups. According to tumor immune microenvironment scores, Spearman’s correlation analysis, and drug sensitivity, immunotherapy may have a higher response rate and better efficacy in patients of the low-risk subgroup. In conclusion, we have reported the clinical significance of NRmRNAs for the prognosis and immune response in CESC patients for the first time. Screening of accurate and effective prognostic markers is important for designing a multi-combined targeted therapeutic strategy and the development of individualized precision medicine.

Extreme purifying selection against point mutations in the human genome

Large-scale genome sequencing has enabled the measurement of strong purifying selection in protein-coding genes. Here we describe a new method, called ExtRaINSIGHT, for measuring such selection in noncoding as well as coding regions of the human genome. ExtRaINSIGHT estimates the prevalence of “ultraselection” by the fractional depletion of rare single-nucleotide variants, after controlling for variation in mutation rates. Applying ExtRaINSIGHT to 71,702 whole genome sequences from gnomAD v3, we find abundant ultraselection in evolutionarily ancient miRNAs and neuronal protein-coding genes, as well as at splice sites. By contrast, we find much less ultraselection in other noncoding RNAs and transcription factor binding sites, and only modest levels in ultraconserved elements. We estimate that ~0.4–0.7% of the human genome is ultraselected, implying ~ 0.26–0.51 strongly deleterious mutations per generation. Overall, our study sheds new light on the genome-wide distribution of fitness effects by combining deep sequencing data and classical theory from population genetics.

Genome-wide mapping of somatic mutation rates uncovers drivers of cancer

Identification of cancer driver mutations that confer a proliferative advantage is central to understanding cancer; however, searches have often been limited to protein-coding sequences and specific non-coding elements (for example, promoters) because of the challenge of modeling the highly variable somatic mutation rates observed across tumor genomes. Here we present Dig, a method to search for driver elements and mutations anywhere in the genome. We use deep neural networks to map cancer-specific mutation rates genome-wide at kilobase-scale resolution. These estimates are then refined to search for evidence of driver mutations under positive selection throughout the genome by comparing observed to expected mutation counts. We mapped mutation rates for 37 cancer types and applied these maps to identify putative drivers within intronic cryptic splice regions, 5′ untranslated regions and infrequently mutated genes. Our high-resolution mutation rate maps, available for web-based exploration, are a resource to enable driver discovery genome-wide.

The impact of genetic modifiers on variation in germline mutation rates within and among human populations.

Mutation rates and spectra differ among human populations. Here, we examine whether this variation could be explained by evolution at mutation modifiers. To this end, we consider genetic modifier sites at which mutations, "mutator alleles," increase genome-wide mutation rates and model their evolution under purifying selection due to the additional deleterious mutations that they cause, genetic drift, and demographic processes. We solve the model analytically for a constant population size and characterize how evolution at modifier sites impacts variation in mutation rates within and among populations. We then use simulations to study the effects of modifier sites under a plausible demographic model for Africans and Europeans. When comparing populations that evolve independently, weakly selected modifier sites (2Nes≈1), which evolve slowly, contribute the most to variation in mutation rates. In contrast, when populations recently split from a common ancestral population, strongly selected modifier sites (2Nes≫1), which evolve rapidly, contribute the most to variation between them. Moreover, a modest number of modifier sites (e.g. 10 per mutation type in the standard classification into 96 types) subject to moderate to strong selection (2Nes>1)could account for the variation in mutation rates observed among human populations. If such modifier sites indeed underlie differences among populations, they should also cause variation in mutation rates within populations and their effects should be detectable in pedigree studies.

Tumor-infiltrating lymphocytes-based subtypes and genomic characteristics of EBV-associated lymphoepithelioma-like carcinoma.

Tumor-infiltrating lymphocytes (TILs) offer a key for morphological diagnosis of lymphoepithelioma-like carcinoma (LELC) and are the foundation of oncoimmunology. To date, no reports have found a specific risk stratification value of TILs and related it to genomic variation in LELC. Based on the stromal TILs (str-TILs) ratio, we classified 105 Epstein-Barr virus (EBV)-associated LELC cases into two subtypes: patients with ≥60% str-TILs area ratio in tumor were classified as subtype I, and otherwise as subtype II. Subtype I patients had significantly better progression-free survival (PFS) and overall survival (OS). We also explored the genomic characteristics of EBV-associated LELC within different involved organs. We performed whole-exome sequencing for 51 patients with enough tissue and analyzed the genomic characteristics of EBV-associated LELC. Overall, EBV-associated LELCs were characterized by a low somatic mutation rate and copy number variations; the enriched genetic lesions affected RTK-RAS, PI3K, and cell cycle pathways. Moreover, EBV-associated LELCs from different organs were more similar to each other genetically as compared with other traditional carcinomas of the same sites-as evidenced by unsupervised clustering based on the quantitative data from both mutation signature and chromosomal aneuploidies. Notably, EBV-associated LELC patients with oncogenic driver alterations showed a worse prognosis compared with patients without such alterations. © 2022 The Pathological Society of Great Britain and Ireland.