Diamantis Sellis

Heterozygote advantage as a natural consequence of adaptation in diploids

Molecular adaptation is typically assumed to proceed by sequential fixation of beneficial mutations. In diploids, this picture presupposes that for most adaptive mutations, the homozygotes have a higher fitness than the heterozygotes. Here, we show that contrary to this expectation, a substantial proportion of adaptive mutations should display heterozygote advantage. This feature of adaptation in diploids emerges naturally from the primary importance of the fitness of heterozygotes for the invasion of new adaptive mutations. We formalize this result in the framework of Fishers influential geometric model of adaptation. We find that in diploids, adaptation should often proceed through a succession of short-lived balanced states that maintain substantially higher levels of phenotypic and fitness variation in the population compared with classic adaptive walks. In fast-changing environments, this variation produces a diversity advantage that allows diploids to remain better adapted compared with haploids despite the disadvantage associated with the presence of unfit homozygotes. The short-lived balanced states arising during adaptive walks should be mostly invisible to current scans for long-term balancing selection. Instead, they should leave signatures of incomplete selective sweeps, which do appear to be common in many species. Our results also raise the possibility that balancing selection, as a natural consequence of frequent adaptation, might play a more prominent role among the forces maintaining genetic variation than is commonly recognized.

Heterozygote Advantage Is a Common Outcome of Adaptation in Saccharomyces cerevisiae.

Adaptation in diploids is predicted to proceed via mutations that are at least partially dominant in fitness. Recently, we argued that many adaptive mutations might also be commonly overdominant in fitness. Natural (directional) selection acting on overdominant mutations should drive them into the population but then, instead of bringing them to fixation, should maintain them as balanced polymorphisms via heterozygote advantage. If true, this would make adaptive evolution in sexual diploids differ drastically from that of haploids. The validity of this prediction has not yet been tested experimentally. Here, we performed four replicate evolutionary experiments with diploid yeast populations (Saccharomyces cerevisiae) growing in glucose-limited continuous cultures. We sequenced 24 evolved clones and identified initial adaptive mutations in all four chemostats. The first adaptive mutations in all four chemostats were three copy number variations, all of which proved to be overdominant in fitness. The fact that fitness overdominant mutations were always the first step in independent adaptive walks supports the prediction that heterozygote advantage can arise as a common outcome of directional selection in diploids and demonstrates that overdominance of de novo adaptive mutations in diploids is not rare.

Conserved noncoding elements follow power-law-like distributions in several genomes as a result of genome dynamics.

Conserved, ultraconserved and other classes of constrained elements (collectively referred as CNEs here), identified by comparative genomics in a wide variety of genomes, are non-randomly distributed across chromosomes. These elements are defined using various degrees of conservation between organisms and several thresholds of minimal length. We here investigate the chromosomal distribution of CNEs by studying the statistical properties of distances between consecutive CNEs. We find widespread power-law-like distributions, i.e. linearity in double logarithmic scale, in the inter-CNE distances, a feature which is connected with fractality and self-similarity. Given that CNEs are often found to be spatially associated with genes, especially with those that regulate developmental processes, we verify by appropriate gene masking that a power-law-like pattern emerges irrespectively of whether elements found close or inside genes are excluded or not. An evolutionary model is put forward for the understanding of these findings that includes segmental or whole genome duplication events and eliminations (loss) of most of the duplicated CNEs. Simulations reproduce the main features of the observed size distributions. Power-law-like patterns in the genomic distributions of CNEs are in accordance with current knowledge about their evolutionary history in several genomes.

Patterns of variation during adaptation in functionally linked loci

An understanding of the distribution of natural patterns of genetic variation is relevant to such fundamental biological fields as evolution and development. One recent approach to understanding such patterns has been to focus on the constraints that may arise as a function of the network or pathway context in which genes are embedded. Despite theoretical expectations of higher evolutionary constraint for genes encoding upstream versus downstream enzymes in metabolic pathways, empirical results have varied. Here we combine two complementary models from population genetics and enzyme kinetics to explore genetic variation as a function of pathway position when selection acts on whole‐pathway flux. We are able to qualitatively reproduce empirically observed patterns of polymorphism and divergence and suggest that expectations should vary depending on the evolutionary trajectory of a population. Upstream genes are initially more polymorphic and diverge faster after an environmental change, while we see the opposite trend as the population approaches its fitness optimum.

A Study of Fractality and Long-Range Order in the Distribution of Transposable Elements in Eukaryotic Genomes Using the Scaling Properties of Block Entropy and Box-Counting

Repeats or Transposable Elements (TEs) are highly repeated sequence stretches, present in virtually all eukaryotic genomes. We explore the distribution of representative TEs from all major classes in entire chromosomes across various organisms. We employ two complementary approaches, the scaling of block entropy and box-counting. Both converge to the conclusion that well-developed fractality is typical of small genomes while in large genomes it appears sporadically and in some cases is rudimentary. The human genome is particularly prone to develop this pattern, as TE chromosomal distributions therein are often highly clustered and inhomogeneous. Comparing with previous works, where occurrence of power-law-like size distributions in inter-repeat distances is studied, we conclude that fractality in entire chromosomes is a more stringent (thus less often encountered) condition. We have formulated a simple evolutionary scenario for the genomic dynamics of TEs, which may account for their fractal distribution in real genomes. The observed fractality and long-range properties of TE genomic distributions have probably contributed to the formation of the “fractal globule”, a model for the confined chromatin organization of the eukaryotic nucleus proposed on the basis of experimental evidence.

Ploidy and the Predictability of Evolution in Fishers Geometric Model

Predicting the future evolutionary state of a population is a primary goal of evolutionary biology. One can differentiate between forward and backward predictability, where forward predictability is the probability of the same adaptive outcome occurring in independent evolutionary trials, and backward predictability is the likelihood of a particular adaptive path given the knowledge of the starting and final states. Most studies of evolutionary predictability assume that alleles along an adaptive walk fix in succession with individual adaptive mutations occurring in monomorphic populations. However, in nature, adaptation generally occurs within polymorphic populations, and there are a number of mechanisms by which polymorphisms can be stably maintained by natural selection. Here we investigate the predictability of evolution in monomorphic and polymorphic situations by studying adaptive walks in diploid populations using Fishers geometric model, which has been previously found to generate balanced polymorphisms through overdominant mutations. We show that overdominant mutations cause a decrease in forward predictability and an increase in backward predictability relative to diploid walks lacking balanced states. We also show that in the presence of balanced polymorphisms, backward predictability analysis can lead to counterintuitive outcomes such as reaching different final adapted population states depending on the order in which mutations are introduced and cases where the true adaptive trajectory appears inviable. As stable polymorphisms can be generated in both haploid and diploid natural populations through a number of mechanisms, we argue that natural populations may contain complex evolutionary histories that may not be easily inferred without historical sampling.Predicting the course of evolution is critical for solving current biomedical challenges such as cancer and the evolution of drug resistant pathogens. One approach to studying evolutionary predictability is to observe repeated, independent evolutionary trajectories of similar organisms under similar selection pressures in order to empirically characterize this adaptive fitness landscape. As this approach is infeasible for many natural systems, a number of recent studies have attempted to gain insight into the adaptive fitness landscape by testing the plausibility of different orders of appearance for a specific set of adaptive mutations in a single adaptive trajectory. While this approach is technically feasible for systems with very few available adaptive mutations, the usefulness of this approach for predicting evolution in situations with highly polygenic adaptation is unknown. It is also unclear whether the presence of stable adaptive polymorphisms can influence the predictability of evolution as measured by these methods. In this work, we simulate adaptive evolution under Fisher’s geometric model to study evolutionary predictability. Remarkably, we find that the predictability estimated by these methods are anti-correlated, and that the presence of stable adaptive polymorphisms can both qualitatively and quantitatively change the predictability of evolution.Several recent experimental studies assessed the likelihood of all possible evolutionary paths between ancestral and evolved sequences. All of these studies measured the fitness of the intermediate genotypes and assumed that the advantageous genotypes fix in the population before acquiring the next adaptive mutation along the path. Unfortunately, the successive fixation assumption used by these studies is typically invalid, given that natural selection often maintain alleles at intermediate frequency by a variety of mechanisms such as frequency-dependent selection, local adaptation, clonal interference, and fitness overdominance. Here we simulate adaptive walks using Fishers geometric model in diploid populations where previous work has shown that adaptation commonly generates balanced polymorphisms through overdominant mutations. We use these simulations to show that the use of the successive fixation assumption in this simple model is largely justified if the goal is to separate viable and inviable paths from each other. However, the estimates of the relative likelihoods of the viable paths become unreliable. We also show that the presence of balanced states along the true path significantly affects the number and likelihood distribution of viable paths when compared to walks without balanced states. These simple simulations highlight the importance of considering the effect of polymorphisms during adaptation especially given the prevalence of functional polymorphisms in natural populations.

On the study of evolutionary predictability using historical reconstruction

Predicting the future evolutionary state of a population is a primary goal of evolutionary biology. One can differentiate between forward and backward predictability, where forward predictability is the probability of the same adaptive outcome occurring in independent evolutionary trials, and backward predictability is the likelihood of a particular adaptive path given the knowledge of the starting and final states. Most studies of evolutionary predictability assume that alleles along an adaptive walk fix in succession with individual adaptive mutations occurring in monomorphic populations. However, in nature, adaptation generally occurs within polymorphic populations, and there are a number of mechanisms by which polymorphisms can be stably maintained by natural selection. Here we investigate the predictability of evolution in monomorphic and polymorphic situations by studying adaptive walks in diploid populations using Fishers geometric model, which has been previously found to generate balanced polymorphisms through overdominant mutations. We show that overdominant mutations cause a decrease in forward predictability and an increase in backward predictability relative to diploid walks lacking balanced states. We also show that in the presence of balanced polymorphisms, backward predictability analysis can lead to counterintuitive outcomes such as reaching different final adapted population states depending on the order in which mutations are introduced and cases where the true adaptive trajectory appears inviable. As stable polymorphisms can be generated in both haploid and diploid natural populations through a number of mechanisms, we argue that natural populations may contain complex evolutionary histories that may not be easily inferred without historical sampling.Predicting the course of evolution is critical for solving current biomedical challenges such as cancer and the evolution of drug resistant pathogens. One approach to studying evolutionary predictability is to observe repeated, independent evolutionary trajectories of similar organisms under similar selection pressures in order to empirically characterize this adaptive fitness landscape. As this approach is infeasible for many natural systems, a number of recent studies have attempted to gain insight into the adaptive fitness landscape by testing the plausibility of different orders of appearance for a specific set of adaptive mutations in a single adaptive trajectory. While this approach is technically feasible for systems with very few available adaptive mutations, the usefulness of this approach for predicting evolution in situations with highly polygenic adaptation is unknown. It is also unclear whether the presence of stable adaptive polymorphisms can influence the predictability of evolution as measured by these methods. In this work, we simulate adaptive evolution under Fisher’s geometric model to study evolutionary predictability. Remarkably, we find that the predictability estimated by these methods are anti-correlated, and that the presence of stable adaptive polymorphisms can both qualitatively and quantitatively change the predictability of evolution.Several recent experimental studies assessed the likelihood of all possible evolutionary paths between ancestral and evolved sequences. All of these studies measured the fitness of the intermediate genotypes and assumed that the advantageous genotypes fix in the population before acquiring the next adaptive mutation along the path. Unfortunately, the successive fixation assumption used by these studies is typically invalid, given that natural selection often maintain alleles at intermediate frequency by a variety of mechanisms such as frequency-dependent selection, local adaptation, clonal interference, and fitness overdominance. Here we simulate adaptive walks using Fishers geometric model in diploid populations where previous work has shown that adaptation commonly generates balanced polymorphisms through overdominant mutations. We use these simulations to show that the use of the successive fixation assumption in this simple model is largely justified if the goal is to separate viable and inviable paths from each other. However, the estimates of the relative likelihoods of the viable paths become unreliable. We also show that the presence of balanced states along the true path significantly affects the number and likelihood distribution of viable paths when compared to walks without balanced states. These simple simulations highlight the importance of considering the effect of polymorphisms during adaptation especially given the prevalence of functional polymorphisms in natural populations.

Predictability of adaptive evolution under the successive fixation assumption

Predicting the future evolutionary state of a population is a primary goal of evolutionary biology. One can differentiate between forward and backward predictability, where forward predictability is the probability of the same adaptive outcome occurring in independent evolutionary trials, and backward predictability is the likelihood of a particular adaptive path given the knowledge of the starting and final states. Most studies of evolutionary predictability assume that alleles along an adaptive walk fix in succession with individual adaptive mutations occurring in monomorphic populations. However, in nature, adaptation generally occurs within polymorphic populations, and there are a number of mechanisms by which polymorphisms can be stably maintained by natural selection. Here we investigate the predictability of evolution in monomorphic and polymorphic situations by studying adaptive walks in diploid populations using Fishers geometric model, which has been previously found to generate balanced polymorphisms through overdominant mutations. We show that overdominant mutations cause a decrease in forward predictability and an increase in backward predictability relative to diploid walks lacking balanced states. We also show that in the presence of balanced polymorphisms, backward predictability analysis can lead to counterintuitive outcomes such as reaching different final adapted population states depending on the order in which mutations are introduced and cases where the true adaptive trajectory appears inviable. As stable polymorphisms can be generated in both haploid and diploid natural populations through a number of mechanisms, we argue that natural populations may contain complex evolutionary histories that may not be easily inferred without historical sampling.Predicting the course of evolution is critical for solving current biomedical challenges such as cancer and the evolution of drug resistant pathogens. One approach to studying evolutionary predictability is to observe repeated, independent evolutionary trajectories of similar organisms under similar selection pressures in order to empirically characterize this adaptive fitness landscape. As this approach is infeasible for many natural systems, a number of recent studies have attempted to gain insight into the adaptive fitness landscape by testing the plausibility of different orders of appearance for a specific set of adaptive mutations in a single adaptive trajectory. While this approach is technically feasible for systems with very few available adaptive mutations, the usefulness of this approach for predicting evolution in situations with highly polygenic adaptation is unknown. It is also unclear whether the presence of stable adaptive polymorphisms can influence the predictability of evolution as measured by these methods. In this work, we simulate adaptive evolution under Fisher’s geometric model to study evolutionary predictability. Remarkably, we find that the predictability estimated by these methods are anti-correlated, and that the presence of stable adaptive polymorphisms can both qualitatively and quantitatively change the predictability of evolution.Several recent experimental studies assessed the likelihood of all possible evolutionary paths between ancestral and evolved sequences. All of these studies measured the fitness of the intermediate genotypes and assumed that the advantageous genotypes fix in the population before acquiring the next adaptive mutation along the path. Unfortunately, the successive fixation assumption used by these studies is typically invalid, given that natural selection often maintain alleles at intermediate frequency by a variety of mechanisms such as frequency-dependent selection, local adaptation, clonal interference, and fitness overdominance. Here we simulate adaptive walks using Fishers geometric model in diploid populations where previous work has shown that adaptation commonly generates balanced polymorphisms through overdominant mutations. We use these simulations to show that the use of the successive fixation assumption in this simple model is largely justified if the goal is to separate viable and inviable paths from each other. However, the estimates of the relative likelihoods of the viable paths become unreliable. We also show that the presence of balanced states along the true path significantly affects the number and likelihood distribution of viable paths when compared to walks without balanced states. These simple simulations highlight the importance of considering the effect of polymorphisms during adaptation especially given the prevalence of functional polymorphisms in natural populations.

Polymorphism and the Predictability of Evolution in Fisher?s Geometric Model

Predicting the future evolutionary state of a population is a primary goal of evolutionary biology. One can differentiate between forward and backward predictability, where forward predictability is the probability of the same adaptive outcome occurring in independent evolutionary trials, and backward predictability is the likelihood of a particular adaptive path given the knowledge of the starting and final states. Most studies of evolutionary predictability assume that alleles along an adaptive walk fix in succession with individual adaptive mutations occurring in monomorphic populations. However, in nature, adaptation generally occurs within polymorphic populations, and there are a number of mechanisms by which polymorphisms can be stably maintained by natural selection. Here we investigate the predictability of evolution in monomorphic and polymorphic situations by studying adaptive walks in diploid populations using Fishers geometric model, which has been previously found to generate balanced polymorphisms through overdominant mutations. We show that overdominant mutations cause a decrease in forward predictability and an increase in backward predictability relative to diploid walks lacking balanced states. We also show that in the presence of balanced polymorphisms, backward predictability analysis can lead to counterintuitive outcomes such as reaching different final adapted population states depending on the order in which mutations are introduced and cases where the true adaptive trajectory appears inviable. As stable polymorphisms can be generated in both haploid and diploid natural populations through a number of mechanisms, we argue that natural populations may contain complex evolutionary histories that may not be easily inferred without historical sampling.Predicting the course of evolution is critical for solving current biomedical challenges such as cancer and the evolution of drug resistant pathogens. One approach to studying evolutionary predictability is to observe repeated, independent evolutionary trajectories of similar organisms under similar selection pressures in order to empirically characterize this adaptive fitness landscape. As this approach is infeasible for many natural systems, a number of recent studies have attempted to gain insight into the adaptive fitness landscape by testing the plausibility of different orders of appearance for a specific set of adaptive mutations in a single adaptive trajectory. While this approach is technically feasible for systems with very few available adaptive mutations, the usefulness of this approach for predicting evolution in situations with highly polygenic adaptation is unknown. It is also unclear whether the presence of stable adaptive polymorphisms can influence the predictability of evolution as measured by these methods. In this work, we simulate adaptive evolution under Fisher’s geometric model to study evolutionary predictability. Remarkably, we find that the predictability estimated by these methods are anti-correlated, and that the presence of stable adaptive polymorphisms can both qualitatively and quantitatively change the predictability of evolution.Several recent experimental studies assessed the likelihood of all possible evolutionary paths between ancestral and evolved sequences. All of these studies measured the fitness of the intermediate genotypes and assumed that the advantageous genotypes fix in the population before acquiring the next adaptive mutation along the path. Unfortunately, the successive fixation assumption used by these studies is typically invalid, given that natural selection often maintain alleles at intermediate frequency by a variety of mechanisms such as frequency-dependent selection, local adaptation, clonal interference, and fitness overdominance. Here we simulate adaptive walks using Fishers geometric model in diploid populations where previous work has shown that adaptation commonly generates balanced polymorphisms through overdominant mutations. We use these simulations to show that the use of the successive fixation assumption in this simple model is largely justified if the goal is to separate viable and inviable paths from each other. However, the estimates of the relative likelihoods of the viable paths become unreliable. We also show that the presence of balanced states along the true path significantly affects the number and likelihood distribution of viable paths when compared to walks without balanced states. These simple simulations highlight the importance of considering the effect of polymorphisms during adaptation especially given the prevalence of functional polymorphisms in natural populations.