Unpredictability of the Fitness Effects of Antimicrobial Resistance Mutations Across Environments in Escherichia coli

The evolution of antimicrobial resistance (AMR) in bacteria is a major public health concern, and antibiotic restriction is often implemented to reduce the spread of resistance. These measures rely on the existence of deleterious fitness effects (i.e. costs) imposed by AMR mutations during growth in the absence of antibiotics. According to this assumption, resistant strains will be outcompeted by susceptible strains that do not pay the cost during the period of restriction. The fitness effects of AMR mutations are generally studied in laboratory reference strains grown in standard growth environments; however, the genetic and environmental context can influence the magnitude and direction of a mutation's fitness effects. In this study, we measure how three sources of variation impact the fitness effects of Escherichia coli AMR mutations: the type of resistance mutation, the genetic background of the host, and the growth environment. We demonstrate that while AMR mutations are generally costly in antibiotic-free environments, their fitness effects vary widely and depend on complex interactions between the mutation, genetic background, and environment. We test the ability of the Rough Mount Fuji fitness landscape model to reproduce the empirical data in simulation. We identify model parameters that reasonably capture the variation in fitness effects due to genetic variation. However, the model fails to accommodate the observed variation when considering multiple growth environments. Overall, this study reveals a wealth of variation in the fitness effects of resistance mutations owing to genetic background and environmental conditions, which will ultimately impact their persistence in natural populations.

A predominant role of genotypic variation in both expression of sperm competition genes and paternity success in Drosophila melanogaster

Sperm competition is a crucial aspect of male reproductive success in many species, including Drosophila melanogaster, and seminal fluid proteins (Sfps) can influence sperm competitiveness. However, the combined effect of environmental and genotypic variation on sperm competition gene expression remains poorly understood. Here, we used Drosophila Genetic Reference Panel (DGRP) inbred lines and manipulated developmental population density (i.e. larval density) to test the effects of genotype, environment and genotype-by-environment interactions (GEI) on the expression of the known sperm competition genes Sex Peptide, Acp36DE and CG9997. High larval density resulted in reduced adult body size, but expression of sperm competition genes remained unaffected. Furthermore, we found no significant GEI but genotypic effects in the expression of SP and Acp36DE. Our results also revealed GEI for relative competitive paternity success (second male paternity; P2), with genes' expression positively correlated with P2. Given the effect of genotype on the expression of genes, we conducted a genome-wide association study (GWAS) and identified polymorphisms in putative cis-regulatory elements as predominant factors regulating the expression of SP and Acp36DE. The association of genotypic variation with sperm competition outcomes, and the resilience of sperm competition genes' expression against environmental challenges, demonstrates the importance of genome variation background in reproductive fitness.

Genotype-by-environment and QTL-by-environment interactions in sweet cherry (Prunus avium L.) for flowering date

In sweet cherry (Prunus avium L.), flowering date is strongly dependent on the environment conditions and, therefore, is a trait of major interest for adaptation to climate change. Such trait can be influenced by genotype-by-environment interaction (G×E), that refers to differences in the response of genotypes to different environments. If not taken into account, G×E can reduce selection accuracy and overall genetic gain. However, little is known about G×E in fruit tree species. Flowering date is a highly heritable and polygenic trait for which many quantitative trait loci (QTLs) have been identified. As for the overall genetic performance, differential expression of QTLs in response to environment (QTL-by-environment interaction, QTL×E) can occur. The present study is based on the analysis of a multi-environment trial (MET) suitable for the study of G×E and QTL×E in sweet cherry. It consists of a sweet cherry F₁ full-sib family (n = 121) derived from the cross between cultivars 'Regina' and 'Lapins' and planted in two copies in five locations across four European countries (France, Italy, Slovenia and Spain) covering a large range of climatic conditions. The aim of this work was to study the effect of the environment on flowering date and estimate G×E, to carry QTL detection in different environments in order to study the QTL stability across environments and to estimate QTL×E. A strong effect of the environment on flowering date and its genetic control was highlighted. Two large-effect and environment-specific QTLs with significant QTL×E were identified on linkage groups (LGs) 1 and 4. This work gives new insights into the effect of the environment on a trait of main importance in one of the most economically important fruit crops in temperate regions. Moreover, molecular markers were developed for flowering date and a strategy consisting in using specific markers for warm or cold regions was proposed to optimize marker-assisted selection (MAS) in sweet cherry breeding programs.

Increasing ecological heterogeneity can constrain biopesticide resistance evolution

Microbial biopesticides containing living parasites are valuable emerging crop protection technologies against insect pests, but they are vulnerable to resistance evolution. Fortunately, the fitness of alleles that provide resistance, including to parasites used in biopesticides, frequently depends on parasite identity and environmental conditions. This context-specificity suggests a sustainable approach to biopesticide resistance management through landscape diversification. To mitigate resistance risks, we advocate increasing the range of biopesticides available to farmers, whilst simultaneously encouraging other aspects of landscape-wide crop heterogeneity that can generate variable selection on resistance alleles. This approach requires agricultural stakeholders to prioritize diversity as well as efficiency, both within agricultural landscapes and the biocontrol marketplace.

Construction of a high-density genetic map and dissection of genetic architecture of six agronomic traits in tobacco (Nicotiana tabacum L.)

Tobacco (Nicotiana tabacum L.) is an economic crop and a model organism for studies on plant biology and genetics. A population of 271 recombinant inbred lines (RIL) derived from K326 and Y3, two elite flue-cured tobacco parents, has been constructed to investigate the genetic basis of agronomic traits in tobacco. Six agronomic traits including natural plant height (nPH), natural leaf number (nLN), stem girth (SG), inter-node length (IL), length of the largest leaf (LL) and width of the largest leaf (LW) were measured in seven environments, spanning the period between 2018 and 2021. We firstly developed an integrated SNP-indel-SSR linkage map with 43,301 SNPs, 2,086 indels and 937 SSRs, which contained 7,107 bin markers mapped on 24 LGs and covered 3334.88 cM with an average genetic distance of 0.469cM. Based on this high-density genetic map, a total of 70 novel QTLs were detected for six agronomic traits by a full QTL model using the software QTLNetwork, of which 32 QTLs showed significant additive effects, 18 QTLs showed significant additive-by-environment interaction effects, 17 pairs showed significant additive-by-additive epistatic effects and 13 pairs showed significant epistasis-by-environment interaction effects. In addition to additive effect as a major contributor to genetic variation, both epistasis effects and genotype-by-environment interaction effects played an important role in explaining phenotypic variation for each trait. In particular, qnLN6-1 was detected with considerably large main effect and high heritability ( h a 2 =34.80%). Finally, four genes including Nt16g00284.1, Nt16g00767.1, Nt16g00853.1, Nt16g00877.1 were predicted as pleiotropic candidate genes for five traits.

Effects of environment and genotype on dispersal differ across departure, transfer and settlement in a butterfly metapopulation

Active dispersal is driven by extrinsic and intrinsic factors at the three stages of departure, transfer and settlement. Most empirical studies capture only one stage of this complex process, and knowledge of how much can be generalized from one stage to another remains unknown. Here we use genetic assignment tests to reconstruct dispersal across 5 years and 232 habitat patches of a Glanville fritillary butterfly (Melitaea cinxia) metapopulation. We link individual dispersal events to weather, landscape structure, size and quality of habitat patches, and individual genotype to identify the factors that influence the three stages of dispersal and post-settlement survival. We found that nearly all tested factors strongly affected departure probabilities, but that the same factors explained very little variation in realized dispersal distances. Surprisingly, we found no effect of dispersal distance on post-settlement survival. Rather, survival was influenced by weather conditions, quality of the natal habitat patch, and a strong interaction between genotype and occupancy status of the settled habitat patch, with more mobile genotypes having higher survival as colonists rather than as immigrants. Our work highlights the multi-causality of dispersal and that some dispersal costs can only be understood by considering extrinsic and intrinsic factors and their interaction across the entire dispersal process.

A Method for Identifying Environmental Stimuli and Genes Responsible for Genotype-by-Environment Interactions From a Large-Scale Multi-Environment Data Set

It has not been fully understood in real fields what environment stimuli cause the genotype-by-environment (G × E) interactions, when they occur, and what genes react to them. Large-scale multi-environment data sets are attractive data sources for these purposes because they potentially experienced various environmental conditions. Here we developed a data-driven approach termed Environmental Covariate Search Affecting Genetic Correlations (ECGC) to identify environmental stimuli and genes responsible for the G × E interactions from large-scale multi-environment data sets. ECGC was applied to a soybean (Glycine max) data set that consisted of 25,158 records collected at 52 environments. ECGC illustrated what meteorological factors shaped the G × E interactions in six traits including yield, flowering time, and protein content and when these factors were involved in the interactions. For example, it illustrated the relevance of precipitation around sowing dates and hours of sunshine just before maturity to the interactions observed for yield. Moreover, genome-wide association mapping on the sensitivities to the identified stimuli discovered candidate and known genes responsible for the G × E interactions. Our results demonstrate the capability of data-driven approaches to bring novel insights on the G × E interactions observed in fields.

Environment-specific genomic prediction ability in maize using environmental covariates depends on environmental similarity to training data

Technology advances have made possible the collection of a wealth of genomic, environmental, and phenotypic data for use in plant breeding. Incorporation of environmental data into environment-specific genomic prediction is hindered in part because of inherently high data dimensionality. Computationally efficient approaches to combining genomic and environmental information may facilitate extension of genomic prediction models to new environments and germplasm, and better understanding of genotype-by-environment (G × E) interactions. Using genomic, yield trial, and environmental data on 1,918 unique hybrids evaluated in 59 environments from the maize Genomes to Fields project, we determined that a set of 10,153 SNP dominance coefficients and a 5-day temporal window size for summarizing environmental variables were optimal for genomic prediction using only genetic and environmental main effects. Adding marker-by-environment variable interactions required dimension reduction, and we found that reducing dimensionality of the genetic data while keeping the full set of environmental covariates was best for environment-specific genomic prediction of grain yield, leading to an increase in prediction ability of 2.7% to achieve a prediction ability of 80% across environments when data were masked at random. We then measured how prediction ability within environments was affected under stratified training-testing sets to approximate scenarios commonly encountered by plant breeders, finding that incorporation of marker-by-environment effects improved prediction ability in cases where training and test sets shared environments, but did not improve prediction in new untested environments. The environmental similarity between training and testing sets had a greater impact on the efficacy of prediction than genetic similarity between training and test sets.

Phenotypic Evaluation and Genetic Analysis of Seedling Emergence in a Global Collection of Wheat Genotypes (Triticum aestivum L.) Under Limited Water Availability

The challenge in establishing an early-sown wheat crop in southern Australia is the need for consistently high seedling emergence when sowing deep in subsoil moisture (>10 cm) or into dry top-soil (4 cm). However, the latter is strongly reliant on a minimum soil water availability to ensure successful seedling emergence. This study aimed to: (1) evaluate 233 Australian and selected international wheat genotypes for consistently high seedling emergence under limited soil water availability when sown in 4 cm of top-soil in field and glasshouse (GH) studies; (2) ascertain genetic loci associated with phenotypic variation using a genome-wide association study (GWAS); and (3) compare across loci for traits controlling coleoptile characteristics, germination, dormancy, and pre-harvest sprouting. Despite significant (P < 0.001) environment and genotype-by-environment interactions within and between field and GH experiments, eight genotypes that included five cultivars, two landraces, and one inbred line had consistently high seedling emergence (mean value > 85%) across nine environments. Moreover, 21 environment-specific quantitative trait loci (QTL) were detected in GWAS analysis on chromosomes 1B, 1D, 2B, 3A, 3B, 4A, 4B, 5B, 5D, and 7D, indicating complex genetic inheritance controlling seedling emergence. We aligned QTL for known traits and individual genes onto the reference genome of wheat and identified 16 QTL for seedling emergence in linkage disequilibrium with coleoptile length, width, and cross-sectional area, pre-harvest sprouting and dormancy, germination, seed longevity, and anthocyanin development. Therefore, it appears that seedling emergence is controlled by multifaceted networks of interrelated genes and traits regulated by different environmental cues.

Prediction of Maize Phenotypic Traits With Genomic and Environmental Predictors Using Gradient Boosting Frameworks

The development of crop varieties with stable performance in future environmental conditions represents a critical challenge in the context of climate change. Environmental data collected at the field level, such as soil and climatic information, can be relevant to improve predictive ability in genomic prediction models by describing more precisely genotype-by-environment interactions, which represent a key component of the phenotypic response for complex crop agronomic traits. Modern predictive modeling approaches can efficiently handle various data types and are able to capture complex nonlinear relationships in large datasets. In particular, machine learning techniques have gained substantial interest in recent years. Here we examined the predictive ability of machine learning-based models for two phenotypic traits in maize using data collected by the Maize Genomes to Fields (G2F) Initiative. The data we analyzed consisted of multi-environment trials (METs) dispersed across the United States and Canada from 2014 to 2017. An assortment of soil- and weather-related variables was derived and used in prediction models alongside genotypic data. Linear random effects models were compared to a linear regularized regression method (elastic net) and to two nonlinear gradient boosting methods based on decision tree algorithms (XGBoost, LightGBM). These models were evaluated under four prediction problems: (1) tested and new genotypes in a new year; (2) only unobserved genotypes in a new year; (3) tested and new genotypes in a new site; (4) only unobserved genotypes in a new site. Accuracy in forecasting grain yield performance of new genotypes in a new year was improved by up to 20% over the baseline model by including environmental predictors with gradient boosting methods. For plant height, an enhancement of predictive ability could neither be observed by using machine learning-based methods nor by using detailed environmental information. An investigation of key environmental factors using gradient boosting frameworks also revealed that temperature at flowering stage, frequency and amount of water received during the vegetative and grain filling stage, and soil organic matter content appeared as important predictors for grain yield in our panel of environments.

Genetic and plastic rewiring of food webs under climate change

Climate change is altering ecological and evolutionary processes across biological scales. These simultaneous effects of climate change pose a major challenge for predicting the future state of populations, communities and ecosystems. This challenge is further exacerbated by the current lack of integration of research focused on these different scales.

We propose that integrating the fields of quantitative genetics and food web ecology will reveal new insights on how climate change may reorganize biodiversity across levels of organization. This is because quantitative genetics links the genotypes of individuals to population‐level phenotypic variation due to genetic (G), environmental (E) and gene‐by‐environment (G × E) factors. Food web ecology, on the other hand, links population‐level phenotypes to the structure and dynamics of communities and ecosystems.

We synthesize data and theory across these fields and find evidence that genetic (G) and plastic (E and G × E) phenotypic variation within populations will change in magnitude under new climates in predictable ways. We then show how changes in these sources of phenotypic variation can rewire food webs by altering the number and strength of species interactions, with consequences for ecosystem resilience. We also find evidence suggesting there are predictable asymmetries in genetic and plastic trait variation across trophic levels, which set the pace for phenotypic change and food web responses to climate change. Advances in genomics now make it possible to partition G, E and G × E phenotypic variation in natural populations, allowing tests of the hypotheses we propose.

By synthesizing advances in quantitative genetics and food web ecology, we provide testable predictions for how the structure and dynamics of biodiversity will respond to climate change.

Genotype by environment interactions in coral bleaching

Climate-driven reef decline has prompted the development of next-generation coral conservation strategies, many of which hinge on the movement of adaptive variation across genetic and environmental gradients. This process is limited by our understanding of how genetic and genotypic drivers of coral bleaching will manifest in different environmental conditions. We reciprocally transplanted 10 genotypes of Acropora cervicornis across eight sites along a 60 km span of the Florida Reef Tract and documented significant genotype × environment interactions in bleaching response during the severe 2015 bleaching event. Performance relative to site mean was significantly different between genotypes and can be mostly explained by ensemble models of correlations with genetic markers. The high explanatory power was driven by significant enrichment of loci associated DNA repair, cell signalling and apoptosis. No genotypes performed above (or below) bleaching average at all sites, so genomic predictors can provide practitioners with 'confidence intervals' about the chance of success in novel habitats. These data have important implications for assisted gene flow and managed relocation, and their integration with traditional active restoration.

Plant Genotype Influences Physicochemical Properties of Substrate as Well as Bacterial and Fungal Assemblages in the Rhizosphere of Balsam Poplar

Abandoned unrestored mines are an important environmental concern as they typically remain unvegetated for decades, exposing vast amounts of mine waste to erosion. Several factors limit the revegetation of these sites, including extreme abiotic and unfavorable biotic conditions. However, some pioneer tree species having high levels of genetic diversity, such as balsam poplar (Populus balsamifera), can naturally colonize these sites and initiate plant succession. This suggests that some tree genotypes are likely more suited for acclimation to the conditions of mine wastes. In this study, we selected two contrasting mine waste storage facilities (waste rock from a gold mine and tailings from a molybdenum mine) from the Abitibi region of Quebec (Canada), on which poplars were found to have grown naturally. First, we assessed in situ the impact of vegetation presence on each mine waste type. The presence of balsam poplars improved soil health locally by modifying the physicochemical properties (e.g., higher nutrient content and pH) of the mine wastes and causing an important shift in their bacterial and fungal community compositions, going from lithotrophic communities that dominate mine waste environments to heterotrophic communities involved in nutrient cycling. Next, in a greenhouse experiment we assessed the impact of plant genotype when grown in these mine wastes. Ten genotypes of P. balsamifera were collected locally, found growing either at the mine sites or in the surrounding natural forest. Tree growth was monitored over two growing seasons, after which the effects of genotype-by-environment interactions were assessed by measuring the physicochemical properties of the substrates and the changes in microbial community assembly. Although substrate type was identified as the main driver of rhizosphere microbiome diversity and community structure, a significant effect due to tree genotype was also detected, particularly for bacterial communities. Plant genotype also influenced aboveground tree growth and the physicochemical properties of the substrates. These results highlight the influence of balsam poplar genotype on the soil environment and the potential importance of tree genotype selection in the context of mine waste revegetation.

Crop Mass and N Status as Prerequisite Covariables for Unraveling Nitrogen Use Efficiency across Genotype-by-Environment-by-Management Scenarios: A Review

Due to the asymptotic nature of the crop yield response curve to fertilizer N supply, the nitrogen use efficiency (NUE, yield per unit of fertilizer applied) of crops declines as the crop N nutrition becomes less limiting. Therefore, it is difficult to directly compare the NUE of crops according to genotype-by-environment-by-management interactions in the absence of any indication of crop N status. The determination of the nitrogen nutrition index (NNI) allows the estimation of crop N status independently of the N fertilizer application rate. Moreover, the theory of N dilution in crops indicates clearly that crop N uptake is coregulated by (i) soil N availability and (ii) plant growth rate capacity. Thus, according to genotype-by-environment-by-management interactions leading to variation in potential plant growth capacity, N demand for a given soil N supply condition would be different; consequently, the NUE of the crop would be dissimilar. We demonstrate that NUE depends on the crop potential growth rate and N status defined by the crop NNI. Thus, providing proper context to NUE changes needs to be achieved by considering comparisons with similar crop mass and NNI to avoid any misinterpretation. The latter needs to be considered not only when analyzing genotype-by-environment-by-management interactions for NUE but for other resource use efficiency inputs such as water use efficiency (colimitation N-water) under field conditions.

Phenotypically Selective Genotyping Realizes More Genetic Gains in a Rainbow Trout Breeding Program in the Presence of Genotype-by-Environment Interactions

Selective genotyping of phenotypically superior animals may lead to bias and less accurate genomic breeding values (GEBV). Performing selective genotyping based on phenotypes measured in the breeding environment (B) is not necessarily a good strategy when the aim of a breeding program is to improve animals’ performance in the commercial environment (C). Our simulation study compared different genotyping strategies for selection candidates and for fish in C in a breeding program for rainbow trout in the presence of genotype-by-environment interactions when the program had limited genotyping resources and unregistered pedigrees of individuals. For the reference population, selective genotyping of top and bottom individuals in C based on phenotypes measured in C led to the highest genetic gains, followed by random genotyping and then selective genotyping of top individuals in C. For selection candidates, selective genotyping of top individuals in B based on phenotypes measured in B led to the highest genetic gains, followed by selective genotyping of top and bottom individuals and then random genotyping. Selective genotyping led to bias in predicting GEBV. However, in scenarios that used selective genotyping of top fish in B and random genotyping of fish in C, predictions of GEBV were unbiased, with genetic correlations of 0.2 and 0.5 between traits measured in B and C. Estimates of variance components were sensitive to genotyping strategy, with an overestimation of the variance with selective genotyping of top and bottom fish and an underestimation of the variance with selective genotyping of top fish. Unbiased estimates of variance components were obtained when fish in B and C were genotyped at random. In conclusion, we recommend phenotypic genotyping of top and bottom fish in C and top fish in B for the purpose of selecting breeding animals and random genotyping of individuals in B and C for the purpose of estimating variance components when a genomic breeding program for rainbow trout aims to improve animals’ performance in C.

A Natural Mutational Event Uncovers a Life History Trade-Off via Hormonal Pleiotropy

Environmental signals often control central life history decisions, including the choice between reproduction and somatic maintenance. Such adaptive developmental plasticity occurs in the nematode Caenorhabditis elegans, where environmental cues govern whether larvae will develop directly into reproducing adults or arrest their development to become stress-resistant dauer larvae. Here, we identified a natural variant underlying enhanced sensitivity to dauer-inducing cues in C. elegans: a 92-bp deletion in the cis-regulatory region of the gene eak-3. This deletion reduces synthesis or activity of the steroid hormone dafachronic acid (DA), thereby increasing environmental sensitivity for dauer induction. Consistent with known pleiotropic roles of DA, this eak-3 variant significantly slows down reproductive growth. We experimentally show that, although the eak-3 deletion can provide a fitness advantage through facilitated dauer production in stressful environments, this allele becomes rapidly outcompeted in favorable environments. The identified eak-3 variant therefore reveals a trade-off in how hormonal responses influence both the pace of developmental timing and the way in which environmental sensitivity controls adaptive plasticity. Together, our results show how a single mutational event altering hormonal signaling can lead to the emergence of a complex life history trade-off.

Population variation in early development can determine ecological resilience in response to environmental change

As climate change transforms seasonal patterns of temperature and precipitation, germination success at marginal temperatures will become critical for the long-term persistence of many plant species and communities. If populations vary in their environmental sensitivity to marginal temperatures across a species' geographical range, populations that respond better to future environmental extremes are likely to be critical for maintaining ecological resilience of the species. Using seeds from two to six populations for each of nine species of Mediterranean plants, we characterized patterns of among-population variation in environmental sensitivity by quantifying genotype-by-environment interactions (G × E) for germination success at temperature extremes, and under two light regimes representing conditions below and above the soil surface. For eight of nine species tested at hot and cold marginal temperatures, we observed substantial among-population variation in environmental sensitivity for germination success, and this often depended on the light treatment. Importantly, different populations often performed best at different environmental extremes. Our results demonstrate that ongoing changes in temperature regime will affect the phenology, fitness, and demography of different populations within the same species differently. We show that quantifying patterns of G × E for multiple populations, and understanding how such patterns arise, can test mechanisms that promote ecological resilience.

Environmental variation impacts trait expression and selection in the legume-rhizobium symbiosis

PREMISE

The ecological outcomes of mutualism are well known to shift across abiotic or biotic environments, but few studies have addressed how different environments impact evolutionary responses, including the intensity of selection on and the expression of genetic variance in key mutualism-related traits.

METHODS

We planted 30 maternal lines of the legume Medicago lupulina in four field common gardens and compared our measures of selection on and genetic variance in nodulation, a key trait reflecting legume investment in the symbiosis, with those from a previous greenhouse experiment using the same 30 M. lupulina lines.

RESULTS

We found that both the mean and genetic variance for nodulation were much greater in the greenhouse than in the field and that the form of selection on nodulation significantly differed across environments. We also found significant genotype-by-environment (G × E) effects for fitness-related traits that were generated by differences in the rank order of plant lines among environments.

CONCLUSIONS

Overall, our results suggest that the expression of genotypic variation and selection on nodulation differ across environments. In the field, significant rank-order changes for plant fitness potentially help maintain genetic variation in natural populations, even in the face of directional or stabilizing selection.

Exome sequences and multi-environment field trials elucidate the genetic basis of adaptation in barley

Broadening the genetic base of crops is crucial for developing varieties to respond to global agricultural challenges such as climate change. Here, we analysed a diverse panel of 371 domesticated lines of the model crop barley to explore the genetics of crop adaptation. We first collected exome sequence data and phenotypes of key life history traits from contrasting multi-environment common garden trials. Then we applied refined statistical methods, including some based on exomic haplotype states, for genotype-by-environment (G×E) modelling. Sub-populations defined from exomic profiles were coincident with barley's biology, geography and history, and explained a high proportion of trial phenotypic variance. Clear G×E interactions indicated adaptation profiles that varied for landraces and cultivars. Exploration of circadian clock-related genes, associated with the environmentally adaptive days to heading trait (crucial for the crop's spread from the Fertile Crescent), illustrated complexities in G×E effect directions, and the importance of latitudinally based genic context in the expression of large-effect alleles. Our analysis supports a gene-level scientific understanding of crop adaption and leads to practical opportunities for crop improvement, allowing the prioritisation of genomic regions and particular sets of lines for breeding efforts seeking to cope with climate change and other stresses.

Additive genetic variance for lifetime fitness and the capacity for adaptation in an annual plant

The immediate capacity for adaptation under current environmental conditions is directly proportional to the additive genetic variance for fitness, V_A (W). Mean absolute fitness, W ¯ , is predicted to change at the rate V A ( W ) W ¯ , according to Fisher's Fundamental Theorem of Natural Selection. Despite ample research evaluating degree of local adaptation, direct assessment of V_A (W) and the capacity for ongoing adaptation is exceedingly rare. We estimated V_A (W) and W ¯ in three pedigreed populations of annual Chamaecrista fasciculata, over three years in the wild. Contrasting with common expectations, we found significant V_A (W) in all populations and years, predicting increased mean fitness in subsequent generations (0.83 to 6.12 seeds per individual). Further, we detected two cases predicting "evolutionary rescue," where selection on standing V_A (W) was expected to increase fitness of declining populations ( W ¯ < 1.0) to levels consistent with population sustainability and growth. Within populations, inter-annual differences in genetic expression of fitness were striking. Significant genotype-by-year interactions reflected modest correlations between breeding values across years, indicating temporally variable selection at the genotypic level that could contribute to maintaining V_A (W). By directly estimating V_A (W) and total lifetime W ¯ , our study presents an experimental approach for studies of adaptive capacity in the wild.

Genome structure predicts modular transcriptome responses to genetic and environmental conditions

Understanding the plasticity, robustness and modularity of transcriptome expression to genetic and environmental conditions is crucial to deciphering how organisms adapt in nature. To test how genome architecture influences transcriptome profiles, we quantified expression responses for distinct temperature-adapted genotypes of the nematode Caenorhabditis briggsae when exposed to chronic temperature stresses throughout development. We found that 56% of the 8,795 differentially expressed genes show genotype-specific changes in expression in response to temperature (genotype-by-environment interactions, GxE). Most genotype-specific responses occur under heat stress, indicating that cold vs. heat stress responses involve distinct genomic architectures. The 22 co-expression modules that we identified differ in their enrichment of genes with genetic vs. environmental vs. interaction effects, as well as their genomic spatial distributions, functional attributes and rates of molecular evolution at the sequence level. Genes in modules enriched for simple effects of either genotype or temperature alone tend to evolve especially rapidly, consistent with disproportionate influence of adaptation or weaker constraint on these subsets of loci. Chromosome-scale heterogeneity in nucleotide polymorphism, however, rather than the scale of individual genes predominates as the source of genetic differences among expression profiles, and natural selection regimes are largely decoupled between coding sequences and noncoding flanking sequences that contain cis-regulatory elements. These results illustrate how the form of transcriptome modularity and genome structure contribute to predictable profiles of evolutionary change.

Genetic correlations and their dependence on environmental similarity-Insights from livestock data

Genetic correlations for a trait across environments are predicted to decrease as environments diverge. However, estimates of genetic correlations from natural populations are typically defined across a limited environmental range and prone to very large standard errors, making it difficult to test this prediction. We address the importance of environmental distance on genetic correlations by employing data from domestic cattle in which abundant and accurate estimates are available from a wide range of environments. Three production traits related to milk yield show a clear decrease in genetic correlations with increasing environmental divergence. This pattern was also evident for growth traits and other yield traits but not for traits related to reproduction, morphology, physiology, or disease. We suspect that this reflects weaker selection on these latter trait classes compared to production traits, or alternatively the effects of selection are constrained by unfavorable genetic correlations between traits. The results support the notion that traits that historically have been under strong directional selection in a small range of frequently encountered environments will evolve high genetic correlations across these environments, while exposure to uncommon (and dissimilar) environments lead to a reranking of gene effects and a decrease in genetic correlations across environments.

GENOTYPE-BY-ENVIRONMENT INTERACTIONS IN THE DETERMINATION OF THE SIZE OF A SECONDARY SEXUAL CHARACTER IN THE COLLARED FLYCATCHER (FICEDULA ALBICOLLIS)

Although genetic variation in characters closely related to fitness is expected to either become depleted by selection or masked by environmental variation, "good gene" models of sexual selection require moderate to high heritabilities of secondary sexual characters to explain the occurrence of costly female mate preferences. In this study, I investigated whether the estimated heritability of a condition-dependent secondary sexual character (i.e., the white forehead badge) in the collared flycatcher varied depending on environmental conditions experienced during offspring growth. The data were collected over a period of 14 years making it possible to exploit natural variation in natal conditions. In addition, natal conditions were experimentally altered through brood size manipulations. During unfavorable conditions caused by generally poor weather or experimentally enlarged brood size, no significant heritability based on father-sons regressions could be demonstrated (0.19 ⩽ h² ⩽ 0.27). In contrast, sons reared during years with favorable weather or in experimentally reduced broods significantly resembled their fathers (0.44 ⩽ h² ⩽ 0.65). In addition, the heritability estimates declined with increasing maternal age. The strong effect of natal environmental condition on the estimated heritability of forehead badge size suggests that the potential genetic benefit from mate choice vary according to environmental conditions (e.g., the benefit is reduced during unfavorable rearing conditions). Because sons reared during poor conditions have probably experienced a natal environment different from that experienced by their fathers, the low heritability estimates obtained under poor conditions seem to be caused by low additive genetic variation expressed in such environments and/or a low genetic correlation between the expression of the trait in the two different environments (i.e., good vs. bad). Both of these explanations imply the presence of genotype-by-environment interactions. If such interactions frequently affect the expression of secondary sexual characters, this may offer an explanation of the high heritabilites sometimes reported for such traits, despite their exposure to long-term directional selection.

REPRODUCTIVE CHARACTERISTICS OF THE FLOWER BREEDING DROSOPHILA HIBISCI BOCK (DROSOPHILIDAE) IN EASTERN AUSTRALIA: GENETIC AND ENVIRONMENTAL DETERMINANTS OF OVARIOLE NUMBER

Quantitative genetic analysis of the ovariole number of the Australian Hibiscus flower-breeding Drosophila hibisci Bock was conducted on populations from two localities along a latitudinal cline in ovariole number previously observed in the species (Starmer et al., in press). Parental strains, F₁ , F_1r (reciprocal), F₂ , backcross, and backcross reciprocal generations were used in a line-cross (generation means) analysis. This analysis revealed both additive and epistatic effects as important determinants of variation in ovariole number when larvae were reared at 25°C. Maternal effects and maternal-by-progeny genetic interactions were not significant. These results are comparable to previous studies that document epistatic components as genetic determinants of ovariole number in D. melanogaster. Parallel studies on ovariole number in D. hibisci parental and hybrid generations (F₁ and F_1r ) reared as larvae at three temperatures (18°, 21.5°, and 25°C) showed environmental effects and genotype-by-environment interactions as significant influences on the phenotype. Maternal effects were present when temperature of larval development was considered and significant, nonlinear environmental effects were detected. Field collections of D. hibisci females showed that field conditions result in significant departure of ovariole number from comparable laboratory reared females. The significant epistatic genetic effects, genotype-by-environment interactions, and maternal effects indicate that the genetic architecture of traits, such as ovariole number, may be more complex than often acknowledged and thus may be compatible with Wright's view of a netlike relationship between the genome and complex characters (Wright 1968).

Genotypic Variation in a Breeding Population of Yellow Sweet Clover (Melilotus officinalis)

Yellow sweet clover is a widely spread legume species that has potential to be used as a forage crop in Western China. However, limited information is available on the genetic variation for herbage yield, key morphological traits, and coumarin content. In this study, 40 half sib (HS) families of M. officinalis were evaluated for genotypic variation and phenotypic and genotypic correlation for the traits: LS (leaf to stem ratio), SV (spring vigor), LA (leaf area), PH (plant height), DW (herbage dry weight), SD (stem diameter), SN (stem number), Cou (coumarin content), SY (seed yield), across two locations, Yuzhong and Linze, in Western China. There was significant (P < 0.05) genotypic variation among the HS families for all traits. There was also significant (P < 0.05) genotype-by-environment interaction for the traits DW, PH, SD, SN, and SV. The estimates of HS family mean repeatability across two locations ranged from 0.32 for SN to 0.89 for LA. Pattern analysis generated four HS family groups where group 3 consisted of families with above average expression for DW and below average expression for Cou. The breeding population developed by polycrossing the selected HS families within group 3 will provide a significant breeding pool for M. officinalis cultivar development in China.

Adaptive Landscapes of Resistance Genes Change as Antibiotic Concentrations Change

Most studies on the evolution of antibiotic resistance are focused on selection for resistance at lethal antibiotic concentrations, which has allowed the detection of mutant strains that show strong phenotypic traits. However, solely focusing on lethal concentrations of antibiotics narrowly limits our perspective of antibiotic resistance evolution. New high-resolution competition assays have shown that resistant bacteria are selected at relatively low concentrations of antibiotics. This finding is important because sublethal concentrations of antibiotics are found widely in patients undergoing antibiotic therapies, and in nonmedical conditions such as wastewater treatment plants, and food and water used in agriculture and farming. To understand the impacts of sublethal concentrations on selection, we measured 30 adaptive landscapes for a set of TEM β-lactamases containing all combinations of the four amino acid substitutions that exist in TEM-50 for 15 β-lactam antibiotics at multiple concentrations. We found that there are many evolutionary pathways within this collection of landscapes that lead to nearly every TEM-genotype that we studied. While it is known that the pathways change depending on the type of β-lactam, this study demonstrates that the landscapes including fitness optima also change dramatically as the concentrations of antibiotics change. Based on these results we conclude that the presence of multiple concentrations of β-lactams in an environment result in many different adaptive landscapes through which pathways to nearly every genotype are available. Ultimately this may increase the diversity of genotypes in microbial populations.

Workshop report: Caenorhabditis nematodes as model organisms to study trait variation and its evolution

A fundamental problem in biology is to understand how genome expression translates into variation in molecular, cellular, developmental, physiological, behavioral, or life-history traits. During the summer of 2014, worm biologists with a keen interest in evolutionary biology and natural ecology met in Les Treilles (France) to define the problems of trait variation better and to discuss empirical approaches using Caenorhabditis species to address these problems. Compared with other model organisms, Caenorhabditis has several advantages, such as well-defined traits that can be subjected to highly controlled environmental and genetic manipulation and the possibility for long-term experimental evolution that can be coupled with genome-wide mapping of trait variation. The Les Treilles workshop brought together researchers studying the evolution of phenotypic plasticity, gene-networks, genome structure and population genetics, sex-determination and development in the laboratory, behavior and the life-history of natural Caenorhabditis populations. Here, we outline the key aims of this workshop and summarize the contributions of each participant.

Host ecotype generates evolutionary and epidemiological divergence across a pathogen metapopulation

The extent and speed at which pathogens adapt to host resistance varies considerably. This presents a challenge for predicting when--and where--pathogen evolution may occur. While gene flow and spatially heterogeneous environments are recognized to be critical for the evolutionary potential of pathogen populations, we lack an understanding of how the two jointly shape coevolutionary trajectories between hosts and pathogens. The rust pathogen Melampsora lini infects two ecotypes of its host plant Linum marginale that occur in close proximity yet in distinct populations and habitats. In this study, we found that within-population epidemics were different between the two habitats. We then tested for pathogen local adaptation at host population and ecotype level in a reciprocal inoculation study. Even after controlling for the effect of spatial structure on infection outcome, we found strong evidence of pathogen adaptation at the host ecotype level. Moreover, sequence analysis of two pathogen infectivity loci revealed strong genetic differentiation by host ecotype but not by distance. Hence, environmental variation can be a key determinant of pathogen population genetic structure and coevolutionary dynamics and can generate strong asymmetry in infection risks through space.

Genomic selection accuracies within and between environments and small breeding groups in white spruce

BACKGROUND

Genomic selection (GS) may improve selection response over conventional pedigree-based selection if markers capture more detailed information than pedigrees in recently domesticated tree species and/or make it more cost effective. Genomic prediction accuracies using 1748 trees and 6932 SNPs representative of as many distinct gene loci were determined for growth and wood traits in white spruce, within and between environments and breeding groups (BG), each with an effective size of Ne ≈ 20. Marker subsets were also tested.

RESULTS

Model fits and/or cross-validation (CV) prediction accuracies for ridge regression (RR) and the least absolute shrinkage and selection operator models approached those of pedigree-based models. With strong relatedness between CV sets, prediction accuracies for RR within environment and BG were high for wood (r = 0.71-0.79) and moderately high for growth (r = 0.52-0.69) traits, in line with trends in heritabilities. For both classes of traits, these accuracies achieved between 83% and 92% of those obtained with phenotypes and pedigree information. Prediction into untested environments remained moderately high for wood (r ≥ 0.61) but dropped significantly for growth (r ≥ 0.24) traits, emphasizing the need to phenotype in all test environments and model genotype-by-environment interactions for growth traits. Removing relatedness between CV sets sharply decreased prediction accuracies for all traits and subpopulations, falling near zero between BGs with no known shared ancestry. For marker subsets, similar patterns were observed but with lower prediction accuracies.

CONCLUSIONS

Given the need for high relatedness between CV sets to obtain good prediction accuracies, we recommend to build GS models for prediction within the same breeding population only. Breeding groups could be merged to build genomic prediction models as long as the total effective population size does not exceed 50 individuals in order to obtain high prediction accuracy such as that obtained in the present study. A number of markers limited to a few hundred would not negatively impact prediction accuracies, but these could decrease more rapidly over generations. The most promising short-term approach for genomic selection would likely be the selection of superior individuals within large full-sib families vegetatively propagated to implement multiclonal forestry.

Beyond the single gene: How epistasis and gene-by-environment effects influence crop domestication

Domestication is a multifaceted evolutionary process, involving changes in individual genes, genetic interactions, and emergent phenotypes. There has been extensive discussion of the phenotypic characteristics of plant domestication, and recent research has started to identify the specific genes and mutational mechanisms that control domestication traits. However, there is an apparent disconnect between the simple genetic architecture described for many crop domestication traits, which should facilitate rapid phenotypic change under selection, and the slow rate of change reported from the archeobotanical record. A possible explanation involves the middle ground between individual genetic changes and their expression during development, where gene-by-gene (epistatic) and gene-by-environment interactions can modify the expression of phenotypes and opportunities for selection. These aspects of genetic architecture have the potential to significantly slow the speed of phenotypic evolution during crop domestication and improvement. Here we examine whether epistatic and gene-by-environment interactions have shaped how domestication traits have evolved. We review available evidence from the literature, and we analyze two domestication-related traits, shattering and flowering time, in a mapping population derived from a cross between domesticated foxtail millet and its wild progenitor. We find that compared with wild progenitor alleles, those favored during domestication often have large phenotypic effects and are relatively insensitive to genetic background and environmental effects. Consistent selection should thus be able to rapidly change traits during domestication. We conclude that if phenotypic evolution was slow during crop domestication, this is more likely due to cultural or historical factors than epistatic or environmental constraints.

Ecological and evolutionary implications of spatial heterogeneity during the off-season for a wild plant pathogen

While recent studies have elucidated many of the factors driving parasite dynamics during the growing season, the ecological and evolutionary dynamics during the off-season (i.e. the period between growing seasons) remain largely unexplored. We combined large-scale surveys and detailed experiments to investigate the overwintering success of the specialist plant pathogen Podosphaera plantaginis on its patchily distributed host plant Plantago lanceolata in the Åland Islands. Twelve years of epidemiological data establish the off-season as a crucial stage in pathogen metapopulation dynamics, with c. 40% of the populations going extinct during the off-season. At the end of the growing season, we observed environmentally mediated variation in the production of resting structures, with major consequences for spring infection at spatial scales ranging from single individuals to populations within a metapopulation. Reciprocal transplant experiments further demonstrated that pathogen population of origin and overwintering site jointly shaped infection intensity in spring, with a weak signal of parasite adaptation to the local off-season environment. We conclude that environmentally mediated changes in the distribution and evolution of parasites during the off-season are crucial for our understanding of host-parasite dynamics, with applied implications for combating parasites and diseases in agriculture, wildlife and human disease systems.

Genetic background and GxE interactions modulate the penetrance of a naturally occurring wing mutation in Drosophila melanogaster

Many genes involved in producing complex traits are incompletely penetrant. One such example is vesiculated, an X-linked gene in Drosophila melanogaster that results in wing defects. To examine the genetic architecture of a complex trait (wings containing vesicles), we placed a naturally occurring variant into multiple autosomal backgrounds and quantified penetrance and expressivity at a range of developmental temperatures. We found significant epistasis, genotype-by-environment interactions, and maternal effects. Sex and temperature effects were modulated by genetic background. The severity of wing phenotypes also varied across different genetic backgrounds, and expressivity was positively correlated with penetrance. We also found evidence of naturally segregating suppressors of vesiculated. These suppressors were present on both the second and third chromosomes, and complex interactions were observed. Taken together, these findings indicate that multiple genetic and environmental factors modulate the phenotypic effects of a naturally occurring vesiculated allele.