The effects of genotype-by-environment interactions on body condition score across three winter supplemental feed environments in a composite beef cattle breed in Montana

Cattle operations in the Northern Great Plains region of the United States face extreme cold weather conditions and require nutritional supplementation over the winter season in order for animals to maintain body condition. In cow-calf operations, body condition scores (BCS) measured at calving and breeding have been shown to be associated with several economically important health and fertility traits, so maintenance of BCS is both an animal welfare and economic concern. A low-to-medium heritability has been found for BCS when measured across various production stages, indicating a large environmental influence but sufficient genetic basis for selection. The present study evaluated BCS measured prior to calving (late winter) and breeding (early summer) under three winter supplementation environments in a multitrait linear mixed model. Traits were discretized by winter supplementation and genetic correlations between environments were considered a reflection of evidence for genotype-by-environment interactions between BCS and diet. Winter supplementation treatments were fed October through April and varied by range access and protein content: 1) feedlot environment with approximately 15% crude protein (CP) corn/silage diet, 2) native rangeland access with 1.8 kg of an 18% CP pellet supplement, and 3) native rangeland access with a self-fed 50% CP and mineral supplement. A total of 2,988 and 2,353 records were collected across multiple parities on 1,010 and 800 individuals for prebreeding and precalving BCS, respectively. Heifers and cows came from a composite beef cattle breed developed and maintained by the USDA Fort Keogh Livestock and Range Research Laboratory near Miles City, Montana. Genetic correlations between treatments 1 and 2, 1 and 3, and 2 and 3 were 0.98, 0.78, and 0.65 and 1.00, 0.98, and 0.99 for precalving and prebreeding BCS, respectively. This provides moderate evidence of genotype-by-environment interactions for precalving BCS under treatment 3 relative to treatments 1 and 2, but no evidence for genotype-by-environment interactions for prebreeding BCS. Treatment 3 differed substantially in CP content relative to treatments 1 and 2, indicating that some animals differ in their ability to maintain BCS up to spring calving across a protein gradient. These results indicate the potential for selection of animals with increased resilience under cold weather conditions and high protein, restricted energy diets to maintain BCS.

Pervasive genotype-by-environment interactions shape the fitness effects of antibiotic resistance mutations

The fitness effects of antibiotic resistance mutations are a major driver of resistance evolution. While the nutrient environment affects bacterial fitness, experimental studies of resistance typically measure fitness of mutants in a single environment only. We explored how the nutrient environment affected the fitness effects of rifampicin-resistant rpoB mutations in Escherichia coli under several conditions critical for the emergence and spread of resistance-the presence of primary or secondary antibiotic, or the absence of any antibiotic. Pervasive genotype-by-environment (GxE) interactions determined fitness in all experimental conditions, with rank order of fitness in the presence and absence of antibiotics being strongly dependent on the nutrient environment. GxE interactions also affected the magnitude and direction of collateral effects of secondary antibiotics, in some cases so drastically that a mutant that was highly sensitive in one nutrient environment exhibited cross-resistance to the same antibiotic in another. It is likely that the mutant-specific impact of rpoB mutations on the global transcriptome underpins the observed GxE interactions. The pervasive, mutant-specific GxE interactions highlight the importance of doing what is rarely done when studying the evolution and spread of resistance in experimental and clinical work: assessing fitness of antibiotic-resistant mutants across a range of relevant environments.

Linking genetic and environmental factors through marker effect networks to understand trait plasticity

Understanding how plants adapt to specific environmental changes and identifying genetic markers associated with phenotypic plasticity can help breeders develop plant varieties adapted to a rapidly changing climate. Here, we propose the use of marker effect networks as a novel method to identify markers associated with environmental adaptability. These marker effect networks are built by adapting commonly used software for building gene coexpression networks with marker effects across growth environments as the input data into the networks. To demonstrate the utility of these networks, we built networks from the marker effects of ∼2,000 nonredundant markers from 400 maize hybrids across 9 environments. We demonstrate that networks can be generated using this approach, and that the markers that are covarying are rarely in linkage disequilibrium, thus representing higher biological relevance. Multiple covarying marker modules associated with different weather factors throughout the growing season were identified within the marker effect networks. Finally, a factorial test of analysis parameters demonstrated that marker effect networks are relatively robust to these options, with high overlap in modules associated with the same weather factors across analysis parameters. This novel application of network analysis provides unique insights into phenotypic plasticity and specific environmental factors that modulate the genome.

Efficient Genomic Prediction of Yield and Dry Matter in Hybrid Potato

There is an ongoing endeavor within the potato breeding sector to rapidly adapt potato from a clonal polyploid crop to a diploid hybrid potato crop. While hybrid breeding allows for the efficient generation and selection of parental lines, it also increases breeding program complexity and results in longer breeding cycles. Over the past two decades, genomic prediction has revolutionized hybrid crop breeding through shorter breeding cycles, lower phenotyping costs, and better population improvement, resulting in increased genetic gains for genetically complex traits. In order to accelerate the genetic gains in hybrid potato, the proper implementation of genomic prediction is a crucial milestone in the rapid improvement of this crop. The authors of this paper set out to test genomic prediction in hybrid potato using current genotyped material with two alternative models: one model that predicts the general combining ability effects (GCA) and another which predicts both the general and specific combining ability effects (GCA+SCA). Using a training set comprising 769 hybrids and 456 genotyped parental lines, we found that reasonable a prediction accuracy could be achieved for most phenotypes with both zero common parents (ρ=0.36-0.61) and one (ρ=0.50-0.68) common parent between the training and test sets. There was no benefit with the inclusion of non-additive genetic effects in the GCA+SCA model despite SCA variance contributing between 9% and 19% of the total genetic variance. Genotype-by-environment interactions, while present, did not appear to affect the prediction accuracy, though prediction errors did vary across the trial's targets. These results suggest that genomically estimated breeding values on parental lines are sufficient for hybrid yield prediction.

Genomic selection for genotype performance and environmental stability in Coffea canephora

Coffee is one of the most important beverages and trade products in the world. Among the multiple research initiatives focused on coffee sustainability, plant breeding provides the best means to increase phenotypic performance and release cultivars that could meet market demands. Since coffee is so well adapted to a diversity of tropical environments, an important question for those confronting the problem of evaluating phenotypic performance is the relevance of genotype-by-environment (GEI) interaction. As a perennial crop with a long juvenile phase, coffee is subjected to significant temporal and spatial variations. Such facts not only hinder the selection of promising materials, but also cause a majority of complaints among growers. In this study, we hypothesized that trait stability in coffee is genetically controlled, and therefore is predictable using molecular information. To test it, we used genome-based methods to predict stability metrics computed with the primary goal of selecting coffee genotypes that combine high phenotypic performance and stability for target environments. Using two populations of Coffea canephora, evaluated across multiple years and locations, our contribution is three-fold: (i) first, we demonstrated that the number of harvest evaluations may be reduced leading to accelerated implementation of molecular breeding; (ii) we showed that stability metrics are predictable; and finally, (iii) both stable and high-performance genotypes can be simultaneously predicted and selected. While this research was carried out on representative environments for coffee production with substantial crossover in genotypic ranking, we anticipate that genomic prediction can be an efficient tool to select coffee genotypes that combine high performance and stability across years and the target locations here evaluated.

Genetic Dissection of Phosphorus Use Efficiency and Genotype-by-Environment Interaction in Maize

Genotype-by-environment interaction (G-by-E) is a common but potentially problematic phenomenon in plant breeding. In this study, we investigated the genotypic performance and two measures of plasticity on a phenotypic and genetic level by assessing 234 maize doubled haploid lines from six populations for 15 traits in seven macro-environments with a focus on varying soil phosphorus levels. It was found intergenic regions contributed the most to the variation of phenotypic linear plasticity. For 15 traits, 124 and 31 quantitative trait loci (QTL) were identified for genotypic performance and phenotypic plasticity, respectively. Further, some genes associated with phosphorus use efficiency, such as Zm00001eb117170, Zm00001eb258520, and Zm00001eb265410, encode small ubiquitin-like modifier E3 ligase were identified. By significantly testing the main effect and G-by-E effect, 38 main QTL and 17 interaction QTL were identified, respectively, in which MQTL38 contained the gene Zm00001eb374120, and its effect was related to phosphorus concentration in the soil, the lower the concentration, the greater the effect. Differences in the size and sign of the QTL effect in multiple environments could account for G-by-E. At last, the superiority of G-by-E in genomic selection was observed. In summary, our findings will provide theoretical guidance for breeding P-efficient and broadly adaptable varieties.

Adaptive plasticity in plant traits increases time to hydraulic failure under drought in a foundation tree

The viability of forest trees, in response to climate change-associated drought, will depend on their capacity to survive through genetic adaptation and phenotypic plasticity in drought tolerance traits. Genotypes with enhanced plasticity for drought tolerance (adaptive plasticity) will have a greater ability to persist and delay the onset of hydraulic failure. By examining populations from different climate-origins grown under contrasting soil water availability, we tested for genotype (G), environment (E) and genotype-by-environment (G × E) effects on traits that determine the time it takes for saplings to desiccate from stomatal closure to 88% loss of stem hydraulic conductance (time to hydraulic failure, THF). Specifically, we hypothesized that: (i) THF is dependent on a G × E interaction, with longer THF for warm, dry climate populations in response to chronic water deficit treatment compared with cool, wet populations, and (ii) hydraulic and allometric traits explain the observed patterns in THF. Corymbia calophylla saplings from two populations originating from contrasting climates (warm-dry or cool-wet) were grown under well-watered and chronic soil water deficit treatments in large containers. Hydraulic and allometric traits were measured and then saplings were dried-down to critical levels of drought stress to estimate THF. Significant plasticity was detected in the warm-dry population in response to water-deficit, with enhanced drought tolerance compared with the cool-wet population. Projected leaf area and total plant water storage showed treatment variation, and minimum conductance showed significant population differences driving longer THF in trees from warm-dry origins grown in water-limited conditions. Our findings contribute information on intraspecific variation in key drought traits, including hydraulic and allometric determinants of THF. It highlights the need to quantify adaptive capacity in populations of forest trees in climate change-type drought to improve predictions of forest die-back.

Herd solutions from genetic evaluations can be used as a tool to rescale the expected expression of genetic potential in cattle

The objective of this study was to determine whether response to selection for carcass weight (CW), conformation (CC) and fat (CF), and the association between heterosis and carcass performance varied by herd environment in cattle. Following edits, carcass information was available for 4,616,761 cattle, of which the majority were some crossbred combination of the following breeds: Angus, Aubrac, Belgian Blue, Blonde d'Aquitaine, Charolais, Hereford, Holstein-Friesian, Jersey, Limousin, Saler, Shorthorn and Simmental. Herd environment was defined separately for each carcass trait using herd solutions outputted from carcass trait genetic evaluations. A total of 3,859 herds were stratified, for each trait, into one of five strata based on their corresponding percentile herd solution rank, with the response to selection and the effect of heterosis then estimated within each stratum. The response in CW and CC from selection on the respective estimated breeding values (EBV) increased between the lowest (0.71 kg and 0.89 CC score increase per unit increase in the respective EBV) and highest (0.99 kg and 1.25 CC score increase per unit increase in the respective EBV) corresponding herd stratum. The response in CF from selection on CF EBV, however, reduced between the lowest and highest CF herd stratum (respective increases of 0.93 and 0.83 CF scores per unit increase in CF EBV). In addition, the effect of a unit increase in heterosis coefficient on CW, CC and CF also varied by herd stratum. Furthermore, results (i.e. the area under relative operating characteristic curves) from the present study demonstrated that the response to selection and heterosis effects estimated for the different herd stratum can be used, along with EBVs and the herd solutions themselves, to improve the accuracy of phenotypic predictions. Results from the present study could help producers to make more informed breeding decisions that are bespoke to their herd.

Sowing Date Affects the Timing and Duration of Key Chickpea (Cicer arietinum L.) Growth Phases

Chickpea is the main legume rotation crop within farming systems in northern New South Wales (NSW), Australia, and is grown mainly under rainfed conditions. Recent expansion of chickpea growing areas in southern and central western NSW expose them to abiotic stresses; however, knowledge about how these stresses affect overall crop development is limited. This study aimed to examine the influence of sowing time on the timing and duration of key chickpea phenological growth phases in southern and central western environments of NSW. Experiments were conducted over two years in southern NSW (Leeton, Wagga Wagga and Yanco (one year)) and central western NSW (Trangie) to identify phenology responses. Climatic, phenology and experimental site data was recorded, and the duration of growth phases and growing degree days calculated. Early sowing (mid-April) generally delayed flowering, extending the crop’s vegetative period, and the progressive delay in sowing resulted in shorter vegetative and podding growth phases. All genotypes showed photoperiod sensitivity, and the mean daily temperature at sowing influenced time to emergence and to some extent crop establishment. This study concludes that environmental factors such as temperature, moisture availability and day length are the main drivers of phenological development in chickpea.

Genomic Selection Using Pedigree and Marker-by-Environment Interaction for Barley Seed Quality Traits From Two Commercial Breeding Programs

With the current advances in the development of low-cost high-density array-based DNA marker technologies, cereal breeding programs are increasingly relying on genomic selection as a tool to accelerate the rate of genetic gain in seed quality traits. Different sources of genetic information are being explored, with the most prevalent being combined additive information from marker and pedigree-based data, and their interaction with the environment. In this, there has been mixed evidence on the performance of use of these data. This study undertook an extensive analysis of 907 elite winter barley (Hordeum vulgare L.) lines across multiple environments from two breeding companies. Six genomic prediction models were evaluated to demonstrate the effect of using pedigree and marker information individually and in combination, as well their interactions with the environment. Each model was evaluated using three cross-validation schemes that allows the prediction of newly developed lines (lines that have not been evaluated in any environment), prediction of new or unobserved years, and prediction of newly developed lines in unobserved years. The results showed that the best prediction model depends on the cross-validation scheme employed. In predicting newly developed lines in known environments, marker information had no advantage to pedigree information. Predictions in this scenario also benefited from including genotype-by-environment interaction in the models. However, when predicting lines and years not observed previously, marker information was superior to pedigree data. Nonetheless, such scenarios did not benefit from the addition of genotype-by-environment interaction. A combination of pedigree-based and marker-based information produced a similar or only marginal improvement in prediction ability. It was also discovered that combining populations from the different breeding programs to increase training population size had no advantage in prediction.

How to analyse plant phenotypic plasticity in response to a changing climate

Contents Summary 1235 I. Introduction 1235 II. The many shapes of phenotypic plasticity 1236 III. Random regression mixed model framework 1237 IV. Conclusions 1240 Acknowledgements 1240 References 1240 SUMMARY: Plant biology is experiencing a renewed interest in the mechanistic underpinnings and evolution of phenotypic plasticity that calls for a re-evaluation of how we analyse phenotypic responses to a rapidly changing climate. We suggest that dissecting plant plasticity in response to increasing temperature needs an approach that can represent plasticity over multiple environments, and considers both population-level responses and the variation between genotypes in their response. Here, we outline how a random regression mixed model framework can be applied to plastic traits that show linear or nonlinear responses to temperature. Random regressions provide a powerful and efficient means of characterising plasticity and its variation. Although they have been used widely in other fields, they have only recently been implemented in plant evolutionary ecology. We outline their structure and provide an example tutorial of their implementation.

Review: Deciphering animal robustness. A synthesis to facilitate its use in livestock breeding and management

As the environments in which livestock are reared become more variable, animal robustness becomes an increasingly valuable attribute. Consequently, there is increasing focus on managing and breeding for it. However, robustness is a difficult phenotype to properly characterise because it is a complex trait composed of multiple components, including dynamic elements such as the rates of response to, and recovery from, environmental perturbations. In this review, the following definition of robustness is used: the ability, in the face of environmental constraints, to carry on doing the various things that the animal needs to do to favour its future ability to reproduce. The different elements of this definition are discussed to provide a clearer understanding of the components of robustness. The implications for quantifying robustness are that there is no single measure of robustness but rather that it is the combination of multiple and interacting component mechanisms whose relative value is context dependent. This context encompasses both the prevailing environment and the prevailing selection pressure. One key issue for measuring robustness is to be clear on the use to which the robustness measurements will employed. If the purpose is to identify biomarkers that may be useful for molecular phenotyping or genotyping, the measurements should focus on the physiological mechanisms underlying robustness. However, if the purpose of measuring robustness is to quantify the extent to which animals can adapt to limiting conditions then the measurements should focus on the life functions, the trade-offs between them and the animal's capacity to increase resource acquisition. The time-related aspect of robustness also has important implications. Single time-point measurements are of limited value because they do not permit measurement of responses to (and recovery from) environmental perturbations. The exception being single measurements of the accumulated consequence of a good (or bad) adaptive capacity, such as productive longevity and lifetime efficiency. In contrast, repeated measurements over time have a high potential for quantification of the animal's ability to cope with environmental challenges. Thus, we should be able to quantify differences in adaptive capacity from the data that are increasingly becoming available with the deployment of automated monitoring technology on farm. The challenge for future management and breeding will be how to combine various proxy measures to obtain reliable estimates of robustness components in large populations. A key aspect for achieving this is to define phenotypes from consideration of their biological properties and not just from available measures.

Systems genomics of metabolic phenotypes in wild-type Drosophila melanogaster

Systems biology is an approach to dissection of complex traits that explicitly recognizes the impact of genetic, physiological, and environmental interactions in the generation of phenotypic variation. We describe comprehensive transcriptional and metabolic profiling in Drosophila melanogaster across four diets, finding little overlap in modular architecture. Genotype and genotype-by-diet interactions are a major component of transcriptional variation (24 and 5.3% of the total variation, respectively) while there were no main effects of diet (<1%). Genotype was also a major contributor to metabolomic variation (16%), but in contrast to the transcriptome, diet had a large effect (9%) and the interaction effect was minor (2%) for the metabolome. Yet specific principal components of these molecular phenotypes measured in larvae are strongly correlated with particular metabolic syndrome-like phenotypes such as pupal weight, larval sugar content and triglyceride content, development time, and cardiac arrhythmia in adults. The second principal component of the metabolomic profile is especially informative across these traits with glycine identified as a key loading variable. To further relate this physiological variability to genotypic polymorphism, we performed evolve-and-resequence experiments, finding rapid and replicated changes in gene frequency across hundreds of loci that are specific to each diet. Adaptation to diet is thus highly polygenic. However, loci differentially transcribed across diet or previously identified by RNAi knockdown or expression QTL analysis were not the loci responding to dietary selection. Therefore, loci that respond to the selective pressures of diet cannot be readily predicted a priori from functional analyses.

Epistasis between mutations is host-dependent for an RNA virus

How, and to what extent, does the environment influence the way mutations interact? Do environmental changes affect both the sign and the magnitude of epistasis? Are there any correlations between environments in the variability, sign or magnitude of epistasis? Very few studies have tackled these questions. Here, we addressed them in the context of viral emergence. Most emerging viruses are RNA viruses with small genomes, overlapping reading frames and multifunctional proteins for which epistasis is abundant. Understanding the effect of host species in the sign and magnitude of epistasis will provide insights into the evolutionary ecology of infectious diseases and the predictability of viral emergence.