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Evolve and resequence (E&R) experiments, in which artificial selection is imposed on organisms in a controlled environment, are becoming an increasingly accessible tool for studying the genetic basis of adaptation. Previous work has assessed how different experimental design parameters affect the power to detect the quantitative trait loci (QTL) that underlie adaptive responses in such experiments, but so far there has been little exploration of how this power varies with the genetic architecture of the evolving traits. In this study, we use forward simulation to build a more realistic model of an E&R experiment in which a quantitative polygenic trait experiences a short, but strong, episode of truncation selection. We study the expected power for QTL detection in such an experiment and how this power is influenced by different aspects of trait architecture, including the number of QTL affecting the trait, their starting frequencies, effect sizes, clustering along a chromosome, dominance, and epistasis patterns. We show that all of these parameters can affect allele frequency dynamics at the QTL and linked loci in complex and often unintuitive ways, and thus influence our power to detect them. One consequence of this is that existing detection methods based on models of independent selective sweeps at individual QTL often have lower detection power than a simple measurement of allele frequency differences before and after selection. Our findings highlight the importance of taking trait architecture into account when designing and interpreting studies of molecular adaptation with temporal data. We provide a customizable modeling framework that will enable researchers to easily simulate E&R experiments with different trait architectures and parameters tuned to their specific study system, allowing for assessment of expected detection power and optimization of experimental design.

The purpose of this study was to control inbreeding for selection experiments on woody breast (WB) meat in broiler. The simulation was designed to figure out which mating plan would show proper breeding values while optimizing inbreeding assuming that a selection study would be done on WB in broilers starting with 500 males and 500 females as a foundational population. The simulated selections were based on Optimum Genetic Contribution theory (OGC) under different conditions over 10 generations, which uses relationships among individuals as weighting factors. It is selecting individuals by weighting estimated breeding values with average relationships among individuals. From the 2nd generation, various selection plans were considered in each sex, which were top 10, 20, 50 and 100 males selected, and top 100 and 200 females selected every generation. Each female bird was assumed to have 10 eggs. The algorithm is as follows: 1) Identify the individual having the best EBV; 2) Calculate average relationships between selected and candidates; 3) Select the individual having the best EBV adjusted for average relationships using the weighting factor k; 4) Repeat process until the number of individuals selected equals number required. Three different weighting values (k=0, 1, 2) were used, which made a total 24 different conditions compared (4×2×3). Additive genetic variance of breast meat was 1.134. Mendelian sampling terms were also considered when the breeding values were generated. Results showed that higher k value (k=2) controlled effectively inbreeding and maintained consistent increases in selection response. Differences in breeding values among selection plans with OGC algorithm and by EBV only was 4% on average; however, average rate of inbreeding (0.1) was controlled by 27% after 10 generations. These results indicate that the OGC algorithm can be used effectively in a short-term selection program with the relatively smaller number of populations.

International audience; Selection programmes are mainly concerned with increasing genetic gain. However, short-term progress should not be obtained at the expense of the within-population genetic variability. Different prediction models for the evolution within a small population of the genetic mean of a selected trait, its genetic variance and its inbreeding have been developed but have mainly been validated through Monte Carlo simulation studies. The purpose of this study was to compare theoretical predictions to experimental results. Two deterministic methods were considered, both grounded on a polygenic additive model. Differences between theoretical predictions and experimental results arise from differences between the true and the assumed genetic model, and from mathematical simplifications applied in the prediction methods. Two sets of experimental lines of chickens were used in this study: the Dutch lines undergoing true truncation mass selection, the other lines (French) undergoing mass selection with a restriction on the representation of the different families. This study confirmed, on an experimental basis, that modelling is an efficient approach to make useful predictions of the evolution of selected populations although the basic assumptions considered in the models (polygenic additive model, normality of the distribution, base population at the equilibrium, etc.) are not met in reality. The two deterministic methods compared yielded results that were close to those observed in real data, especially when the selection scheme followed the rules of strict mass selection: for instance, both predictions overestimated the genetic gain in the French experiment, whereas both predictions were close to the observed values in the Dutch experiment.

Evolve and resequence (E&R) experiments, in which artificial selection is imposed on organisms in a controlled environment, are becoming an increasingly accessible tool for studying the genetic basis of adaptation. Previous work has assessed how different experimental design parameters affect the power to detect the quantitative trait loci (QTLs) that underlie adaptive responses in such experiments, but so far there has been little exploration of how this power varies with the genetic architecture of the evolving traits. In this study, we use forward simulation to build a realistic model of an E&R experiment in which a quantitative polygenic trait experiences a short, but strong, episode of truncation selection. We study the expected power for QTL detection in such an experiment and how this power is influenced by different aspects of trait architecture, including the number of QTLs affecting the trait, their starting frequencies, effect sizes, clustering along a chromosome, dominance, and epistasis patterns. We show that all of the above parameters can affect allele frequency dynamics at the QTLs and linked loci in complex and often unintuitive ways, and thus influence our power to detect them. One consequence of this is that existing detection methods based on models of independent selective sweeps at individual QTLs often have lower detection power than a simple measurement of allele frequency differences before and after selection. Our findings highlight the importance of taking trait architecture into account in designing studies of molecular adaptation with temporal data. We provide a customizable modeling framework that will enable researchers to easily simulate E&R experiments with different trait architectures and parameters tuned to their specific study system, allowing for assessment of expected detection power and optimization of experimental design.

This chapter examines short-term (a few generations) selection response in the mean of a trait. Traditionally, such experiments are analyzed using least-squares (LS) approaches. While ordinary LS (OLS) is often used, genetic drift causes the residual to be both correlated and heteroscedastic, resulting in the sampling variances given by OLS being too small. This chapter details the appropriate general LS (GLS) approaches to properly account for this residual error structure. It also reviews some of the common features observed in short-term selection experiments and examines experimental designs, such as the use of a control population versus a divergence-selection approach. It concludes by discussing another linear model used mainly by plant breeders, generation-means analysis (GMA), wherein remnant seed for several generations of response are crossed and then grown in a common garden. Such an analysis can provide insight into the genetic nature of any response.

Effective pest management strategies for a targeted pest species must rely on accurate, reliable and reproducible estimates of population dynamics. Importance of such approaches is even more conspicuous when assessing pest&rsquo, s potential to utilize other stored products. Using an experimental evolution approach, we have focused our attention on a common bean pest, the seed beetle (Acanthoscelides obtectus). We looked into the potential to invade and sustain population growth on two suboptimal host plants (chickpeas and mung beans). Such an approach simulates steps of the host-shift process in storages. By analyzing population dynamics during initial encountering with a new host plant, we detected a population drop for both novel hosts. However, transgenerational development in a novel environment resulted in a constant population growth in chickpeas, but not in mung bean populations. Reversal of chickpea selected populations to original host plant has led to a severe decrease in population parameters due to low viability of immatures, while the opposite trend was detected in mung bean populations. This paper highlights the importance of good practice in estimating population dynamics for economically important species. With special emphasis on storage pest species, we discuss how this approach can be useful for estimating invading potential of pest insects.

When the full pedigree of individuals whose values (records) were used in the selection decisions during an experiment (or breeding program) is known, LS analysis can be replaced by mixed models and their Bayesian extensions. In this setting, REML can be used to estimate genetic variances and BLUP can be used to estimate the mean breeding value in any given generation. The latter allows for genetic trends to be separated from environmental trends without the need for a control population. Under the infinitesimal model setting (wherein selection-induced allele-frequency changes are small during the course of the experiment), the use of the relationship matrix in a BLUP analysis accounts for drift, nonrandom mating, and linkage disequilibrium.

There is evidence that fisheries are altering the phenotypic composition of fish populations, often in ways that may reduce the value of fish stocks for the exploiters. Despite the increasing number of theoretical and field studies, there is still debate as to whether these changes are genetic, can be reversed, and are occurring rapidly enough to be considered in fisheries management. We review the contribution that selection experiments have already provided with respect to the study of the evolutionary effect of fisheries, identify issues that still require more study, and outline future directions for doing so. Selection experiments have already been crucial in showing that harvesting can lead to phenotypic and genetic evolution over relatively short time frames. Furthermore, the experiments have shown the changes involve many other traits than those under direct selection, and that these changes tend to have population-level consequences, including a decreasing fisheries yield. However, experiments focused on fisheries-induced evolution that fulfil all our requirements are still lacking. Future studies should have more controlled and realistic set-ups and assess genetic changes in maturation and growth (i.e. traits most often reported to change) to be more relevant to exploited populations in the wild.

Summary Selection experiments in laboratory animals and livestock have provided a wealth of information on genetic parameters of quantitative traits and on the effectiveness of selection in the short and long term on both directly selected and correlated traits. They have stimulated developments in theory and tests of it, and extreme selected lines continue to be source material for biological study. Some of the main questions and findings are briefly reviewed. Yet much of successful animal breeding practice has been based essentially on statistical methods, assuming where necessary the infinitesimal model, and new developments such as genomic selection are similarly not based on selection experiments. Information on the genetic architecture of quantitative traits is provided by selection experiments, but new methods for deeper studies of the biology are available. I discuss the future role for selection experiments in view of changes in funding streams and technology and conclude that there is little case for starting new experiments, but retention of existing long-term lines is desirable and DNA should be collected from all lines on a continuing basis.

Directed evolution is a powerful approach for isolating high-affinity binders from complex libraries. In affinity maturation experiments, binders with the highest affinities in the library are typically isolated through selections for decreased off rate using a suitable selection platform (e.g. phage display or ribosome display). In such experiments, the library is initially exposed to biotinylated antigen and the binding reaction is allowed to proceed. A large excess of unbiotinylated antigen is then added as a competitor to capture the vast majority of rapidly dissociating molecules; the slowly dissociating library members can subsequently be rescued by capturing the biotin-carrying complexes. To optimize the parameters for such affinity maturation experiments, we performed both deterministic and stochastic simulations of off-rate selection experiments using different input libraries. Our results suggest that the most critical parameters for achieving the lowest off rates after selection are the ratio of competitor antigen to selectable antigen and the selection time. Furthermore, the selection time has an optimum that depends on the experimental setup and the nature of the library. Notably, if selections are carried out for times much longer than the optimum, equilibrium is reached and the selection pressure is weakened or lost. Comparison of different selection strategies revealed that sequential selection rounds with lower stringency are favored over high-stringency selection experiments due to enhanced diversity in the selected pools. Such simulations may be helpful in optimizing affinity maturation strategies and off-rate selection experiments.

Experimental evolution studies can be used to explore genomic response to artificial and natural selection. In such studies, loci that display larger allele frequency change than expected by genetic drift alone are assumed to be directly or indirectly associated with traits under selection. However, such studies report surprisingly many loci under selection, suggesting that current tests for allele frequency change may be subject to P-value inflation and hence be anticonservative. One factor known from genomewide association (GWA) studies to cause P-value inflation is population stratification, such as relatedness among individuals. Here, we suggest that by treating presence of an individual in a population after selection as a binary response variable, existing GWA methods can be used to account for relatedness when estimating allele frequency change. We show that accounting for relatedness like this effectively reduces false-positives in tests for allele frequency change in simulated data with varying levels of population structure. However, once relatedness has been accounted for, the power to detect causal loci under selection is low. Finally, we demonstrate the presence of P-value inflation in allele frequency change in empirical data spanning multiple generations from an artificial selection experiment on tarsus length in two free-living populations of house sparrow and correct for this using genomic control. Our results indicate that since allele frequencies in large parts of the genome may change when selection acts on a heritable trait, such selection is likely to have considerable and immediate consequences for the eco-evolutionary dynamics of the affected populations. This is the peer reviewed version of the following article: [Controlling for P-value inflation in allele frequency change in experimental evolution and artificial selection experiments], which has been published in final form at [http://onlinelibrary.wiley.com/doi/10.1111/1755-0998.12631/abstract]. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving. Locked until 25.11.2017 due to copyright restrictions.

TMC125 is a potent new investigational nonnucleoside reverse transcriptase inhibitor (NNRTI) that is active against human immunodeficiency virus type 1 (HIV-1) with resistance to currently licensed NNRTIs. Sequential passage experiments with both wild-type virus and NNRTI-resistant virus were performed to identify mutations selected by TMC125 in vitro. In addition to “classic” selection experiments at a low multiplicity of infection (MOI) with increasing concentrations of inhibitors, experiments at a high MOI with fixed concentrations of inhibitors were performed to ensure a standardized comparison between TMC125 and current NNRTIs. Both low- and high-MOI experiments demonstrated that the development of resistance to TMC125 required multiple mutations which frequently conferred cross-resistance to efavirenz and nevirapine. In high-MOI experiments, 1 μM TMC125 completely inhibited the breakthrough of resistant virus from wild-type and NNRTI-resistant HIV-1, in contrast to efavirenz and nevirapine. Furthermore, breakthrough of virus from site-directed mutant (SDM) SDM-K103N/Y181C occurred at the same time or later with TMC125 as breakthrough from wild-type HIV-1 with efavirenz or nevirapine. The selection experiments identified mutations selected by TMC125 that included known NNRTI-associated mutations L100I, Y181C, G190E, M230L, and Y318F and the novel mutations V179I and V179F. Testing the antiviral activity of TMC125 against a panel of SDMs indicated that the impact of these individual mutations on resistance was highly dependent upon the presence and identity of coexisting mutations. These results demonstrate that TMC125 has a unique profile of activity against NNRTI-resistant virus and possesses a high genetic barrier to the development of resistance in vitro.

SYNOPSIS. Laboratory natural selection and artificial selection are vital tools for addressing specific questions about evolutionary patterns of variation. Laboratory natural selection can illuminate whether a putative selective agent is capable of generating long-term, sustained changes in individual traits and suites of traits. Artificial selection is the essential tool for understanding the general evolvability of traits and the extent to which genetic correlations constrain evolution. We review the contexts in which each type of experiment seems capable of offering key insights into important evolutionary issues. We also discuss theoretical and methodological considerations that play critical roles in designing selection experiments that are relevant to evolutionary patterns of trait variation. In particular, we focus on the critical role of selection intensity and the consequences of experiments with different intensities. While selection experiments are not practical in many cases, sophisticated selection experiments—designed with careful consideration of the theory of selection—should be taken beyond model organisms and used in well-chosen natural systems to understand natural patterns of variation.

A major goal in evolutionary biology is to understand the origins and fates of adaptive mutations. Natural selection may act to increase the frequency of de novo beneficial mutations, or those already present in the population as standing genetic variation. These beneficial mutations may ultimately reach fixation in a population, or they may stop increasing in frequency once a particular phenotypic state has been achieved. It is not yet well understood how different features of population biology, and/or different environmental circumstances affect these adaptive processes. Experimental evolution is a promising technique for studying the dynamics of beneficial alleles, as populations evolving in the laboratory experience natural selection in a replicated, controlled manner. Whole-genome sequencing, regularly obtained over the course of sustained laboratory selection, could potentially reveal insights into the mutational dynamics that most likely occur in natural populations under similar circumstances. To date, only a few evolution experiments for which whole-genome data are available exist. This review describes results from these resequenced laboratory-selected populations, in systems with and without sexual recombination. In asexual systems, adaptation from new mutations can be studied, and results to date suggest that the complete, unimpeded fixation of these mutations is not always observed. In sexual systems, adaptation from standing genetic variation can be studied, and in the admittedly few examples we have, the complete fixation of standing variants is not always observed. To date, the relative frequency of adaptation from new mutations versus standing variation has not been tested using a single experimental system, but recent studies usingCaenorhabditis elegansandSaccharomyces cerevisiaesuggest that this a realistic future goal.

Bacteria possess a range of mechanisms to move in different environments, and these mechanisms have important direct and correlated impacts on the virulence of opportunistic pathogens. Bacteria use two surface organelles to facilitate motility: a single polar flagellum, and type IV pili, enabling swimming in aqueous habitats and twitching along hard surfaces, respectively. Here, we address whether there are trade-offs between these motility mechanisms, and hence whether different environments could select for altered motility. We experimentally evolved initially isogenic Pseudomonas aeruginosa under conditions that favored the different types of motility, and found evidence for a trade-off mediated by antagonistic pleiotropy between swimming and twitching. Moreover, changes in motility resulted in correlated changes in other behaviors, including biofilm formation and growth within an insect host. This suggests environmental origins of a particular motile opportunistic pathogen could predictably influence motility and virulence.

Theories of the evolution of niche breadth usually depend on the assumption that genotypes that are well adapted to exploit one habitat or resource are not well adapted to others. Such “trade-offs” are often apparent in interspecific comparisons, but have been harder to document at the within-population level. Selection experiments provide a promising means for detecting within-population trade-offs: if selection for adaptation to one environment (e.g., diet, host, temperature) reproducibly lowers fitness in another, trade-offs are the likeliest explanation. Here, I describe strategies for using selection experiments to detect ecological trade-offs and discuss some of the ambiguities that can arise in interpreting the results of such experiments. I also review two sets of studies that found evidence for trade-offs using selection experiments.

Population cage experiments were employed to detect variability in fitness among Cry2Ab resistant and Cry2Ab susceptible genotypes of Helicoverpa armigera (Hubner) (Lepidoptera: Noctuidae). In two experiments, reciprocal crosses between a Cry2Ab resistant colony (SP15) and a susceptible colony (GR) established populations where the frequency of the allele that conferred resistance was 0.5. Experimental populations were then maintained without exposure to Cry toxins. At the F2 generation and on later occasions, the pooled egg output from each population was sampled, and emerging neonate larvae were screened to monitor the frequency of the resistant allele. Resistance is recessive so homozygous resistant insects could be readily identified as they are the only genotype to survive and grow when exposed to a discriminating concentration of Cry2Ab toxin. Assuming Hardy—Weinberg equilibrium after the F1 generation, and the persistence of a 1:1 ratio of resistant and susceptible alleles, one quarter of ...

Laboratory selection experiments are powerful tools for establishing evolutionary potentials. Such experiments provide two types of information, knowledge about genetic architecture and insight into evolutionary dynamics. They can be roughly classified into two types: (1) artificial selection in which the experimenter selects on a focal trait or trait index, and (2) quasi-natural selection in which the experimenter establishes a set of environmental conditions and then allows the population to evolve. Both approaches have been used in the study of phenotypic plasticity. Artificial selection experiments have taken various forms including: selection directly on a reaction norm, selection on a trait in multiple environments, and selection on a trait in a single environment. In the latter experiments, evolution of phenotypic plasticity is investigated as a correlated response. Quasi-natural selection experiments have examined the effects of both spatial and temporal variation. I describe how to carry out such experiments, summarize past efforts, and suggest further avenues of research.

A complex substrate consisting of fly ash, coal gangue and excess sludge was used as an experimental soil in pot culture experiments. Different soil compositions were tested by observing the growth of arbuscular mycorrhizal (AM) fungi inoculated white clover, rye grass or corn. The biomass of the host plants, the mycorrhizal colonization (MC) rate and the mycorrhizal dependency (MD) were measured. The research addresses the preferable AMF-plant-substrate combination appropriate for restoration of coal mines. We used two inoculation methods: single-inoculation with Glomus versiforme or Glomus mosseae and a dual inoculation with both G.v and G.m. The results show that G.m is the preferable fungi and that dual inoculation does not show advantages for the restoration of coal mines. White clover inoculated with AM fungi is the most suitable condition for restoration of coal mines. The best weight ratio of fly ash, coal gangue and excess sludge was found to be 20:60:20. The optimum treatment conditions of AMF-plant-activated-substrate are described.

Two common features of long-term selection experiments are that, first, there is typically no evidence for selection limits due to exhaustion of genetic variation, and second, selection plateaus are frequently observed that last multiple generations before a response to selection is resumed. These features are usually attributed to the high mutation rates of quantitative traits, and the effects of linkage disequilibrium. Using previously published theoretical results and a simple deterministic model I explore the potential role of gene interaction in generating these patterns seen in the response to long-term selection experiments. I show that epistasis provides a pool of variation that can contribute to an extended response to selection, but that this extended response will, at least under some circumstances, result in intermediate selection plateaus.

Two common features of long-term selection experiments are that, first, there is typically no evidence for selection limits due to exhaustion of genetic variation, and second, selection plateaus are frequently observed that last multiple generations before a response to selection is resumed. These features are usually attributed to the high mutation rates of quantitative traits, and the effects of linkage disequilibrium. Using previously published theoretical results and a simple deterministic model I explore the potential role of gene interaction in generating these patterns seen in the response to long-term selection experiments. I show that epistasis provides a pool of variation that can contribute to an extended response to selection, but that this extended response will, at least under some circumstances, result in intermediate selection plateaus.