Interpreting dN/dS under different selective regimes in cancer evolution

The standard relationship between the dN/dS statistic and the selection coefficient is contingent upon the computation of the rate of fixation of non-synonymous and synonymous mutations among divergent lineages (substitutions). In cancer genomics, however, dN/dS is typically calculated by including mutations that are still segregating in the cell population. The interpretation of dN/dS within sexual populations has been shown to be problematic. Here we used a simple model of somatic evolution to study the relationship between dN/dS and the selection coefficient in the presence of deleterious, neutral, and beneficial mutations in cancer. We found that dN/dS can be used to distinguish cancer genes under positive or negative selection, but it is not always informative about the magnitude of the selection coefficient. In particular, under the asexual scenario simulated, dN/dS is insensitive to negative selection strength. Furthermore, the relationship between dN/dS and the positive selection coefficient depends on the mutation detection threshold, and, in particular scenarios, it can become non-linear. Our results warn about the necessary caution when interpreting the results drawn from dN/dS estimates in cancer.

Evidence of positive and negative selection associated with DNA methylation

Signatures of negative selection are pervasive amongst complex traits and diseases. However, it is unclear whether such signatures exist for DNA methylation (DNAm) that has been proposed to have a functional role in disease. We estimate polygenicity, SNP-based heritability and model the joint distribution of effect size and minor allele frequency (MAF) to estimate a selection coefficient (S) for 2000 heritable DNAm sites in 1774 individuals from the Avon Longitudinal Study of Parents and Children. Additionally, we estimate S for meta stable epi alleles and DNAm sites associated with aging and mortality, birthweight and body mass index. Quantification of MAF-dependent genetic architectures estimated from genotype and DNAm reveal evidence of positive (S>0) and negative selection (S<0) and confirm previous evidence of negative selection for birthweight. Evidence of both negative and positive selection highlights the role of DNAm as an intermediary in multiple biological pathways with competing function.

The impact of Fishers Reproductive Compensation on raising equilibrium frequencies of semi-dominant, non-lethal mutations under mutation/selection balance.

Fishers reproductive compensation (fRC) occurs when a species demography means the death of an individual allows increased survival of his/her relatives, usually assumed to be full sibs. This likely occurs in many species, including humans. Several important recessive human genetic diseases cause early foetal/infant death allowing fRC to act on these mutations. The impact of fRC on these genetic conditions has been calculated and shown to be substantial as quantified by w, the fold increase in equilibrium frequencies of the mutation under fRC compared to its absence i.e. w=1.22 and w =1.33 for autosomal and sex-linked loci, respectively. However, the impact of fRC on the frequency of the much large class of semi-dominant, non-lethal mutations is unknown. This is calculated here by a mixture of simulation and algebra and shown that w is approximately 2-h*s and 2-0.19s-0.85h*s for autosomal and sex-linked loci respectively where h is dominance (varied between 0.05 and 0.95) and s is selection coefficient (varied between 0.05 and 0.9). These results show that the actions of fRC can almost double equilibrium frequency of mutations with low values of h and/or s. The dynamics of fRC acting on this type of mutation are also identified and discussed.

Time to fixation in changing environments

Although many experimental and theoretical studies on natural selection have been carried out in a constant environment, as natural environments typically vary in time, it is important to ask if and how the results of these investigations are affected by a changing environment. Here, we study the properties of the conditional fixation time defined as the time to fixation of a new mutant that is destined to fix in a finite, randomly mating diploid population with intermediate dominance that is evolving in a periodically changing environment. It is known that in a static environment, the conditional mean fixation time of a co-dominant beneficial mutant is equal to that of a deleterious mutant with the same magnitude of selection coefficient. We find that this symmetry is not preserved, even when the environment is changing slowly. More generally, we find that the conditional mean fixation time of an initially beneficial mutant in a slowly changing environment depends weakly on the dominance coefficient and remains close to the corresponding result in the static environment. However, for an initially deleterious mutant under moderate and slowly varying selection, the fixation time differs substantially from that in a constant environment when the mutant is recessive. As fixation times are intimately related to the levels and patterns of genetic diversity, our results suggest that for beneficial sweeps, these quantities are only mildly affected by temporal variation in environment. In contrast, environmental change is likely to impact the patterns due to recessive deleterious sweeps strongly.

Extreme purifying selection against point mutations in the human genome

Genome sequencing of tens of thousands of human individuals has recently enabled the measurement of large selective effects for mutations to protein-coding genes. Here we describe a new method, called ExtRaINSIGHT, for measuring similar selective effects at individual sites in noncoding as well as in coding regions of the human genome. ExtRaINSIGHT estimates the prevalance of strong purifying selection, or "ultraselection" (λs), as the fractional depletion of rare single-nucleotide variants (minor allele frequency <0.1%) in a target set of genomic sites relative to matched sites that are putatively neutrally evolving, in a manner that controls for local variation and neighbor-dependence in mutation rate. We show using simulations that, above an appropriate threshold, λs is closely related to the average site-specific selection coefficient against heterozygous point mutations, as predicted at mutation-selection balance. Applying ExtRaINSIGHT to 71,702 whole genome sequences from gnomAD v3, we find particularly strong evidence of ultraselection in evolutionarily ancient miRNAs and neuronal protein-coding genes, as well as at splice sites. Moreover, our estimated selection coefficient against heterozygous amino-acid replacements across the genome (at 1.4%) is substantially larger than previous estimates based on smaller sample sizes. By contrast, we find weak evidence of ultraselection in other noncoding RNAs and transcription factor binding sites, and only modest evidence in ultraconserved elements and human accelerated regions. We estimate that ~0.3-0.5% of the human genome is ultraselected, with one third to one half of ultraselected sites falling in coding regions. These estimates suggest ~0.3-0.4 lethal or nearly lethal de novo mutations per potential human zygote, together with ~2 de novo mutations that are more weakly deleterious. Overall, our study sheds new light on the genome-wide distribution of fitness effects for new point mutations by combining deep new sequencing data sets and classical theory from population genetics.

Implementation of Fraudulent Sellers Detection System of Online Marketplaces using Machine Learning Techniques

The E-commerce proportion in global retail expenditure has been steadily increasing over the years showing an obvious shift from brick and mortar to retail clicks. To analyze the exact problem of building an interactive models for the identification of auction fraud in the entry of data into ecommerce. This is why the most popular site's business develops with retailers and other auction customers. Where viral customers purchase products from online trading, customers may worry about fraudulent actions to get unlawful benefits from honest parties. Proactive modesty systems for detecting fraud are thus a necessary practice to prevent such illegal activities. The shopping product is built according to the customer's requirements and is safer online and resting, and the rules and regulations that are necessary to follow no longer seem to be the best of workable selection, coefficient limits that facilitate the shopping product and make it easier for the user model to compete on each platform so that it can experiment.

The Antimicrobial Resistance Crisis: An Inadvertent, Unfortunate but Nevertheless Informative Experiment in Evolutionary Biology

The global rise of antimicrobial resistance (AMR) phenotypes is an exemplar for rapid evolutionary response. Resistance arises as a consequence of humanity’s widespread and largely indiscriminate use of antimicrobial compounds. However, some features of this crisis remain perplexing. The remarkably widespread and rapid rise of diverse, novel and effective resistance phenotypes is in stark contrast to the apparent paucity of antimicrobial producers in the global microbiota. From the viewpoint of evolutionary theory, it should be possible to use selection coefficients to examine these phenomena. In this work we introduce an elaboration on the selection coefficient s termed selective efficiency, considering the genetic, metabolic, ecological and evolutionary impacts that accompany selective phenotypes. We then demonstrate the utility of the selective efficiency concept using AMR and antimicrobial production phenotypes as ‘worked examples’ of the concept. In accomplishing this objective, we also put forward cogent hypotheses to explain currently puzzling aspects of the AMR crisis. Finally, we extend the selective efficiency concept into a consideration of the ongoing management of the AMR crisis.

Predicting population genetic change in an autocorrelated random environment: Insights from a large automated experiment

Most natural environments exhibit a substantial component of random variation, with a degree of temporal autocorrelation that defines the color of environmental noise. Such environmental fluctuations cause random fluctuations in natural selection, affecting the predictability of evolution. But despite long-standing theoretical interest in population genetics in stochastic environments, there is a dearth of empirical estimation of underlying parameters of this theory. More importantly, it is still an open question whether evolution in fluctuating environments can be predicted indirectly using simpler measures, which combine environmental time series with population estimates in constant environments. Here we address these questions by using an automated experimental evolution approach. We used a liquid-handling robot to expose over a hundred lines of the micro-alga Dunaliella salina to randomly fluctuating salinity over a continuous range, with controlled mean, variance, and autocorrelation. We then tracked the frequencies of two competing strains through amplicon sequencing of nuclear and choloroplastic barcode sequences. We show that the magnitude of environmental fluctuations (determined by their variance), but also their predictability (determined by their autocorrelation), had large impacts on the average selection coefficient. The variance in frequency change, which quantifies randomness in population genetics, was substantially higher in a fluctuating environment. The reaction norm of selection coefficients against constant salinity yielded accurate predictions for the mean selection coefficient in a fluctuating environment. This selection reaction norm was in turn well predicted by environmental tolerance curves, with population growth rate against salinity. However, both the selection reaction norm and tolerance curves underestimated the variance in selection caused by random environmental fluctuations. Overall, our results provide exceptional insights into the prospects for understanding and predicting genetic evolution in randomly fluctuating environments.

Adaptation and Natural Selection

Here non-random shifts in allele frequencies over time are introduced, as well as how to incorporate varying levels of selection into a model of a single population through time. This chapter highlights the difference between weak and strong selection, the dynamics of single allele versus genotype-level selection, and how selection strength and population size affect allele frequency distributions over time. Finally the inference of the selection coefficient from allele frequency data is discussed, alongside the concepts of overdominance and underdominance.

Evolution in the weak-mutation limit: Stasis periods punctuated by fast transitions between saddle points on the fitness landscape

A mathematical analysis of the evolution of a large population under the weak-mutation limit shows that such a population would spend most of the time in stasis in the vicinity of saddle points on the fitness landscape. The periods of stasis are punctuated by fast transitions, in lnNe/s time (Ne, effective population size; s, selection coefficient of a mutation), when a new beneficial mutation is fixed in the evolving population, which accordingly moves to a different saddle, or on much rarer occasions from a saddle to a local peak. Phenomenologically, this mode of evolution of a large population resembles punctuated equilibrium (PE) whereby phenotypic changes occur in rapid bursts that are separated by much longer intervals of stasis during which mutations accumulate but the phenotype does not change substantially. Theoretically, PE has been linked to self-organized criticality (SOC), a model in which the size of “avalanches” in an evolving system is power-law-distributed, resulting in increasing rarity of major events. Here we show, however, that a PE-like evolutionary regime is the default for a very simple model of an evolving population that does not rely on SOC or any other special conditions.