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Multi-environment trials are routinely conducted in plant breeding to select candidates for the next selection cycle. In this study, we compare the prediction accuracy of four developed genomic-enabled prediction models: (1) single-environment, main genotypic effect model (SM); (2) multi-environment, main genotypic effects model (MM); (3) multi-environment, single variance G×E deviation model (MDs); and (4) multi-environment, environment-specific variance G×E deviation model (MDe). Each of these four models were fitted using two kernel methods: a linear kernel Genomic Best Linear Unbiased Predictor, GBLUP (GB), and a nonlinear kernel Gaussian kernel (GK). The eight model-method combinations were applied to two extensive Brazilian maize data sets (HEL and USP data sets), having different numbers of maize hybrids evaluated in different environments for grain yield (GY), plant height (PH), and ear height (EH). Results show that the MDe and the MDs models fitted with the Gaussian kernel (MDe-GK, and MDs-GK) had the highest prediction accuracy. For GY in the HEL data set, the increase in prediction accuracy of SM-GK over SM-GB ranged from 9 to 32%. For the MM, MDs, and MDe models, the increase in prediction accuracy of GK over GB ranged from 9 to 49%. For GY in the USP data set, the increase in prediction accuracy of SM-GK over SM-GB ranged from 0 to 7%. For the MM, MDs, and MDe models, the increase in prediction accuracy of GK over GB ranged from 34 to 70%. For traits PH and EH, gains in prediction accuracy of models with GK compared to models with GB were smaller than those achieved in GY. Also, these gains in prediction accuracy decreased when a more difficult prediction problem was studied.

An enduring question in plant physiology and evolution is how single genotypes of plants optimize performance in diverse, often highly variable, environments. We grew 35 natural accessions of the grass Brachypodium distachyon in four environments in the glasshouse, contrasting soil water deficit, elevated temperature and their interaction. We modeled treatment, genotype and interactive effects on leaf-level and whole-plant traits, including fecundity. We also assessed the relationship between glasshouse-measured traits and parameters related to climate at the place of origin. We found abundant genetic variation in both constitutive and induced traits related to plant–water relations. Most traits showed strong interaction between temperature and water availability, and we observed genotype-by-environment interaction for several traits. Notably, leaf free proline abundance showed a strong effect of genotype × temperature × water. We found strong associations between phenology, biomass and water use efficiency (WUE) with parameters describing climate of origin. Plants respond to multiple stressors in ways not directly predictable from single stressors, underscoring the complex and trait-specific mechanisms of environmental response. Climate–trait correlations support a role for WUE and phenology in local adaptation to climate in B. distachyon.

Evaluation of maize varieties under multiple environments, including drought and low nitrogen (N) stressed sites is an important breeding approach, to identify well adapted and stable maize varieties. This study was undertaken to identify new quality protein maize (QPM) hybrids that have good agronomic performance and assess the presence of genotype by environment (G × E) interaction and grain yield stability of QPM hybrids under different environment conditions. Forty-five hybrids, including two QPM, two non-QPM and one local check were evaluated across 34 environments under stress and non-stress conditions in Ethiopia, Zimbabwe, Zambia, Mozambique, and Malawi during 2018 to 2020. Additive Main Effects and Multiplicative Interaction (AMMI) and Genotype main effects plus G × E interaction (GGE) bi-plots were used for stability analysis. Environment, genotype and G × E interaction effects were significant for grain yield and other traits in all management conditions. The top yielding hybrids were 44 (QS7646) 12 (CZH15099Q) under optimum; 14 (CZH15142Q), 44 (QS7646) and 23 (CZH17192Q) under random stress; 9 (CZH142237Q) and 10 (CZH142238Q) under managed drought; and 14 (CZH15142Q) and 34 (CZH17203Q) under low N conditions. Among these, 10 (CZH142238Q) and 14 (CZH15142Q) were the most stable hybrids and can be recommended for release in sub-Saharan Africa to improve food and nutritional security of smallholder farmers who depend on maize. Kwekwe (KWE), Bindura (BIN), Chokwe (CHO) and Bako (BK2) were identified as the most discriminating and representative for optimum, random stress, managed drought and low N environments, respectively and help to identify superior hybrids.

Abstract We formalize “enviromic markers” as modeling units parallel to DNA markers, but herein for genotype-environment (G × E) prediction. Four operational premises (linearity; site potential; heterogeneous favorability; and envirotypic covariates (ECs)-genotype-dependence) are presented to enable their use in linear mixed models and also to motivate four construction strategies: (i) using raw environmental covariates as linear markers; (ii) applying transformations to capture mild nonlinearities; (iii) deriving ecophysiological functions; and (iv) engineering markers with Artificial Intelligence (AI) models which learn nonlinear environment → phenotype mappings for linear downstream use. Environmental data quality control is detailed, including checks of spatial coverage and resolution, variance within the TPE, collinearity control, and spatial/temporal validation without leakage. Envirome data are linked with GIS to compute environmental kernels, quantify covariate shifts, and deliver pixel-level predictions with uncertainty diagnostics. The framework clarifies assumptions and standardizes the use of enviromic markers for predictive breeding analyses.

Dispersal is one of the most fundamental components of ecology, and affects processes as diverse as population growth, metapopulation dynamics, gene flow and adaptation. Although the act of moving from one habitat to another entails major costs to the disperser, empirical and theoretical studies suggest that these costs can be reduced by having morphological, physiological or behavioural specializations for dispersal. A few recent studies on different systems showed that individuals exhibit personality-dependent dispersal, meaning that dispersal tendency is associated with boldness, sociability or aggressiveness. Indeed, in several species, dispersers not only develop behavioural differences at the onset of dispersal, but display these behavioural characteristics through their life cycle. While personality-dependent dispersal has been demonstrated in only a few species, we believe that it is a widespread phenomenon with important ecological consequences. Here, we review the evidence for behavioural differences between dispersers and residents, to what extent they constitute personalities. We also examine how a link between personality traits and dispersal behaviours can be produced and how personality-dependent dispersal affects the dynamics of metapopulations and biological invasions. Finally, we suggest future research directions for population biologists, behavioural ecologists and conservation biologists such as how the direction and the strength of the relationship between personality traits and dispersal vary with ecological contexts.

Adverse childhood experiences have been described as one of the major environmental risk factors for depressive disorder. Similarly, the deleterious impact of early traumatic experiences on depression seems to be moderated by individual genetic variability. Serotonin transporter (5-HTT) and brain-derived neurotrophic factor (BDNF) modulate the effect of childhood adversity on adult depression, although inconsistencies across studies have been found. Moreover, the gene x environment (GxE) interaction concerning the different types of childhood adversity remains poorly understood. The aim of this study was to analyse the putative interaction between the 5-HTT gene (5-HTTLPR polymorphism), the BDNF gene (Val66Met polymorphism) and childhood adversity in accounting for adult depressive symptoms. A sample of 534 healthy individuals filled in self-report questionnaires of depressive symptomatology [the Symptom Check List 90 Revised (SCL-90-R)] and different types of childhood adversities [the Childhood Trauma Questionnaire (CTQ)]. The 5-HTTLPR polymorphism (5-HTT gene) and the Val66Met polymorphism (BDNF gene) were genotyped in the whole sample. Total childhood adversity (beta=0.27, p<0.001), childhood sexual abuse (CSA; beta=0.17, p<0.001), childhood emotional abuse (beta=0.27, p<0.001) and childhood emotional neglect (beta=0.22, p<0.001) had an impact on adult depressive symptoms. CSA had a greater impact on depressive symptoms in Met allele carriers of the BDNF gene than in the Val/Val group (F=5.87, p<0.0001), and in S carriers of the 5-HTTLPR polymorphism (5-HTT gene) (F=5.80, p<0.0001). Childhood adversity per se predicted higher levels of adult depressive symptoms. In addition, BDNF Val66Met and 5-HTTLPR polymorphisms seemed to moderate the effect of CSA on adult depressive symptoms.

Background On-going global climate change poses a serious threat for natural populations unless they are able to evolutionarily adapt to changing environmental conditions (e.g. increasing average temperatures, occurrence of extreme weather events). A prerequisite for evolutionary change is within-population heritable genetic variation in traits subject to selection. In relation to climate change, mainly phenological traits as well as heat and desiccation resistance have been examined for such variation. Therefore, it is important to investigate adaptive potential under climate change conditions across a broader range of traits. This is especially true for life-history traits and defences against natural enemies (e.g. parasites) since they influence organisms’ fitness both directly and through species interactions. We examined the adaptive potential of fitness-related traits and their responses to heat waves in a population of a freshwater snail, Lymnaea stagnalis . We estimated family-level variation and covariation in life history (size, reproduction) and constitutive immune defence traits [haemocyte concentration, phenoloxidase (PO)-like activity, antibacterial activity of haemolymph] in snails experimentally exposed to typical (15 °C) and heat wave (25 °C) temperatures. We also assessed variation in the reaction norms of these traits between the treatments. Results We found that at the heat wave temperature, snails were larger and reproduced more, while their immune defence was reduced. Snails showed high family-level variation in all examined traits within both temperature treatments. The only negative genetic correlation (between reproduction and antibacterial activity) appeared at the high temperature. However, we found no family-level variation in the responses of most examined traits to the experimental heat wave (i.e. largely parallel reaction norms between the treatments). Only the reduction of PO-like activity when exposed to the high temperature showed family-level variation, suggesting that the cost of heat waves may be lower for some families and could evolve under selection. Conclusion Our results suggest that there is genetic potential for adaptation within both thermal environments and that trait evolution may not be strongly affected by trade-offs between them. However, rare differences in thermal reaction norms across families indicate limited evolutionary potential in the responses of snails to changing temperatures during extreme weather events.

The common bean is a tropical facultative short-day legume that is now grown in tropical and temperate zones. This observation underscores how domestication and modern breeding can change the adaptive phenology of a species. A key adaptive trait is the optimal timing of the transition from the vegetative to the reproductive stage. This trait is responsive to genetically controlled signal transduction pathways and local climatic cues. A comprehensive characterization of this trait can be started by assessing the quantitative contribution of the genetic and environmental factors, and their interactions. This study aimed to locate significant QTL (G) and environmental (E) factors controlling time-to-flower in the common bean, and to identify and measure G × E interactions. Phenotypic data were collected from a biparental [Andean × Mesoamerican] recombinant inbred population (F11:14, 188 genotypes) grown at five environmentally distinct sites. QTL analysis using a dense linkage map revealed 12 QTL, five of which showed significant interactions with the environment. Dissection of G × E interactions using a linear mixed-effect model revealed that temperature, solar radiation, and photoperiod play major roles in controlling common bean flowering time directly, and indirectly by modifying the effect of certain QTL. The model predicts flowering time across five sites with an adjusted r-square of 0.89 and root-mean square error of 2.52 d. The model provides the means to disentangle the environmental dependencies of complex traits, and presents an opportunity to identify in silico QTL allele combinations that could yield desired phenotypes under different climatic conditions.

The high potential fitness benefit of phenotypic plasticity tempts us to expect phenotypic plasticity as a frequent adaptation to environmental heterogeneity. Examples of proven adaptive plasticity in plants, however, are scarce and most plastic responses actually may be 'passive' rather than adaptive. This suggests that frequently requirements for the evolution of adaptive plasticity are not met or that such evolution is impeded by constraints. Here we outline requirements and potential constraints for the evolution of adaptive phenotypic plasticity, identify open questions, and propose new research approaches. Important open questions concern the genetic background of plasticity, genetic variation in plasticity, selection for plasticity in natural habitats, and the nature and occurrence of costs and limits of plasticity. Especially promising tools to address these questions are selection gradient analysis, meta-analysis of studies on genotype-by-environment interactions, QTL analysis, cDNA-microarray scanning and quantitative PCR to quantify gene expression, and two-dimensional gel electrophoresis to quantify protein expression. Studying plasticity along the pathway from gene expression to the phenotype and its relationship with fitness will help us to better understand why adaptive plasticity is not more universal, and to more realistically predict the evolution of plastic responses to environmental change.

The differential susceptibility theory states that children differ in their susceptibility towards environmental experiences, partially due to plasticity genes. Individuals carrying specific variants in such genes will be more disadvantaged in negative but, conversely, more advantaged in positive environments. Understanding gene–environment interactions may help unravel the causal mechanisms involved in multifactorial psychiatric disorders such as Attention-Deficit/Hyperactivity Disorder (ADHD). The differential susceptibility theory was examined by investigating the presence of interaction effects between maternal expressed emotion (EE; warmth and criticism) and the solitary and combined effects of plasticity genes ( DAT1, DRD4, 5 - HTT ) on prosocial and antisocial behaviour (measured with parent- and self-reports) in children with ADHD and their siblings ( N  = 366, M  = 17.11 years, 74.9 % male). Maternal warmth was positively associated with prosocial behaviour and negatively with antisocial behaviour, while maternal criticism was positively associated with antisocial behaviour and negatively with prosocial behaviour. No evidence of differential susceptibility was found. The current study found no evidence for differential susceptibility based on the selected plasticity genes, in spite of strong EE–behaviour associations. It is likely that additional factors play a role in the complex relationship between genes, environment and behaviour.