Compensatory Evolution and the Origins of Innovations

Cryptic genetic sequences have attenuated effects on phenotypes. In the classic view, relaxed selection allows cryptic genetic diversity to build up across individuals in a population, providing alleles that may later contribute to adaptation when co-opted—e.g., following a mutation increasing expression from a low, attenuated baseline. This view is described, for example, by the metaphor of the spread of a population across a neutral network in genotype space. As an alternative view, consider the fact that most phenotypic traits are affected by multiple sequences, including cryptic ones. Even in a strictly clonal population, the co-option of cryptic sequences at different loci may have different phenotypic effects and offer the population multiple adaptive possibilities. Here, we model the evolution of quantitative phenotypic characters encoded by cryptic sequences and compare the relative contributions of genetic diversity and of variation across sites to the phenotypic potential of a population. We show that most of the phenotypic variation accessible through co-option would exist even in populations with no polymorphism. This is made possible by a history of compensatory evolution, whereby the phenotypic effect of a cryptic mutation at one site was balanced by mutations elsewhere in the genome, leading to a diversity of cryptic effect sizes across sites rather than across individuals. Cryptic sequences might accelerate adaptation and facilitate large phenotypic changes even in the absence of genetic diversity, as traditionally defined in terms of alternative alleles.     

CanDrA: Cancer-Specific Driver Missense Mutation Annotation with Optimized Features

Driver mutations are somatic mutations that provide growth advantage to tumor cells, while passenger mutations are those not functionally related to oncogenesis. Distinguishing drivers from passengers is challenging because drivers occur much less frequently than passengers, they tend to have low prevalence, their functions are multifactorial and not intuitively obvious. Missense mutations are excellent candidates as drivers, as they occur more frequently and are potentially easier to identify than other types of mutations. Although several methods have been developed for predicting the functional impact of missense mutations, only a few have been specifically designed for identifying driver mutations. As more mutations are being discovered, more accurate predictive models can be developed using machine learning approaches that systematically characterize the commonality and peculiarity of missense mutations under the background of specific cancer types. Here, we present a cancer driver annotation (CanDrA) tool that predicts missense driver mutations based on a set of 95 structural and evolutionary features computed by over 10 functional prediction algorithms such as CHASM, SIFT, and MutationAssessor. Through feature optimization and supervised training, CanDrA outperforms existing tools in analyzing the glioblastoma multiforme and ovarian carcinoma data sets in The Cancer Genome Atlas and the Cancer Cell Line Encyclopedia project.     

The effects of Hill-Robertson interference between weakly selected mutations on patterns of molecular evolution and variation

Associations between selected alleles and the genetic backgrounds on which they are found can reduce the efficacy of selection. We consider the extent to which such interference, known as the Hill-Robertson effect, acting between weakly selected alleles, can restrict molecular adaptation and affect patterns of polymorphism and divergence. In particular, we focus on synonymous-site mutations, considering the fate of novel variants in a two-locus model and the equilibrium effects of interference with multiple loci and reversible mutation. We find that weak selection Hill-Robertson (wsHR) interference can considerably reduce adaptation, e.g., codon bias, and, to a lesser extent, levels of polymorphism, particularly in regions of low recombination. Interference causes the frequency distribution of segregating sites to resemble that expected from more weakly selected mutations and also generates specific patterns of linkage disequilibrium. While the selection coefficients involved are small, the fitness consequences of wsHR interference across the genome can be considerable. We suggest that wsHR interference is an important force in the evolution of nonrecombining genomes and may explain the unexpected constancy of codon bias across species of very different census population sizes, as well as several unusual features of codon usage in Drosophila.     

Identification of Constrained Cancer Driver Genes Based on Mutation Timing

Cancer drivers are genomic alterations that provide cells containing them with a selective advantage over their local competitors, whereas neutral passengers do not change the somatic fitness of cells. Cancer-driving mutations are usually discriminated from passenger mutations by their higher degree of recurrence in tumor samples. However, there is increasing evidence that many additional driver mutations may exist that occur at very low frequencies among tumors. This observation has prompted alternative methods for driver detection, including finding groups of mutually exclusive mutations and incorporating prior biological knowledge about gene function or network structure. Dependencies among drivers due to epistatic interactions can also result in low mutation frequencies, but this effect has been ignored in driver detection so far. Here, we present a new computational approach for identifying genomic alterations that occur at low frequencies because they depend on other events. Unlike passengers, these constrained mutations display punctuated patterns of occurrence in time. We test this driver–passenger discrimination approach based on mutation timing in extensive simulation studies, and we apply it to cross-sectional copy number alteration (CNA) data from ovarian cancer, CNA and single-nucleotide variant (SNV) data from breast tumors and SNV data from colorectal cancer. Among the top ranked predicted drivers, we find low-frequency genes that have already been shown to be involved in carcinogenesis, as well as many new candidate drivers. The mutation timing approach is orthogonal and complementary to existing driver prediction methods. It will help identifying from cancer genome data the alterations that drive tumor progression.     

Progress and prospects in mapping recent selection in the genome

One of the central goals of evolutionary biology is to understand the genetic basis of adaptive evolution. The availability of nearly complete genome sequences from a variety of organisms has facilitated the collection of data on naturally occurring genetic variation on the scale of hundreds of loci to whole genomes. Such data have changed the focus of molecular population genetics from making inferences about adaptive evolution at single loci to identifying which loci, out of hundreds to thousands, have been recent targets of natural selection. A major challenge in this effort is distinguishing the effects of selection from those of the demographic history of populations. Here we review some current progress and remaining challenges in the field.     

Recombination disequilibrium in ideal and natural populations

Following Hardy-Weinberg disequilibrium (HWD) occurring at a single locus and linkage disequilibrium (LD) between two loci in generations, we here proposed the third genetic disequilibrium in a population: recombination disequilibrium (RD). RD is a measurement of crossover interference among multiple loci in a random mating population. In natural populations besides recombination interference, RD may also be due to selection, mutation, gene conversion, drift and/or migration. Therefore, similarly to LD, RD will also reflect the history of natural selection and mutation. In breeding populations, RD purely results from recombination interference and hence can be used to build or evaluate and correct a linkage map. Practical examples from F₂, testcross and human populations indeed demonstrate that RD is useful for measuring recombination interference between two short intervals and evaluating linkage maps. As with LD, RD will be important for studying genetic mapping, association of haplotypes with disease, plant breading and population history.     

Emergent neutrality in adaptive asexual evolution

In nonrecombining genomes, genetic linkage can be an important evolutionary force. Linkage generates interference interactions, by which simultaneously occurring mutations affect each other's chance of fixation. Here, we develop a comprehensive model of adaptive evolution in linked genomes, which integrates interference interactions between multiple beneficial and deleterious mutations into a unified framework. By an approximate analytical solution, we predict the fixation rates of these mutations, as well as the probabilities of beneficial and deleterious alleles at fixed genomic sites. We find that interference interactions generate a regime of emergent neutrality: all genomic sites with selection coefficients smaller in magnitude than a characteristic threshold have nearly random fixed alleles, and both beneficial and deleterious mutations at these sites have nearly neutral fixation rates. We show that this dynamic limits not only the speed of adaptation, but also a population's degree of adaptation in its current environment. We apply the model to different scenarios: stationary adaptation in a time-dependent environment and approach to equilibrium in a fixed environment. In both cases, the analytical predictions are in good agreement with numerical simulations. Our results suggest that interference can severely compromise biological functions in an adapting population, which sets viability limits on adaptive evolution under linkage.     

Statistical inference of selection and divergence from a time-dependent Poisson random field model

We apply a recently developed time-dependent Poisson random field model to aligned DNA sequences from two related biological species to estimate selection coefficients and divergence time. We use Markov chain Monte Carlo methods to estimate species divergence time and selection coefficients for each locus. The model assumes that the selective effects of non-synonymous mutations are normally distributed across genetic loci but constant within loci, and synonymous mutations are selectively neutral. In contrast with previous models, we do not assume that the individual species are at population equilibrium after divergence. Using a data set of 91 genes in two Drosophila species, D. melanogaster and D. simulans, we estimate the species divergence time t(div) = 2.16 N(e) (or 1.68 million years, assuming the haploid effective population size N(e) = 6.45 x 10(5) years) and a mean selection coefficient per generation μ(γ) = 1.98/N(e). Although the average selection coefficient is positive, the magnitude of the selection is quite small. Results from numerical simulations are also presented as an accuracy check for the time-dependent model.     

LIFE AT THE FRONT OF AN EXPANDING POPULATION

Environmental changes have caused episodes of habitat expansions in the evolutionary history of many species. These range changes affect the dynamics of biological evolution in multiple ways. Recent microbial experiments as well as simulations suggest that enhanced genetic drift at the frontier of a two-dimensional range expansion can cause genetic sectoring patterns with fractal domain boundaries. Here, we propose and analyze a simple model of asexual biological evolution at expanding frontiers that explains these neutral patterns and predicts the effect of natural selection. We find that beneficial mutations give rise to sectors with an opening angle that depends sensitively on the selective advantage of the mutants. Deleterious mutations, on the other hand, are not able to establish a sector permanently. They can, however, temporarily "surf" on the population front, and thereby reach unusually high frequencies. As a consequence, expanding frontiers are loaded with a high fraction of mutants at mutation–selection balance. Numerically, we also determine the condition at which the wild type is lost in favor of deleterious mutants (genetic meltdown) at a growing front. Our prediction for this error threshold differs qualitatively from existing well-mixed theories, and sets tight constraints on sustainable mutation rates for populations that undergo frequent range expansions.     

Perspective: Sign epistasis and genetic constraint on evolutionary trajectories

Epistasis for fitness means that the selective effect of a mutation is conditional on the genetic background in which it appears. Although epistasis is widely observed in nature, our understanding of its consequences for evolution by natural selection remains incomplete. In particular, much attention focuses only on its influence on the instantaneous rate of changes in frequency of selected alleles via epistatic contribution to the additive genetic variance for fitness. Thus, in this framework epistasis only has evolutionary importance if the interacting loci are simultaneously segregating in the population. However, the selective accessibility of mutational trajectories to high fitness genotypes may depend on the genetic background in which novel mutations appear, and this effect is independent of population polymorphism at other loci. Here we explore this second influence of epistasis on evolution by natural selection. We show that it is the consequence of a particular form of epistasis, which we designate sign epistasis. Sign epistasis means that the sign of the fitness effect of a mutation is under epistatic control; thus, such a mutation is beneficial on some genetic backgrounds and deleterious on others. Recent experimental innovations in microbial systems now permit assessment of the fitness effects of individual mutations on multiple genetic backgrounds. We review this literature and identify many examples of sign epistasis, and we suggest that the implications of these results may generalize to other organisms. These theoretical and empirical considerations imply that strong genetic constraint on the selective accessibility of trajectories to high fitness genotypes may exist and suggest specific areas of investigation for future research.     

Evaluation of a novel method for the identification of coevolving protein residues

A novel method for the identification of correlated pairs in aligned homologous protein sequences is presented and evaluated against a model of simulated protein evolution incorporating covariation. Our method is shown to be capable of identifying all coevolutionary pairs of sites, with minimal interference by background correlations, in aligned sequence sets containing approximately 60 sequences with a tree depth of at least 30 accepted point mutations. This result is expected even in the presence of a large degree of neutral and non-correlated evolution. It is postulated that, since naturally occurring protein families may be subject to stronger selection pressures and a lesser degree of neutral evolution, this method of covariation analysis may be generally more robust than the model would indicate.     

A powerful regression-based method for admixture mapping of isolation across the genome of hybrids

We propose a novel method that uses natural admixture between divergent lineages (hybridization) to investigate the genetic architecture of reproductive isolation and adaptive introgression. Our method employs multinomial regression to estimate genomic clines and to quantify introgression for individual loci relative to the genomic background (clines in genotype frequency along a genomic admixture gradient). Loci with patterns of introgression that deviate significantly from null expectations based on the remainder of the genome are potentially subject to selection and thus of interest to understanding adaptation and the evolution of reproductive isolation. Using simulations, we show that different forms of selection modify these genomic clines in predictable ways and that our method has good power to detect moderate to strong selection for multiple forms of selection. Using individual-based simulations, we demonstrate that our method generally has a low false positive rate, except when genetic drift is particularly pronounced (e.g. low population size, low migration rates from parental populations, and substantial time since initial admixture). Additional individual-based simulations reveal that moderate selection against heterozygotes can be detected as much as 50 c m away from the focal locus directly experiencing selection, but is not detected at unlinked loci. Finally, we apply our analytical method to previously published data sets from a mouse ( Mus musculus and M. domesticus ) and two sunflower ( Helianthus petiolaris and H. annuus ) hybrid zones. This method should be applicable to numerous species that are currently the focus of research in evolution and ecology and should help bring about new insights regarding the processes underlying the origin and maintenance of biological diversity.     

The dynamics of HIV-1 adaptation in early infection

Human immunodeficiency virus type 1 (HIV-1) undergoes a severe population bottleneck during sexual transmission and yet adapts extremely rapidly to the earliest immune responses. The bottleneck has been inferred to typically consist of a single genome, and typically eight amino acid mutations in viral proteins spread to fixation by the end of the early chronic phase of infection in response to selection by CD8(+) T cells. Stochastic simulation was used to examine the effects of the transmission bottleneck and of potential interference among spreading immune-escape mutations on the adaptive dynamics of the virus in early infection. If major viral population genetic parameters are assigned realistic values that permit rapid adaptive evolution, then a bottleneck of a single genome is not inconsistent with the observed pattern of adaptive fixations. One requirement is strong selection by CD8(+) T cells that decreases over time. Such selection may reduce effective population sizes at linked loci through genetic hitchhiking. However, this effect is predicted to be minor in early infection because the transmission bottleneck reduces the effective population size to such an extent that the resulting strong selection and weak mutation cause beneficial mutations to fix sequentially and thus avoid interference.     

Standing genetic variation and compensatory evolution in transgenic organisms: a growth-enhanced salmon simulation

Genetically modified strains usually are generated within defined genetic backgrounds to minimize variation for the engineered characteristic in order to facilitate basic research investigations or for commercial application. However, interactions between transgenes and genetic background have been documented in both model and commercial agricultural species, indicating that allelic variation at transgene-modifying loci are not uncommon in genomes. Engineered organisms that have the potential to allow entry of transgenes into natural populations may cause changes to ecosystems via the interaction of their specific phenotypes with ecosystem components and services. A transgene introgressing through natural populations is likely to encounter a range of natural genetic variation (among individuals or sub-populations) that could result in changes in phenotype, concomitant with effects on fitness and ecosystem consequences that differ from that seen in the progenitor transgenic strain. In the present study, using a growth hormone transgenic salmon example, we have modeled selection of modifier loci (single and multiple) in the presence of a transgene and have found that accounting for genetic background can significantly affect the persistence of transgenes in populations, potentially reducing or reversing a "Trojan gene" effect. Influences from altered life history characteristics (e.g., developmental timing, age of maturation) and compensatory demographic/ecosystem controls (e.g., density dependence) also were found to have a strong influence on transgene effects. Further, with the presence of a transgene in a population, genetic backgrounds were found to shift in non-transgenic individuals as well, an effect expected to direct phenotypes away from naturally selected optima. The present model has revealed the importance of understanding effects of selection for background genetics on the evolution of phenotypes in populations harbouring transgenes.     

Quantifying selection acting on a complex trait using allele frequency time series data

When selection is acting on a large genetically diverse population, beneficial alleles increase in frequency. This fact can be used to map quantitative trait loci by sequencing the pooled DNA from the population at consecutive time points and observing allele frequency changes. Here, we present a population genetic method to analyze time series data of allele frequencies from such an experiment. Beginning with a range of proposed evolutionary scenarios, the method measures the consistency of each with the observed frequency changes. Evolutionary theory is utilized to formulate equations of motion for the allele frequencies, following which likelihoods for having observed the sequencing data under each scenario are derived. Comparison of these likelihoods gives an insight into the prevailing dynamics of the system under study. We illustrate the method by quantifying selective effects from an experiment, in which two phenotypically different yeast strains were first crossed and then propagated under heat stress (Parts L, Cubillos FA, Warringer J, et al. [14 co-authors]. 2011. Revealing the genetic structure of a trait by sequencing a population under selection. Genome Res). From these data, we discover that about 6% of polymorphic sites evolve nonneutrally under heat stress conditions, either because of their linkage to beneficial (driver) alleles or because they are drivers themselves. We further identify 44 genomic regions containing one or more candidate driver alleles, quantify their apparent selective advantage, obtain estimates of recombination rates within the regions, and show that the dynamics of the drivers display a strong signature of selection going beyond additive models. Our approach is applicable to study adaptation in a range of systems under different evolutionary pressures.     

The effects of deleterious mutations on evolution at linked sites

The process of evolution at a given site in the genome can be influenced by the action of selection at other sites, especially when these are closely linked to it. Such selection reduces the effective population size experienced by the site in question (the Hill-Robertson effect), reducing the level of variability and the efficacy of selection. In particular, deleterious variants are continually being produced by mutation and then eliminated by selection at sites throughout the genome. The resulting reduction in variability at linked neutral or nearly neutral sites can be predicted from the theory of background selection, which assumes that deleterious mutations have such large effects that their behavior in the population is effectively deterministic. More weakly selected mutations can accumulate by Muller's ratchet after a shutdown of recombination, as in an evolving Y chromosome. Many functionally significant sites are probably so weakly selected that Hill-Robertson interference undermines the effective strength of selection upon them, when recombination is rare or absent. This leads to large departures from deterministic equilibrium and smaller effects on linked neutral sites than under background selection or Muller's ratchet. Evidence is discussed that is consistent with the action of these processes in shaping genome-wide patterns of variation and evolution.     

The evolution of genetic architecture under frequency-dependent disruptive selection

We propose a model to analyze a quantitative trait under frequency-dependent disruptive selection. Selection on the trait is a combination of stabilizing selection and intraspecific competition, where competition is maximal between individuals with equal phenotypes. In addition, there is a density-dependent component induced by population regulation. The trait is determined additively by a number of biallelic loci, which can have different effects on the trait value. In contrast to most previous models, we assume that the allelic effects at the loci can evolve due to epistatic interactions with the genetic background. Using a modifier approach, we derive analytical results under the assumption of weak selection and constant population size, and we investigate the full model by numerical simulations. We find that frequency-dependent disruptive selection favors the evolution of a highly asymmetric genetic architecture, where most of the genetic variation is concentrated on a small number of loci. We show that the evolution of genetic architecture can be understood in terms of the ecological niches created by competition. The phenotypic distribution of a population with an adapted genetic architecture closely matches this niche structure. Thus, evolution of the genetic architecture seems to be a plausible way for populations to adapt to regimes of frequency-dependent disruptive selection. As such, it should be seen as a potential evolutionary pathway to discrete polymorphisms and as a potential alternative to other evolutionary responses, such as the evolution of sexual dimorphism or assortative mating.     

Haldane's sieve and adaptation from the standing genetic variation

We consider populations that adapt to a sudden environmental change by fixing alleles found at mutation-selection balance. In particular, we calculate probabilities of fixation for previously deleterious alleles, ignoring the input of new mutations. We find that "Haldane's sieve"--the bias against the establishment of recessive beneficial mutations--does not hold under these conditions. Instead probabilities of fixation are generally independent of dominance. We show that this result is robust to patterns of sex expression for both X-linked and autosomal loci. We further show that adaptive evolution is invariably slower at X-linked than autosomal loci when evolution begins from mutation-selection balance. This result differs from that obtained when adaptation uses new mutations, a finding that may have some bearing on recent attempts to distinguish between hitchhiking and background selection by contrasting the molecular population genetics of X-linked vs. autosomal loci. Last, we suggest a test to determine whether adaptation used new mutations or previously deleterious alleles from the standing genetic variation.     

Components of Selection in the Evolution of the Influenza Virus: Linkage Effects Beat Inherent Selection

The influenza virus is an important human pathogen, with a rapid rate of evolution in the human population. The rate of homologous recombination within genes of influenza is essentially zero. As such, where two alleles within the same gene are in linkage disequilibrium, interference between alleles will occur, whereby selection acting upon one allele has an influence upon the frequency of the other. We here measured the relative importance of selection and interference effects upon the evolution of influenza. We considered time-resolved allele frequency data from the global evolutionary history of the haemagglutinin gene of human influenza A/H3N2, conducting an in-depth analysis of sequences collected since 1996. Using a model that accounts for selection-caused interference between alleles in linkage disequilibrium, we estimated the inherent selective benefit of individual polymorphisms in the viral population. These inherent selection coefficients were in turn used to calculate the total selective effect of interference acting upon each polymorphism, considering the effect of the initial background upon which a mutation arose, and the subsequent effect of interference from other alleles that were under selection. Viewing events in retrospect, we estimated the influence of each of these components in determining whether a mutant allele eventually fixed or died in the global viral population. Our inherent selection coefficients, when combined across different regions of the protein, were consistent with previous measurements of dN/dS for the same system. Alleles going on to fix in the global population tended to be under more positive selection, to arise on more beneficial backgrounds, and to avoid strong negative interference from other alleles under selection. However, on average, the fate of a polymorphism was determined more by the combined influence of interference effects than by its inherent selection coefficient.     

Climatic adaptation and ecological divergence between two closely related pine species in Southeast China

Climate is one of the most important drivers for adaptive evolution in forest trees. Climatic selection contributes greatly to local adaptation and intraspecific differentiation, but this kind of selection could also have promoted interspecific divergence through ecological speciation. To test this hypothesis, we examined intra‐ and interspecific genetic variation at 25 climate‐related candidate genes and 12 reference loci in two closely related pine species, Pinus massoniana Lamb. and Pinus hwangshanensis Hisa, using population genetic and landscape genetic approaches. These two species occur in Southeast China but have contrasting ecological preferences in terms of several environmental variables, notably altitude, although hybrids form where their distributions overlap. One or more robust tests detected signals of recent and/or ancient selection at two‐thirds (17) of the 25 candidate genes, at varying evolutionary timescales, but only three of the 12 reference loci. The signals of recent selection were species specific, but signals of ancient selection were mostly shared by the two species likely because of the shared evolutionary history. F_ST outlier analysis identified six SNPs in five climate‐related candidate genes under divergent selection between the two species. In addition, a total of 24 candidate SNPs representing nine candidate genes showed significant correlation with altitudinal divergence in the two species based on the covariance matrix of population history derived from reference SNPs. Genetic differentiation between these two species was higher at the candidate genes than at the reference loci. Moreover, analysis using the isolation‐with‐migration model indicated that gene flow between the species has been more restricted for climate‐related candidate genes than the reference loci, in both directions. Taken together, our results suggest that species‐specific and divergent climatic selection at the candidate genes might have counteracted interspecific gene flow and played a key role in the ecological divergence of these two closely related pine species.