Distribution Of Fitness Effects Research Articles

Distribution of fitness effects of mutations obtained from a simple genetic regulatory network model

Beneficial and deleterious mutations change an organism’s fitness but the distribution of these mutational effects on fitness are unknown. Several experimental, theoretical, and computational studies have explored this question but are limited because of experimental restrictions, or disconnect with physiology. Here we attempt to characterize the distribution of fitness effects (DFE) due to mutations in a cellular regulatory motif. We use a simple mathematical model to describe the dynamics of gene expression in the lactose utilization network, and use a cost-benefit framework to link the model output to fitness. We simulate mutations by changing model parameters and computing altered fitness to obtain the DFE. We find beneficial mutations distributed exponentially, but distribution of deleterious mutations seems far more complex. In addition, we find neither the starting fitness, nor the exact location on the fitness landscape, affecting these distributions qualitatively. Lastly, we quantify epistasis in our model and find that the distribution of epistatic effects remains qualitatively conserved across different locations on the fitness landscape. Overall, we present a first attempt at exploring the specific statistical features of the fitness landscape associated with a system, by using the specific mathematical model associated with it.

Estimation of allele-specific fitness effects across human protein-coding sequences and implications for disease.

A central challenge in human genomics is to understand the cellular, evolutionary, and clinical significance of genetic variants. Here, we introduce a unified population-genetic and machine-learning model, called Linear Allele-Specific Selection InferencE (LASSIE), for estimating the fitness effects of all observed and potential single-nucleotide variants, based on polymorphism data and predictive genomic features. We applied LASSIE to 51 high-coverage genome sequences annotated with 33 genomic features and constructed a map of allele-specific selection coefficients across all protein-coding sequences in the human genome. This map is generally consistent with previous inferences of the bulk distribution of fitness effects but reveals pervasive weak negative selection against synonymous mutations. In addition, the estimated selection coefficients are highly predictive of inherited pathogenic variants and cancer driver mutations, outperforming state-of-the-art variant prioritization methods. By contrasting our estimated model with ultrahigh coverage ExAC exome-sequencing data, we identified 1118 genes under unusually strong negative selection, which tend to be exclusively expressed in the central nervous system or associated with autism spectrum disorder, as well as 773 genes under unusually weak selection, which tend to be associated with metabolism. This combination of classical population genetic theory with modern machine-learning and large-scale genomic data is a powerful paradigm for the study of both human evolution and disease.

Inferring the distribution of fitness effects of spontaneous mutations in Chlamydomonas reinhardtii.

Spontaneous mutations are the source of new genetic variation and are thus central to the evolutionary process. In molecular evolution and quantitative genetics, the nature of genetic variation depends critically on the distribution of effects of mutations on fitness and other quantitative traits. Spontaneous mutation accumulation (MA) experiments have been the principal approach for investigating the overall rate of occurrence and cumulative effect of mutations but have not allowed the phenotypic effects of individual mutations to be studied directly. Here, we crossed MA lines of the green alga Chlamydomonas reinhardtii with its unmutated ancestral strain to create haploid recombinant lines, each carrying an average of 50% of the accumulated mutations in a large number of combinations. With the aid of the genome sequences of the MA lines, we inferred the genotypes of the mutations, assayed their growth rate as a measure of fitness, and inferred the distribution of fitness effects (DFE) using a Bayesian mixture model. We infer that the DFE is highly leptokurtic (L-shaped). Of mutations with absolute fitness effects exceeding 1%, about one-sixth increase fitness in the laboratory environment. The inferred distribution of effects for deleterious mutations is consistent with a strong role for nearly neutral evolution. Specifically, such a distribution predicts that nucleotide variation and genetic variation for quantitative traits will be insensitive to change in the effective population size.

The genomic determinants of adaptive evolution in a fungal pathogen.

Unravelling the strength, frequency, and distribution of selective variants along the genome as well as the underlying factors shaping this distribution are fundamental goals of evolutionary biology. Antagonistic host‐pathogen coevolution is thought to be a major driver of genome evolution between interacting species. While rapid evolution of pathogens has been documented in several model organisms, the genetic mechanisms of their adaptation are still poorly understood and debated, particularly the role of sexual reproduction. Here, we apply a population genomic approach to infer genome‐wide patterns of selection among 13 isolates of Zymoseptoria tritici, a fungal pathogen characterized by extremely high genetic diversity, gene density, and recombination rates. We report that the genome of Z. tritici undergoes a high rate of adaptive substitutions, with 44% of nonsynonymous substitutions being adaptive on average. This fraction reaches 68% in so‐called effector genes encoding determinants of pathogenicity, and the distribution of fitness effects differs in this class of genes as they undergo adaptive mutations with stronger positive fitness effects, but also more slightly deleterious mutations. Besides the globally high rate of adaptive substitutions, we report a negative relationship between pN/pS and the fine‐scale recombination rate and a strong positive correlation between the rate of adaptive nonsynonymous substitutions (ωa) and recombination rate. This result suggests a pervasive role of both background selection and Hill‐Robertson interference even in a species with an exceptionally high recombination rate (60 cM/Mb on average). While transposable elements (TEs) have been suggested to contribute to adaptation by creating compartments of fast‐evolving genomic regions, we do not find a significant effect of TEs on the rate of adaptive mutations. Overall our study suggests that sexual recombination is a significant driver of genome evolution, even in rapidly evolving organisms subject to recurrent mutations with large positive effects.

The Impact of Protein Architecture on Adaptive Evolution.

Adaptive mutations play an important role in molecular evolution. However, the frequency and nature of these mutations at the intramolecular level are poorly understood. To address this, we analyzed the impact of protein architecture on the rate of adaptive substitutions, aiming to understand how protein biophysics influences fitness and adaptation. Using Drosophila melanogaster and Arabidopsis thaliana population genomics data, we fitted models of distribution of fitness effects and estimated the rate of adaptive amino-acid substitutions both at the protein and amino-acid residue level. We performed a comprehensive analysis covering genome, gene, and protein structure, by exploring a multitude of factors with a plausible impact on the rate of adaptive evolution, such as intron number, protein length, secondary structure, relative solvent accessibility, intrinsic protein disorder, chaperone affinity, gene expression, protein function, and protein–protein interactions. We found that the relative solvent accessibility is a major determinant of adaptive evolution, with most adaptive mutations occurring at the surface of proteins. Moreover, we observe that the rate of adaptive substitutions differs between protein functional classes, with genes encoding for protein biosynthesis and degradation signaling exhibiting the fastest rates of protein adaptation. Overall, our results suggest that adaptive evolution in proteins is mainly driven by intermolecular interactions, with host–pathogen coevolution likely playing a major role.

Fitness Effects of Single Amino Acid Insertions and Deletions in TEM-1 β-Lactamase

Short insertions and deletions (InDels) are a common type of mutation found in nature and a useful source of variation in protein engineering. InDel events have important consequences in protein evolution, often opening new pathways for adaptation. However, much less is known about the effects of InDels compared to point mutations and amino acid substitutions. In particular, deep mutagenesis studies on the distribution of fitness effects of mutations have focused almost exclusively on amino acid substitutions. Here, we present a near-comprehensive analysis of the fitness effects of single amino acid InDels in TEM-1 β-lactamase. While we found InDels to be largely deleterious, partially overlapping deletion-tolerant and insertion-tolerant regions were observed throughout the protein, especially in unstructured regions and at the end of helices. The signal sequence of TEM-1 tolerated InDels more than the mature protein. Most regions of the protein tolerated insertions more than deletions, but a few regions tolerated deletions more than insertions. We examined the relationship between InDel tolerance and a variety of measures to help understand its origin. These measures included evolutionary variation in β-lactamases, secondary structure identity, tolerance to amino acid substitutions, solvent accessibility, and side-chain weighted contact number. We found secondary structure, weighted contact number, and evolutionary variation in class A beta-lactamases to be the somewhat predictive of InDel fitness effects.

The Impact of Natural Selection on Short Insertion and Deletion Variation in the Great Tit Genome.

Insertions and deletions (INDELs) remain understudied, despite being the most common form of genetic variation after single nucleotide polymorphisms. This stems partly from the challenge of correctly identifying the ancestral state of an INDEL and thus identifying it as an insertion or a deletion. Erroneously assigned ancestral states can skew the site frequency spectrum, leading to artificial signals of selection. Consequently, the selective pressures acting on INDELs are, at present, poorly resolved. To tackle this issue, we have recently published a maximum likelihood approach to estimate the mutation rate and the distribution of fitness effects for INDELs. Our approach estimates and controls for the rate of ancestral state misidentification, overcoming issues plaguing previous INDEL studies. Here, we apply the method to INDEL polymorphism data from ten high coverage (∼44×) European great tit (Parus major) genomes. We demonstrate that coding INDELs are under strong purifying selection with a small proportion making it into the population (∼4%). However, among fixed coding INDELs, 71% of insertions and 86% of deletions are fixed by positive selection. In noncoding regions, we estimate ∼80% of insertions and ∼52% of deletions are effectively neutral, the remainder show signatures of purifying selection. Additionally, we see evidence of linked selection reducing INDEL diversity below background levels, both in proximity to exons and in areas of low recombination.

Adaptive Landscapes in the Age of Synthetic Biology.

For nearly a century adaptive landscapes have provided overviews of the evolutionary process and yet they remain metaphors. We redefine adaptive landscapes in terms of biological processes rather than descriptive phenomenology. We focus on the underlying mechanisms that generate emergent properties such as epistasis, dominance, trade-offs and adaptive peaks. We illustrate the utility of landscapes in predicting the course of adaptation and the distribution of fitness effects. We abandon aged arguments concerning landscape ruggedness in favor of empirically determining landscape architecture. In so doing, we transform the landscape metaphor into a scientific framework within which causal hypotheses can be tested.

PolyDFEv2.0: testing for invariance of the distribution of fitness effects within and across species.

Distribution of fitness effects (DFE) of mutations can be inferred from site frequency spectrum (SFS) data. There is mounting interest to determine whether distinct genomic regions and/or species share a common DFE, or whether evidence exists for differences among them. polyDFEv2.0 fits multiple SFS datasets at once and provides likelihood ratio tests for DFE invariance across datasets. Simulations show that testing for DFE invariance across genomic regions within a species requires models accounting for distinct sources of heterogeneity (chance and genuine difference in DFE) underlying differences in SFS data in these regions. Not accounting for this will result in the spurious detection of DFE differences. polyDFEv2.0 is implemented in C and is accompanied by a series of R functions that facilitate post-processing of the output. It is available as source code and compiled binaries under a GNU General Public License v3.0 from https://github.com/paula-tataru/polyDFE. Supplementary data are available at Bioinformatics online.

Genetic rescue and the maintenance of native ancestry

Genetic rescue (GR) is an important tool in conservation biology for the management of highly inbred populations. However, GR comes at a potential cost: the reduction in native genomic content. The degree to which adaptive evolution for new immigrant alleles lead to complete or partial replacement of the native genome, depends on recombination rates, genome size, effective population sizes, and distributions of fitness effects. As one of the most well-studied examples of an inbred species that exchanged genetic material with a less inbred species, we use Neanderthals and humans as an example. Using simulations, we show that for parameters realistic for these species, the increase in fitness caused by GR is mirrored by an almost proportional reduction in the fraction of the native genome preserved. The main reason is that the effect of selection will be strongest in the first few generations during which time there is only little break-up, by recombination, of the immigrant chromosomes. We also show that in the presence of a strong and short-lived bottleneck with recessive mutations, GR leads to a stronger increase in fitness than in the presence of additive effects or a more prolonged bottleneck.

Mutational load causes stochastic evolutionary outcomes in acute RNA viral infection.

Mutational load is known to be of importance for the evolution of RNA viruses, the combination of a high mutation rate and large population size leading to an accumulation of deleterious mutations. However, while the effects of mutational load on global viral populations have been considered, its quantitative effects at the within-host scale of infection are less well understood. We here show that even on the rapid timescale of acute disease, mutational load has an effect on within-host viral adaptation, reducing the effective selection acting upon beneficial variants by ∼10 per cent. Furthermore, mutational load induces considerable stochasticity in the pattern of evolution, causing a more than five-fold uncertainty in the effective fitness of a transmitted beneficial variant. Our work aims to bridge the gap between classic models from population genetic theory and the biology of viral infection. In an advance on some previous models of mutational load, we replace the assumption of a constant variant fitness cost with an experimentally-derived distribution of fitness effects. Expanding previous frameworks for evolutionary simulation, we introduce the Wright-Fisher model with continuous mutation, which describes a continuum of possible modes of replication within a cell. Our results advance our understanding of adaptation in the context of strong selection and a high mutation rate. Despite viral populations having large absolute sizes, critical events in viral adaptation, including antigenic drift and the onset of drug resistance, arise through stochastic evolutionary processes.

Molecular Basis of Complex Heritability in Natural Genotype-to-Phenotype Relationships

Although the statistical complexity of heredity has long been evident, its molecular origins remain elusive. To investigate, we charted a complete genotype-to-phenotype map for 90 complex traits in a large population of wild diploid yeast. Networks defined by these 18,007 loci differed substantially from those determined by gene deletions or protein interactions. Missense, synonymous, and regulatory variants contributed to phenotype. Synonymous variants altered codon adaptation index early in open reading frames. Regulatory variants occurred in open chromatin; their disruption drove hybrid vigor. Pleiotropic variants often altered disordered sequences within signaling hubs. Their effects correlated across environments – even when antagonistic – suggesting that large fitness gains bring concomitant costs. Finally, we estimated distributions of fitness effects for natural variants, revealing that traits appearing ‘omnigenic’ in less powered studies have finite genetic determinants. Integrating these principles into genome reading and writing will be crucial as it becomes routine in research, industry, and medicine.

Nearly Neutral Evolution across the Drosophila melanogaster Genome.

Under the nearly neutral theory of molecular evolution, the proportion of effectively neutral mutations is expected to depend upon the effective population size (Ne). Here, we investigate whether this is the case across the genome of Drosophila melanogaster using polymorphism data from North American and African lines. We show that the ratio of the number of nonsynonymous and synonymous polymorphisms is negatively correlated to the number of synonymous polymorphisms, even when the nonindependence is accounted for. The relationship is such that the proportion of effectively neutral nonsynonymous mutations increases by ∼45% as Ne is halved. However, we also show that this relationship is steeper than expected from an independent estimate of the distribution of fitness effects from the site frequency spectrum. We investigate a number of potential explanations for this and show, using simulation, that this is consistent with a model of genetic hitchhiking: Genetic hitchhiking depresses diversity at neutral and weakly selected sites, but has little effect on the diversity of strongly selected sites.

The fitness landscape of the codon space across environments.

Fitness landscapes map the relationship between genotypes and fitness. However, most fitness landscape studies ignore the genetic architecture imposed by the codon table and thereby neglect the potential role of synonymous mutations. To quantify the fitness effects of synonymous mutations and their potential impact on adaptation on a fitness landscape, we use a new software based on Bayesian Monte Carlo Markov Chain methods and re-estimate selection coefficients of all possible codon mutations across 9 amino acid positions in Saccharomyces cerevisiae Hsp90 across 6 environments. We quantify the distribution of fitness effects of synonymous mutations and show that it is dominated by many mutations of small or no effect and few mutations of larger effect. We then compare the shape of the codon fitness landscape across amino acid positions and environments, and quantify how the consideration of synonymous fitness effects changes the evolutionary dynamics on these fitness landscapes. Together these results highlight a possible role of synonymous mutations in adaptation and indicate the potential mis-inference when they are neglected in fitness landscape studies.

Biased Inference of Selection Due to GC-Biased Gene Conversion and the Rate of Protein Evolution in Flycatchers When Accounting for It.

The rate of recombination impacts on rates of protein evolution for at least two reasons: it affects the efficacy of selection due to linkage and influences sequence evolution through the process of GC-biased gene conversion (gBGC). We studied how recombination, via gBGC, affects inferences of selection in gene sequences using comparative genomic and population genomic data from the collared flycatcher (Ficedula albicollis). We separately analyzed different mutation categories (“strong”-to-“weak,” “weak-to-strong,” and GC-conservative changes) and found that gBGC impacts on the distribution of fitness effects of new mutations, and leads to that the rate of adaptive evolution and the proportion of adaptive mutations among nonsynonymous substitutions are underestimated by 22–33%. It also biases inferences of demographic history based on the site frequency spectrum. In light of this impact, we suggest that inferences of selection (and demography) in lineages with pronounced gBGC should be based on GC-conservative changes only. Doing so, we estimate that 10% of nonsynonymous mutations are effectively neutral and that 27% of nonsynonymous substitutions have been fixed by positive selection in the flycatcher lineage. We also find that gene expression level, sex-bias in expression, and the number of protein–protein interactions, but not Hill–Robertson interference (HRI), are strong determinants of selective constraint and rate of adaptation of collared flycatcher genes. This study therefore illustrates the importance of disentangling the effects of different evolutionary forces and genetic factors in interpretation of sequence data, and from that infer the role of natural selection in DNA sequence evolution.

Gene expression drives the evolution of dominance

Dominance is a fundamental concept in molecular genetics and has implications for understanding patterns of genetic variation, evolution, and complex traits. However, despite its importance, the degree of dominance in natural populations is poorly quantified. Here, we leverage multiple mating systems in natural populations of Arabidopsis to co-estimate the distribution of fitness effects and dominance coefficients of new amino acid changing mutations. We find that more deleterious mutations are more likely to be recessive than less deleterious mutations. Further, this pattern holds across gene categories, but varies with the connectivity and expression patterns of genes. Our work argues that dominance arises as a consequence of the functional importance of genes and their optimal expression levels.

Conserved Noncoding Elements Influence the Transposable Element Landscape in Drosophila.

Highly conserved noncoding elements (CNEs) constitute a significant proportion of the genomes of multicellular eukaryotes. The function of most CNEs remains elusive, but growing evidence indicates they are under some form of purifying selection. Noncoding regions in many species also harbor large numbers of transposable element (TE) insertions, which are typically lineage specific and depleted in exons because of their deleterious effects on gene function or expression. However, it is currently unknown whether the landscape of TE insertions in noncoding regions is random or influenced by purifying selection on CNEs. Here, we combine comparative and population genomic data in Drosophila melanogaster to show that the abundance of TE insertions in intronic and intergenic CNEs is reduced relative to random expectation, supporting the idea that selective constraints on CNEs eliminate a proportion of TE insertions in noncoding regions. However, we find no evidence for differences in the allele frequency spectra for polymorphic TE insertions in CNEs versus those in unconstrained spacer regions, suggesting that the distribution of fitness effects acting on observable TE insertions is similar across different functional compartments in noncoding DNA. Our results provide evidence that selective constraints on CNEs contribute to shaping the landscape of TE insertion in eukaryotic genomes, and provide further evidence that CNEs are indeed functionally constrained and not simply mutational cold spots.

Mutation rates and effects in single cells

Single-Cell Analysis Understanding the dynamics of mutations and the distribution of fitness effects is critical for most evolutionary models. Robert et al. used a single-cell technology to visualize the accumulation of new mutations. The method identifies DNA sequences with mispaired bases and

Mutation dynamics and fitness effects followed in single cells.

Mutations have been investigated for more than a century but remain difficult to observe directly in single cells, which limits the characterization of their dynamics and fitness effects. By combining microfluidics, time-lapse imaging, and a fluorescent tag of the mismatch repair system in Escherichia coli, we visualized the emergence of mutations in single cells, revealing Poissonian dynamics. Concomitantly, we tracked the growth and life span of single cells, accumulating ~20,000 mutations genome-wide over hundreds of generations. This analysis revealed that 1% of mutations were lethal; nonlethal mutations displayed a heavy-tailed distribution of fitness effects and were dominated by quasi-neutral mutations with an average cost of 0.3%. Our approach has enabled the investigation of single-cell individuality in mutation rate, mutation fitness costs, and mutation interactions.

Experimental Determination and Prediction of the Fitness Effects of Random Point Mutations in the Biosynthetic Enzyme HisA.

The distribution of fitness effects of mutations is a factor of fundamental importance in evolutionary biology. We determined the distribution of fitness effects of 510 mutants that each carried between 1 and 10 mutations (synonymous and nonsynonymous) in the hisA gene, encoding an essential enzyme in the l-histidine biosynthesis pathway of Salmonella enterica. For the full set of mutants, the distribution was bimodal with many apparently neutral mutations and many lethal mutations. For a subset of 81 single, nonsynonymous mutants most mutations appeared neutral at high expression levels, whereas at low expression levels only a few mutations were neutral. Furthermore, we examined how the magnitude of the observed fitness effects was correlated to several measures of biophysical properties and phylogenetic conservation.We conclude that for HisA: (i) The effect of mutations can be masked by high expression levels, such that mutations that are deleterious to the function of the protein can still be neutral with regard to organism fitness if the protein is expressed at a sufficiently high level; (ii) the shape of the fitness distribution is dependent on the extent to which the protein is rate-limiting for growth; (iii) negative epistatic interactions, on an average, amplified the combined effect of nonsynonymous mutations; and (iv) no single sequence-based predictor could confidently predict the fitness effects of mutations in HisA, but a combination of multiple predictors could predict the effect with a SD of 0.04 resulting in 80% of the mutations predicted within 12% of their observed selection coefficients.