Contrasting allelic effects for pistachio salinity tolerance in juvenile and mature trees.

Breeding perennial tree crops often requires prediction of mature performance from juvenile data. To assess the utility of juvenile screens to predict salinity tolerance of mature pistachio trees, we compared performance of 3-month ungrafted seedlings and 4-year-old grafted rootstocks under salinity stress. The QTL allele associated with higher salt exclusion from seedling leaves conferred lower growth in saline field conditions, suggesting that mapping QTL in seedlings may be easier than discerning the optimal allele for field performance.

Natural variation in photosynthesis and water use efficiency of locally adapted Persian walnut populations under drought stress and recovery.

Persian walnut is a drought-sensitive species with considerable genetic variation in the photosynthesis and water use efficiency of its populations, which is largely unexplored. Here, we aimed to elucidate changes in the efficiency of photosynthesis and water content using a diverse panel of 60 walnut families which were submitted to a progressive drought for 24 days, followed by two weeks of re-watering. Severe water-withholding reduced leaf relative water content (RWC) by 20%, net photosynthetic rate (Pn) by 50%, stomatal conductance (gs) by 60%, intercellular CO2 concentration (Ci) by 30%, and transpiration rate (Tr) by 50%, but improved water use efficiency (WUE) by 25%. Severe water-withholding also inhibited photosystem II functionality as indicated by reduced quantum yield of intersystem electron transport (φEo) and transfer of electrons per reaction center (ET0/RC), also enhanced accumulation of QA (VJ) resulted in the reduction of the photosynthetic performance (PIABS) and maximal quantum yield of PSII (FV/FM); while elevated quantum yield of energy dissipation (φDo), energy fluxes for absorption (ABS/RC) and dissipated energy flux (DI0/RC) in walnut families. Cluster analysis classified families into three main groups (tolerant, moderately tolerant, and sensitive), with the tolerant group from dry climates exhibiting lesser alterations in assessed parameters than the other groups. Multivariate analysis of phenotypic data demonstrated that RWC and biophysical parameters related to the chlorophyll fluorescence such as FV/FM, φEo, φDo, PIABS, ABS/RC, ET0/RC, and DI0/RC represent fast, robust and non-destructive biomarkers for walnut performance under drought stress. Finally, phenotype-environment association analysis showed significant correlation of some photosynthetic traits with geoclimatic factors, suggesting a key role of climate and geography in the adaptation of walnut to its habitat conditions.

Genome-wide association analysis and pathway enrichment provide insights into the genetic basis of photosynthetic responses to drought stress in Persian walnut.

Uncovering the genetic basis of photosynthetic trait variation under drought stress is essential for breeding climate-resilient walnut cultivars. To this end, we examined photosynthetic capacity in a diverse panel of 150 walnut families (1500 seedlings) from various agro-climatic zones in their habitats and grown in a common garden experiment. Photosynthetic traits were measured under well-watered (WW), water-stressed (WS) and recovery (WR) conditions. We performed genome-wide association studies (GWAS) using three genomic datasets: genotyping by sequencing data (∼43 K SNPs) on both mother trees (MGBS) and progeny (PGBS) and the Axiom™ Juglans regia 700 K SNP array data (∼295 K SNPs) on mother trees (MArray). We identified 578 unique genomic regions linked with at least one trait in a specific treatment, 874 predicted genes that fell within 20 kb of a significant or suggestive SNP in at least two of the three GWAS datasets (MArray, MGBS, and PGBS), and 67 genes that fell within 20 kb of a significant SNP in all three GWAS datasets. Functional annotation identified several candidate pathways and genes that play crucial roles in photosynthesis, amino acid and carbohydrate metabolism, and signal transduction. Further network analysis identified 15 hub genes under WW, WS and WR conditions including GAPB, PSAN, CRR1, NTRC, DGD1, CYP38, and PETC which are involved in the photosynthetic responses. These findings shed light on possible strategies for improving walnut productivity under drought stress.

Predictive modeling of Persian walnut (Juglans regia L.) in vitro proliferation media using machine learning approaches: a comparative study of ANN, KNN and GEP models.

BACKGROUND: Optimizing plant tissue culture media is a complicated process, which is easily influenced by genotype, mineral nutrients, plant growth regulators (PGRs), vitamins and other factors, leading to undesirable and inefficient medium composition. Facing incidence of different physiological disorders such as callusing, shoot tip necrosis (STN) and vitrification (Vit) in walnut proliferation, it is necessary to develop prediction models for identifying the impact of different factors involving in this process. In the present study, three machine learning (ML) approaches including multi-layer perceptron neural network (MLPNN), k-nearest neighbors (KNN) and gene expression programming (GEP) were implemented and compared to multiple linear regression (MLR) to develop models for prediction of in vitro proliferation of Persian walnut (Juglans regia L.). The accuracy of developed models was evaluated using coefficient of determination (R2), root mean square error (RMSE) and mean absolute error (MAE). With the aim of optimizing the selected prediction models, multi-objective evolutionary optimization algorithm using particle swarm optimization (PSO) technique was applied. RESULTS: Our results indicated that all three ML techniques had higher accuracy of prediction than MLR, for example, calculated R2 of MLPNN, KNN and GEP vs. MLR was 0.695, 0.672 and 0.802 vs. 0.412 in Chandler and 0.358, 0.377 and 0.428 vs. 0.178 in Rayen, respectively. The GEP models were further selected to be optimized using PSO. The comparison of modeling procedures provides a new insight into in vitro culture medium composition prediction models. Based on the results, hybrid GEP-PSO technique displays good performance for modeling walnut tissue culture media, while MLPNN and KNN have also shown strong estimation capability. CONCLUSION: Here, besides MLPNN and GEP, KNN also is introduced, for the first time, as a simple technique with high accuracy to be used for developing prediction models in optimizing plant tissue culture media composition studies. Therefore, selection of the modeling technique to study depends on the researcher's desire regarding the simplicity of the procedure, obtaining clear results as entire formula and/or less time to analyze.

Combining phenotype, genotype, and environment to uncover genetic components underlying water use efficiency in Persian walnut.

Walnut production is challenged by climate change and abiotic stresses. Elucidating the genomic basis of adaptation to climate is essential to breeding drought-tolerant cultivars for enhanced productivity in arid and semi-arid regions. Here, we aimed to identify loci potentially involved in water use efficiency (WUE) and adaptation to drought in Persian walnut using a diverse panel of 95 walnut families (950 seedlings) from Iran, which show contrasting levels of water availability in their native habitats. We analyzed associations between phenotypic, genotypic, and environmental variables from data sets of 609 000 high-quality single nucleotide polymorphisms (SNPs), three categories of phenotypic traits [WUE-related traits under drought, their drought stress index, and principal components (PCs)], and 21 climate variables and their combination (first three PCs). Our genotype-phenotype analysis identified 22 significant and 266 suggestive associations, some of which were for multiple traits, suggesting their correlation and a possible common genetic control. Also, genotype-environment association analysis found 115 significant and 265 suggestive SNP loci that displayed potential signals of local adaptation. Several sets of stress-responsive genes were found in the genomic regions significantly associated with the aforementioned traits. Most of the candidate genes identified are involved in abscisic acid signaling, stomatal regulation, transduction of environmental signals, antioxidant defense system, osmotic adjustment, and leaf growth and development. Upon validation, the marker-trait associations identified for drought tolerance-related traits would allow the selection and development of new walnut rootstocks or scion cultivars with superior WUE.

Mathematical Modeling and Optimizing of in Vitro Hormonal Combination for G × N15 Vegetative Rootstock Proliferation Using Artificial Neural Network-Genetic Algorithm (ANN-GA).

The efficiency of a hybrid systems method which combined artificial neural networks (ANNs) as a modeling tool and genetic algorithms (GAs) as an optimizing method for input variables used in ANN modeling was assessed. Hence, as a new technique, it was applied for the prediction and optimization of the plant hormones concentrations and combinations for in vitro proliferation of Garnem (G × N15) rootstock as a case study. Optimizing hormones combination was surveyed by modeling the effects of various concentrations of cytokinin-auxin, i.e., BAP, KIN, TDZ, IBA, and NAA combinations (inputs) on four growth parameters (outputs), i.e., micro-shoots number per explant, length of micro-shoots, developed callus weight (CW) and the quality index (QI) of plantlets. Calculation of statistical values such as R2 (coefficient of determination) related to the accuracy of ANN-GA models showed a considerably higher prediction accuracy for ANN models, i.e., micro-shoots number: R2 = 0.81, length of micro-shoots: R2 = 0.87, CW: R2 = 0.88, QI: R2 = 0.87. According to the results, among the input variables, BAP (19.3), KIN (9.64), and IBA (2.63) showed the highest values of variable sensitivity ratio for proliferation rate. The GA showed that media containing 1.02 mg/l BAP in combination with 0.098 mg/l IBA could lead to the optimal proliferation rate (10.53) for G × N15 rootstock. Another objective of the present study was to compare the performance of predicted and optimized cytokinin-auxin combination with the best optimized obtained concentrations of our other experiments. Considering three growth parameters (length of micro-shoots, micro-shoots number, and proliferation rate), the last treatment was found to be superior to the rest of treatments for G × N15 rootstock in vitro multiplication. Very little difference between the ANN predicted and experimental data confirmed high capability of ANN-GA method in predicting new optimized protocols for plant in vitro propagation.

Artificial Neural Network Genetic Algorithm As Powerful Tool to Predict and Optimize In vitro Proliferation Mineral Medium for G × N15 Rootstock.

One of the major obstacles to the micropropagation of Prunus rootstocks has, up until now, been the lack of a suitable tissue culture medium. Therefore, reformulation of culture media or modification of the mineral content might be a breakthrough to improve in vitro multiplication of G × N15 (garnem). We found artificial neural network in combination of genetic algorithm (ANN-GA) as a very precise and powerful modeling system for optimizing the culture medium, So that modeling the effects of MS mineral salts ([Formula: see text], [Formula: see text], [Formula: see text], Ca2+, K+, [Formula: see text], Mg2+, and Cl-) on in vitro multiplication parameters (the number of microshoots per explant, average length of microshoots, weight of calluses derived from the base of stem explants, and quality index of plantlets) of G × N15. Showed high R2 correlation values of 87, 91, 87, and 74 between observed and predicted values were found for these four growth parameters, respectively. According to the ANN-GA results, among the input variables, [Formula: see text] and [Formula: see text] had the highest values of VSR in data set for the parameters studied. The ANN-GA showed that the best proliferation rate was obtained from medium containing (mM) 27.5 [Formula: see text], 14 [Formula: see text], 5 Ca2+, 25.9 K+, 0.7 Mg2+, 1.1 [Formula: see text], 4.7 [Formula: see text], and 0.96 Cl-. The performance of the medium optimized by ANN-GA, denoted as YAS (Yadollahi, Arab and Shojaeiyan), was compared to that of standard growth media for all Prunus rootstock, including the Murashige and Skoog (MS) medium, (specific media) EM, Quoirin and Lepoivre (QL) medium, and woody plant medium (WPM) Prunus. With respect to shoot length, shoot number per cultured explant and productivity (number of microshoots × length of microshoots), YAS was found to be superior to other media for in vitro multiplication of G × N15 rootstocks. In addition, our results indicated that by using ANN-GA, we were able to determine a suitable culture medium formulation to achieve the best in vitro productivity.