GGE biplot analysis for cane yield and sugar yield in advanced clones of sugarcane (Saccharum sp. complex).

The present investigation was taken up to study the G × E interaction and stability in 14 sugarcane clones during 2020-2021 and 2021-2022 at four different locations namely Pantnagar, Kashipur, Dhanauri (Haridwar), and Dhakrani (Dehradun) for cane yield (CY) and sugar yield (SY) at the 10-month and 12-month stages. The research aimed to identify stable, high-yielding sugarcane clones adaptable to diverse environmental conditions, enhancing productivity and profitability for farmers in Uttarakhand, India. The combined ANOVA revealed significant differences among the clones (22.20% to 29.54% variation), environments (35% to 39.62% variation), and their interactions (19.91% to 24.16% variation) for CY and SY at both stages. To analyze the stability of genotypes and G × E interactions, the GGE biplot method was employed. The first two PCs explained 77.94% for CY, 74.39% for SY at the 10-month stage, and 81.01% for SY at 12-month stage of the total variation of the GGE model. The GGE biplots revealed that for CY, the mega-environment exhibited CoPant 16222 and CoPant 16223 as the winning genotypes. For SY at the 10-month stage, CoPant 17221 and CoPant 16222 were the best clones in two different mega-environments, while at the 12-month stage, the mega-environment showed CoPant 16222 and CoPant 16223 as the winning genotypes. Dehradun (2020) and Kashipur (2020) were identified as the best test environments for selecting widely and specifically adapted genotypes, respectively, for CY and SY at the 10-month as well as 12-month stages. In a nutshell, GGE biplot analysis identified the best-performing sugarcane clones and best test environments in Uttarakhand, India. Clone CoPant 16222 showed high mean performance and stability for cane and sugar yield, making it suitable for recommendation to farmers.

Interaction of childhood trauma with BDNF and FKBP5 gene polymorphisms in predicting burnout in general occupational groups.

Both the BDNF gene rs6265 and the FKBP5 gene rs1360780 polymorphisms are independently associated with adult psychotic-like experiences, when exposed to high childhood abuse; however, it remains unclear whether the relationship between childhood abuse and burnout is moderated by these two single nucleotide polymorphisms (SNPs). Furthermore, there is an interaction between glucocorticoid receptor transcriptional activity and BDNF signaling. Therefore, we investigated the interaction of these two SNPs with childhood trauma in predicting burnout. We recruited 990 participants (mean age 33.06 years, S.D. = 6.31) from general occupational groups and genotyped them for rs6265 and rs1360780. Burnout, childhood trauma, resilience, and job stress were measured through a series of rating scales. Gene-by-environment and gene-by-gene-by-environment interactions were examined using linear hierarchical regression and PROCESS macro in SPSS. Covariates included demographics and resilience. We found that rs6265 moderated the association between job stress and emotional exhaustion. Both rs6265 and rs1360780 moderated the association between childhood abuse and cynicism. There was significant interaction of childhood abuse × rs6265 × rs1360780 on emotional exhaustion and reduced personal accomplishment, so that rs6265 CC genotype and rs1360780 TT genotype together predicted higher levels of emotional exhaustion under high childhood abuse, while rs6265 TT genotype and rs1360780 CC genotype together exerted a resilient effect on reduced personal accomplishment in the face of childhood abuse. Our findings suggest that the rs6265 CC genotype and rs1360780 TT genotype may jointly contribute to increased risk of burnout under childhood trauma.

Stability Analysis and Identification of Superior Hybrids in Pearl Millet [Pennisetum glaucum (L.) R. Br.] Using the Multi Trait Stability Index.

Pearl millet stands as an important staple food and feed for arid and semi-arid regions of India and South Africa. It is also a quick supplier of important micronutrients like Fe and Zn via grain to combat micronutrient deficiencies among people in developing countries. India has notably spearheaded advancements in pearl millet production and productivity through the All India Coordinated Pearl Millet Improvement Project. There were 21 hybrids evaluated over arid and semi-arid ecologies of the western and southern regions of India. AMMI and GGE biplot models were adopted to recommend a specific hybrid for the particular locality. A joint analysis of variation indicated a significant genotype-environment interaction for most of the agronomical and grain micronutrient parameters. Pearson's correlation values dissected the significant and positive correlation among agronomic traits and the negative correlation with grain micronutrient traits. GGE biplot analysis recommended the SHT 106 as a dual-purpose hybrid and SHT 115 as a biofortified hybrid for the grain's Fe and Zn content. SHT 110 and SHT 108 were selected as stable and high grain yield-producing hybrids across all environments and specifically for E1, E2, and E4 as per the Which-Won-Where and What biplot. SHT 109 and SHT 103 hybrids were stable and high dry fodder yield-producing hybrids across all environments. In this study, the Multi-Trait Stability Index (MTSI) was employed to select the most stable and high-performing hybrids for all traits. It selected SHT 120, SHT 106, and SHT 104 for stability and great performance across all environments. These findings underscored the significance of tailored hybrid recommendations and the potential of pearl millet in addressing both food security and malnutrition challenges in various agro-ecological regions.

Country-wide, multi-location trials of Green Super Rice lines for yield performance and stability analysis using genetic and stability parameters.

Rice (Oryza sativa L.) is an important member of the family Poaceae and more than half of world population depend for their dietary nutrition on rice. Rice cultivars with higher yield, resilience to stress and wider adaptability are essential to ensure production stability and food security. The fundamental objective of this study was to identify higher-yielding rice genotypes with stable performance and wider adaptability in a rice growing areas of Pakistan. A triplicate RCBD design experiment with 20 Green Super Rice (GSR) advanced lines was conducted at 12 rice growing ecologies in four Provinces of Pakistan. Grain yield stability performance was assessed by using different univariate and multivariate statistics. Analysis of variance revealed significant differences among genotypes, locations, and G x E interaction for mean squares (p < 0.05) of major yield contributing traits. All the studied traits except for number of tillers per plant revealed higher genotypic variance than environmental variance. Broad sense heritability was estimated in the range of 44.36% to 98.60%. Based on ASV, ASI, bi, Wi2, σ2i and WAAS statistics, the genotypes G1, G4, G5, G8, G11 and G12 revealed lowest values for parametric statistics and considered more stable genotypes based on  paddy yield. The additive main effects and multiplicative interaction (AMMI) model revealed significant variation (p < 0.05) for genotypes, non-signification for environment and highly significant for G × E interaction. The variation proportion of PC1 and PC2 from interaction revealed 67.2% variability for paddy yield. Based on 'mean verses stability analysis of GGE biplot', 'Which-won-where' GGE Biplot, 'discriminativeness vs. representativeness' pattern of stability, 'IPCA and WAASB/GY' ratio-based stability Heat-map, and ranking of genotypes, the genotypes G1, G2, G3, G5, G8, G10, G11 and G13 were observed ideal genotypes with yield potential more than 8 tons ha-1. Discriminativeness vs. representativeness' pattern of stability identifies two environments, E5 (D.I Khan, KPK) and E6 (Usta Muhammad, Baluchistan) were best suited for evaluating genotypic yield performance. Based on these findings we have concluded that the genotypes G1, G2, G3, G5, G8, G10, G11 and G13 could be included in the commercial varietal development process and future breeding program.

Evolutionary divergence of developmental plasticity and learning of mating tactics in Trinidadian guppies.

Behavioural plasticity is a major driver in the early stages of adaptation, but its effects in mediating evolution remain elusive because behavioural plasticity itself can evolve. In this study, we investigated how male Trinidadian guppies (Poecilia reticulata) adapted to different predation regimes diverged in behavioural plasticity of their mating tactic. We reared F2 juveniles of high- or low-predation population origins with different combinations of social and predator cues and assayed their mating behaviour upon sexual maturity. High-predation males learned their mating tactic from conspecific adults as juveniles, while low-predation males did not. High-predation males increased courtship when exposed to chemical predator cues during development; low-predation males decreased courtship in response to immediate chemical predator cues, but only when they were not exposed to such cues during development. Behavioural changes induced by predator cues were associated with developmental plasticity in brain morphology, but changes acquired through social learning were not. We thus show that guppy populations diverged in their response to social and ecological cues during development, and correlational evidence suggests that different cues can shape the same behaviour via different neural mechanisms. Our study demonstrates that behavioural plasticity, both environmentally induced and socially learnt, evolves rapidly and shapes adaptation when organisms colonize ecologically divergent habitats.

La plasticidad conductual es un factor importante en las primeras fases de adaptación, pero se conocen poco sus efectos sobre la evolución porque la plasticidad conductual en sí puede evolucionar. En este estudio, investigamos cómo los machos del guppy de Trinidad (Poecilia reticulata) adaptados a regímenes de depredación diferentes, han divergido en la plasticidad de su táctica de apareamiento. Criamos juveniles provenientes de poblaciones de alta y baja depredación hasta segunda generación (F2) bajo diferentes combinaciones de señales sociales y de depredación, y evaluamos su comportamiento de apareamiento al llegar a la madurez sexual. Los machos de alta depredación aprendieron su táctica de apareamiento de sus conespecíficos adultos, mientras que los machos de baja depredación no. Los machos de alta depredación aumentaron su cortejo al ser expuestos a señales de depredadores durante su desarrollo; mientras que los machos de baja depredación redujeron su cortejo en respuesta a señales inmediatas de depredadores, pero tan solo cuando no fueron expuestos a tales señales durante el desarrollo. Los cambios conductuales observados inducidos por las señales de depredación están asociados con una plasticidad en el desarrollo de la morfología cerebral, pero los cambios adquiridos por aprendizaje social no. En conclusión, demostramos que las poblaciones de guppy han divergido en su respuesta a señales sociales y ecológicas durante su desarrollo, y mostramos evidencia correlativa que sugiere que diferentes tipos de señales pueden influenciar el mismo comportamiento via mecanismos neuronales diferentes. Nuestro estudio muestra que la plasticidad conductual, tanto inducida por el medio ambiente combo aprendida socialmente, evoluciona rápidamente e influencia la adaptación durante la colonización de hábitats ecológicamente divergentes.

Assessment of yield performances for grain sorghum varieties by AMMI and GGE biplot analyses.

Grain sorghum is an exceptional source of dietary nutrition with outstanding economic values. Breeding of grain sorghum can be slowed down by the occurrence of genotype × environment interactions (GEI) causing biased estimation of yield performance in multi-environments and therefore complicates direct phenotypic selection of superior genotypes. Multi-environment trials by randomized complete block design with three replications were performed on 13 newly developed grain sorghum varieties at seven test locations across China for two years. Additive main effects and multiplicative interaction (AMMI) and genotype + genotype × environment (GGE) biplot models were adopted to uncover GEI patterns and effectively identify high-yielding genotypes with stable performance across environments. Yield (YLD), plant height (PH), days to maturity (DTM), thousand seed weight (TSW), and panicle length (PL) were measured. Statistical analysis showed that target traits were influenced by significant GEI effects (p < 0.001), that broad-sense heritability estimates for these traits varied from 0.40 to 0.94 within the medium to high range, that AMMI and GGE biplot models captured more than 66.3% of total variance suggesting sufficient applicability of both analytic models, and that two genotypes, G3 (Liaoza No.52) and G10 (Jinza 110), were identified as the superior varieties while one genotype, G11 (Jinza 111), was the locally adapted variety. G3 was the most stable variety with highest yielding potential and G10 was second to G3 in average yield and stability whereas G11 had best adaptation only in one test location. We recommend G3 and G10 for the production in Shenyang, Chaoyang, Jinzhou, Jinzhong, Yulin, and Pingliang, while G11 for Yili.

Environment-by-PGS Interaction in the Classical Twin Design: An Application to Childhood Anxiety and Negative Affect.

One type of genotype-environment interaction occurs when genetic effects on a phenotype are moderated by an environment; or when environmental effects on a phenotype are moderated by genes. Here we outline these types of genotype-environment interaction models, and propose a test of genotype-environment interaction based on the classical twin design, which includes observed genetic variables (polygenic scores: PGSs) that account for part of the genetic variance of the phenotype. We introduce environment-by-PGS interaction and the results of a simulation study to address statistical power and parameter recovery. Next, we apply the model to empirical data on anxiety and negative affect in children. The power to detect environment-by-PGS interaction depends on the heritability of the phenotype, and the strength of the PGS. The simulation results indicate that under realistic conditions of sample size, heritability and strength of the interaction, the environment-by-PGS model is a viable approach to detect genotype-environment interaction. In 7-year-old children, we defined two PGS based on the largest genetic association studies for 2 traits that are genetically correlated to childhood anxiety and negative affect, namely major depression (MDD) and intelligence (IQ). We find that common environmental influences on negative affect are amplified for children with a lower IQ-PGS.

Yield stability and economic heterosis analysis in newly bred sunflower hybrids throughout diverse agro-ecological zones.

Exploration of heterosis is a strategy for enhancing sunflower yield and productivity. In India, the greatest constraints on sunflower production are stagnant and inconsistent yields. By raising them in a variety of ecological conditions, stable per-se performance with the highest yielding potential sunflower hybrids were selected. Sustainable agriculture requires the use of desirable hybrids with high seed yields and oil content too. By making three distinct crossing sets from 32 sunflower genotypes, 11 cytoplasmic male sterility (CMS), and 21 restorer lines, a total of 124 hybrids were developed (comprising both lines and tester). After extensive field evaluation of all hybrids, only eight superior F1s belonging to all three sets, as well as the three national control hybrids KBSH-53, LSFH-171, and DRSH-1, were selected for stability analysis in four agro-ecological regions of West Bengal, India viz., Nimpith, Baruipur, Bankura, and Berhapore. The genetic stability of several phenotypic characters was assessed using statistical models that examine genotype-environment interaction (G × E) in multi-locational yield trials. In this experiment, the performance of hybrids under various environmental circumstances over two-year periods was measured using regression coefficient (bi) and deviations from regression (S2di). With the exception of genotypes CMS-852A × EC-601751 for volume weight (0.9335) and CMS-302A × EC-623011 for head diameter (0.0905) and volume weight (0.6425), all sunflower genotypes for all concerned traits had extremely minor and negligible deviations from regression (S2di), which showed significant values. The genotypes having insignificant values of S2di were more stable. The economic heterosis of these novel hybrids was also quantified. CMS-302A × EC-623011 in which seed yield was recorded 20.90, 20.91, 20.95 and 20.90% higher than DRSH-1 at Nimpith, Baruipur, Bankura and PORS (Berhampur), respectively. The research revealed that CMS-302A × EC-623011, CMS-853A × EC-623027 and P-2-7-1A × EC-512682 exhibited good seed production and stability for critical agronomic parameters in addition to oil content. As a result, the current researches enlighten to find out how stable the expression of important economic traits in sunflower hybrids is.

Genotype × Environment Interaction and Stability Analysis of Commercial Hybrid Grain Corn Genotypes in Different Environments.

The introduction of superior grain corn genotypes with high and stable yield (YLD) in most environments is important to increase local production and reduce dependency on imported grain corn. In this study, days to tasseling (DT), plant height, and YLD of 11 grain corn genotypes were observed in 10 environments to evaluate the effects of genotype (G), environment (E), and genotype by environment interactions (GEI) using GGE analysis and the stability of genotypes using stability parameters. In each location, grain corn genotypes were arranged in three replications using a randomized complete block design. An analysis of variance showed that all three traits were highly significant toward G and E factors, whereas GEI showed that only DT and YLD were highly significant. Genotype V14 produced the highest YLD of 10,354 kg/ha, followed by V4 (10,114 kg/ha) and V2 (9797.74 kg/ha). These three genotypes also dominated in seven out of 10 tested environments. With regard to stability ranking, genotype V4 was the most stable genotype, with a big gap difference between the second (V14) and third places (V2). Therefore, V14, V4, and V2 were the most promising genotypes because of their great YLD performance and most stable across tested environments, which can be recommended to farmers for high-scale planting.

Multi-location evaluation of mungbean (Vigna radiata L.) in Indian climates: Ecophenological dynamics, yield relation, and characterization of locations.

Crop yield varies considerably within agroecology depending on the genetic potential of crop cultivars and various edaphic and climatic variables. Understanding site-specific changes in crop yield and genotype × environment interaction are crucial and needs exceptional consideration in strategic breeding programs. Further, genotypic response to diverse agro-ecologies offers identification of strategic locations for evaluating traits of interest to strengthen and accelerate the national variety release program. In this study, multi-location field trial data have been used to investigate the impact of environmental conditions on crop phenological dynamics and their influence on the yield of mungbean in different agroecological regions of the Indian subcontinent. The present attempt is also intended to identify the strategic location(s) favoring higher yield and distinctiveness within mungbean genotypes. In the field trial, a total of 34 different mungbean genotypes were grown in 39 locations covering the north hill zone (n = 4), northeastern plain zone (n = 6), northwestern plain zone (n = 7), central zone (n = 11) and south zone (n = 11). The results revealed that the effect of the environment was prominent on both the phenological dynamics and productivity of the mungbean. Noticeable variations (expressed as coefficient of variation) were observed for the parameters of days to 50% flowering (13%), days to maturity (12%), reproductive period (21%), grain yield (33%), and 1000-grain weight (14%) across the environments. The genotype, environment, and genotype × environment accounted for 3.0, 54.2, and 29.7% of the total variation in mungbean yield, respectively (p < 0.001), suggesting an oversized significance of site-specific responses of the genotypes. Results demonstrated that a lower ambient temperature extended both flowering time and the crop period. Linear mixed model results revealed that the changes in phenological events (days to 50 % flowering, days to maturity, and reproductive period) with response to contrasting environments had no direct influence on crop yields (p > 0.05) for all the genotypes except PM 14-11. Results revealed that the south zone environment initiated early flowering and an extended reproductive period, thus sustaining yield with good seed size. While in low rainfall areas viz., Sriganganagar, New Delhi, Durgapura, and Sagar, the yield was comparatively low irrespective of genotypes. Correlation results and PCA indicated that rainfall during the crop season and relative humidity significantly and positively influenced grain yield. Hence, the present study suggests that the yield potential of mungbean is independent of crop phenological dynamics; rather, climatic variables like rainfall and relative humidity have considerable influence on yield. Further, HA-GGE biplot analysis identified Sagar, New Delhi, Sriganganagar, Durgapura, Warangal, Srinagar, Kanpur, and Mohanpur as the ideal testing environments, which demonstrated high efficiency in the selection of new genotypes with wider adaptability.

Ascertaining yield and grain protein content stability in wheat genotypes having the Gpc-B1 gene using univariate, multivariate, and correlation analysis.

The high performance and stability of wheat genotypes for yield, grain protein content (GPC), and other desirable traits are critical for varietal development and food and nutritional security. Likewise, the genotype by environment (G × E) interaction (GEI) should be thoroughly investigated and favorably utilized whenever genotype selection decisions are made. The present study was planned with the following two major objectives: 1) determination of GEI for some advanced wheat genotypes across four locations (Ludhiana, Ballowal, Patiala, and Bathinda) of Punjab, India; and 2) selection of the best genotypes with high GPC and yield in various environments. Different univariate [Eberhart and Ruessll's models; Perkins and Jinks' models; Wrike's Ecovalence; and Francis and Kannenberg's models], multivariate (AMMI and GGE biplot), and correlation analyses were used to interpret the data from the multi-environmental trial (MET). Consequently, both the univariate and multivariate analyses provided almost similar results regarding the top-performing and stable genotypes. The analysis of variance revealed that variation due to environment, genotype, and GEI was highly significant at the 0.01 and 0.001 levels of significance for all studied traits. The days to flowering, plant height, spikelets per spike, grain per spike, days to maturity, and 1000-grain weight were specifically affected by the environment, whereas yield was mainly affected by the environment and GEI. Genotypes, on the other hand, had a greater impact on the GPC than environmental conditions. As a result, a multi-environmental investigation was necessary to identify the GEI for wheat genotype selection because the GEI was very significant for all of the evaluated traits. Yield, 1000-grain weight, spikelet per spike, and days to maturity were observed to have positive correlations, implying the feasibility of their simultaneous selection for yield enhancement. However, GPC was observed to have a negative correlation with yield. Patiala was found to be the most discriminating environment for both yield and GPC and also the most effective representative environment for GPC, whereas Ludhiana was found to be the most effective representative environment for yield. Eventually, two NILs (BWL7508, and BWL7511) were selected as the top across all environments for both yield and GPC.

The Evaluation of Common Bean (Phaseolus vulgaris L.) Genotypes under Water Stress Based on Physiological and Agronomic Parameters.

Drought affects common bean productivity, and the severity of its impact is expected to increase due to climate change. The use of versatile genotypes could contribute to securing future bean production. This study investigates the adaptability of 10 common bean genotypes of indeterminate growth type under water scarcity conditions by measuring agronomic and physiological parameters. The evaluation occurs under irrigation treatments applied at two different phenological stages (anthesis (WDA) and seed filling initiation (WDSF)). The recorded adaptabilities of the genotypes (G) showed that G10 produced the highest overall seed yield in the normal irrigation (NI) (197.22 g plant-1) and WDA (192.78 g plant-1), while the G6 had the highest yield at WDSF (196.71 g plant-1). For the genotype's average mean, chlorophyll content decreased by 10.5% under drought at WDSF. Net photosynthetic rate (Pn), stomatal conductance (gs), and transpiration rate (E) were reduced at WDA by 53%, 80.8%, and 61.4% and at WDSF by 43.75%, 57.7%, and 36%, respectively, while relative water content (RWC) reduced by 16.48%, on average, for both stages. G10 and G6 showed adaptability when water scarcity occurred at an early (WDA) or later stage (WDSF), respectively, providing insights into using germplasm resources to cope with the drought effect.

G × EBLUP: A novel method for exploring genotype by environment interactions and genomic prediction.

Genotype by environment (G × E) interaction is fundamental in the biology of complex traits and diseases. However, most of the existing methods for genomic prediction tend to ignore G × E interaction (GEI). In this study, we proposed the genomic prediction method G × EBLUP by considering GEI. Meanwhile, G × EBLUP can also detect the genome-wide single nucleotide polymorphisms (SNPs) subject to GEI. Using comprehensive simulations and analysis of real data from pigs and maize, we showed that G × EBLUP achieved higher efficiency in mapping GEI SNPs and higher prediction accuracy than the existing methods, and its superiority was more obvious when the GEI variance was large. For pig and maize real data, compared with GBLUP, G × EBLUP showed improvement by 3% in the prediction accuracy for backfat thickness, while our findings indicated that the trait of days to 100 kg of pig was not affected by GEI and G × EBLUP did not improve the accuracy of genomic prediction for the trait. A significant advantage was observed for G × EBLUP in maize; the prediction accuracy was improved by ∼5.0 and 7.7% for grain weight and water content, respectively. Furthermore, G × EBLUP was not influenced by the number of environment levels. It could determine a favourable environment using SNP Bayes factors for each environment, implying that it is a robust and useful method for market-specific animal and plant breeding. We proposed G × EBLUP, a novel method for the estimation of genomic breeding value by considering GEI. This method identified the genome-wide SNPs that were susceptible to GEI and yielded higher genomic prediction accuracies and lower mean squared error compared with the GBLUP method.

Identification of a SGCD × Discrimination Interaction Effect on Systolic Blood Pressure in African American Adults in the Jackson Heart Study.

BACKGROUND: In the United States, hypertension disproportionately afflicts over half of African American adults, many of whom also experience racial discrimination. Understanding gene × discrimination effects may help explain racial disparities in hypertension. METHODS: We tested for the main effects and interactive effects of 5 candidate single nucleotide polymorphisms (SNPs: rs2116737, rs11190458, rs2445762, rs2597955, and rs2416545) and experiences of discrimination on blood pressure (BP) in African Americans not taking antihypertensive medications in the Jackson Heart Study from Mississippi (n = 2,933). Multiple linear regression models assumed an additive genetic model and adjusted for ancestry, age, sex, body mass index, education, and relatedness. We additionally tested recessive and dominant genetic models. RESULTS: Discrimination was significantly associated with higher diastolic BP (P = 0.003). In contrast, there were no main effects of any SNP on BP. When analyzing SNPs and discrimination together, SGCD (Sarcoglycan Delta; rs2116737) demonstrated a gene × environment interaction. Specifically, an SGCD × Discrimination interaction was associated with systolic BP (ß =1.95, P = 0.00028) in a recessive model. Participants carrying a T allele, regardless of discrimination experiences, and participants with a GG genotype and high experiences of discrimination had higher systolic BP than participants with a GG genotype and low experiences of discrimination. This finding suggests the SGCD GG genotype may have a protective effect on systolic BP, but only in a setting of low discrimination. CONCLUSIONS: The inclusion of culturally relevant stressors, like discrimination, may be important to understand the gene-environment interplay likely underlying complex diseases with racial health inequities.

Genomic Prediction Accuracy of Stripe Rust in Six Spring Wheat Populations by Modeling Genotype by Environment Interaction.

Some previous studies have assessed the predictive ability of genome-wide selection on stripe (yellow) rust resistance in wheat, but the effect of genotype by environment interaction (GEI) in prediction accuracies has not been well studied in diverse genetic backgrounds. Here, we compared the predictive ability of a model based on phenotypic data only (M1), the main effect of phenotype and molecular markers (M2), and a model that incorporated GEI (M3) using three cross-validations (CV1, CV2, and CV0) scenarios of interest to breeders in six spring wheat populations. Each population was evaluated at three to eight field nurseries and genotyped with either the DArTseq technology or the wheat 90K single nucleotide polymorphism arrays, of which a subset of 1,058- 23,795 polymorphic markers were used for the analyses. In the CV1 scenario, the mean prediction accuracies of the M1, M2, and M3 models across the six populations varied from -0.11 to -0.07, from 0.22 to 0.49, and from 0.19 to 0.48, respectively. Mean accuracies obtained using the M3 model in the CV1 scenario were significantly greater than the M2 model in two populations, the same in three populations, and smaller in one population. In both the CV2 and CV0 scenarios, the mean prediction accuracies of the three models varied from 0.53 to 0.84 and were not significantly different in all populations, except the Attila/CDC Go in the CV2, where the M3 model gave greater accuracy than both the M1 and M2 models. Overall, the M3 model increased prediction accuracies in some populations by up to 12.4% and decreased accuracy in others by up to 17.4%, demonstrating inconsistent results among genetic backgrounds that require considering each population separately. This is the first comprehensive genome-wide prediction study that investigated details of the effect of GEI on stripe rust resistance across diverse spring wheat populations.

Phenotypic Trait Subdivision Provides New Sight Into the Directional Improvement of Eucommia ulmoides Oliver.

Eucommia ulmoides Oliver has been used extensively in many fields. To satisfy increasing demand, great efforts must be made to further improve its traits. However, limited information is available on these traits, which is a factor that restricts their improvement. To improve traits directionally, nine clones were assigned to six sites to analyze the effect of different variation sources (the genotype, site, and genotype × environment interaction) on the phenotypic trait. In addition, a mixed linear model was used to assess the contribution of variations. In general, for most traits, the site effect accounted for a larger proportion of the variance, followed by the genotype and genotype × environment interaction effects. All the studied genotypes and sites had a significant effect, indicating that they could be improved by selecting preferable genotypes or cultivation areas, respectively. Interestingly, growth traits or economic traits could be improved simultaneously. Trait performance and stability are necessary when selecting genotypes. Moreover, the discriminating ability of genotypes should be considered in selecting cultivation areas. Annual mean temperature and annual sunshine duration proved to be crucial factors that affected the traits. They were correlated positively with economic traits and leaf yield and correlated negatively with growth traits. These findings contributed to selecting a wider range of cultivation areas. Regarding the genotype × environment interaction effect, there were significant differences only in the gutta-percha content, the total number of leaves, and the chlorogenic acid content. These traits could also be improved by choosing appropriate genotypes for the local environment. The research has provided preliminary data on the main factors that affect the traits of E. ulmoides and offered solutions for trait improvement. This information could be a reference for the trait improvement of other plants.

Adaptability and Stability of Faba Bean (Vicia faba L.) Accessions under Diverse Environments and Herbicide Treatments.

The adaptability and stability of 37 faba bean (Vicia faba L.) accessions with different levels of tolerance to metribuzin or imazethapyr was assessed across 12 season-location-herbicide experiments. Significant Genotype x environment (GE) interaction was found for the days to flowering (DFLR), plant height (PLHT) and grain yield (GY). Performance and stability of the accessions regarding PLHT and GY were assessed using four different stability parameters: cultivar superiority, static stability, Wricke's eco-valence and Finlay and Wilkinson's regression model. The stability parameters ranked these genotypes differently suggesting that PLHT and GY stability should be assessed not only on a single or a few stability parameters but on a combination of them. GGE biplot analysis indicated that the environments representing metribuzin treatment at Marchouch 2014-2015 and the non-treated treatment at Terbol 2018-2019 are the ideal environments for evaluating faba bean genotypes. GGE biplots showed herbicide tolerant accession IG12983 with simultaneous average PLHT, GY and stability across the environments. The performance of other tolerant accessions, namely IG13945, IG13906, IG106453, FB2648, and FB1216 was less stable but superior under specific mega environments. Therefore, utilizing these accessions in faba bean breeding programs would help broaden the adaptability to diverse locations-season-herbicide treatments.

Parenting moderates the etiology of callous-unemotional traits in middle childhood.

BACKGROUND: Callous-unemotional (CU) traits are associated with chronic and escalating trajectories of antisocial behavior. Extant etiologic studies suggest that heritability estimates for CU traits vary substantially, while also pointing to an environmental association between parenting and CU traits. METHODS: We used twin modeling to estimate additive genetic (A), shared environmental (C), and nonshared environmental (E) influences on CU traits, measured with the Inventory of Callous-Unemotional Traits (ICU) and its subscales. Our sample included 600 twin pairs (age 6-11, 230 monozygotic) from neighborhoods with above-average levels of family poverty, a risk factor for antisocial behavior. We examined the extent to which correlations between parenting, measured via parent and child report on the Parental Environment Questionnaire, and CU traits reflected genetic versus environmental factors. Then, we tested whether parenting moderated the heritability of CU traits. RESULTS: In the context of lower-income neighborhoods, CU traits were moderately to highly heritable (A = 54%) with similar moderate-to-high nonshared environmental influences (E = 46%). Bivariate models revealed that associations between CU traits and warm parenting were genetic (rA = .22) and environmental (rE = .19) in origin, whereas associations between CU traits and harsh parenting were largely genetic in origin (rA = .70). The heritability of CU traits decreased with increasing parental warmth and decreasing harshness. CONCLUSIONS: Callous-unemotional traits are both genetic and environmental in origin during middle childhood, but genetic influences are moderated by parenting quality. Parenting may be an important target for interventions, particularly among youth with greater genetic risk.

Genome-wide association studies dissect the G × E interaction for agronomic traits in a worldwide collection of safflowers (Carthamus tinctorius L.).

Genome-wide association studies were conducted using a globally diverse safflower (Carthamus tinctorius L.) Genebank collection for grain yield (YP), days to flowering (DF), plant height (PH), 500 seed weight (SW), seed oil content (OL), and crude protein content (PR) in four environments (sites) that differed in water availability. Phenotypic variation was observed for all traits. YP exhibited low overall genetic correlations (rGoverall) across sites, while SW and OL had high rGoverall and high pairwise genetic correlations (rGij) across all pairwise sites. In total, 92 marker-trait associations (MTAs) were identified using three methods, single locus genome-wide association studies (GWAS) using a mixed linear model (MLM), the Bayesian multi-locus method (BayesR), and meta-GWAS. MTAs with large effects across all sites were detected for OL, SW, and PR, and MTAs specific for the different water stress sites were identified for all traits. Five MTAs were associated with multiple traits; 4 of 5 MTAs were variously associated with the three traits of SW, OL, and PR. This study provided insights into the phenotypic variability and genetic architecture of important safflower agronomic traits under different environments. Supplementary Information: The online version contains supplementary material available at 10.1007/s11032-022-01295-8.

Genotype and Environment Affect the Grain Quality and Yield of Winter Oats (Avena sativa L.).

The extent to which the quality and yield of plant varieties are influenced by the environment is important for their successful uptake by end users particularly as climatic fluctuations are resulting in environments that are highly variable from one growing season to another. The genotype-by-environment interaction (GEI) of milling quality and yield was studied using four winter oat varieties in multi-locational trials over 4 years in the U.K. Significant differences across the 22 environments were found between physical grain quality and composition as well as grain yield, with the environment having a significant effect on all of the traits measured. Grain yield was closely related to grain number m-2 whereas milling quality traits were related to grain size attributes. Considerable genotype by environment interaction was obtained for all grain quality traits and stability analysis revealed that the variety Mascani was the least sensitive to the environment for all milling quality traits measured whereas the variety Balado was the most sensitive. Examination of environmental conditions at specific within-year stages of crop development indicated that both temperature and rainfall during grain development were correlated with grain yield and ß-glucan content and with the ease of removing the hull (hullability).