Understanding the contrasting biochemical changes in different plant parts in response to drought can help to formulate smart strategies to develop drought tolerant genotypes. The current study used metabolomics and physiological approaches to understand the differential biochemical changes coupled with physiological adjustments in leaves and roots to cope with drought stress in two wheat genotypes, LA754 (drought tolerant) and AGS2038 (drought sensitive). The gas chromatography-mass spectrometry (GC-MS) analysis and physiological trait estimation were performed in the roots and leaves after drought imposition. Drought induced reduction was observed in all physiological and yield related traits. In LA754, higher numbers of metabolites were altered in leaves (45) compared to roots (20) which indicates that plants allocated more resources to leaves in tolerant genotype. In addition, the metabolic components of the root were less affected by the stress which supports the idea that the roots are more drought tolerant than the leaf or shoot. In AGS2038, thirty and twenty eight metabolites were altered in the leaves and roots, respectively. This indicates that the sensitive genotype compromised resource allocation to leaves, rather allocated more towards roots. Tryptophan, valine, citric acid, fumaric acid, and malic acid showed higher accumulation in leaf in LA754, but decreased in the root, while glyceric acid was highly accumulated in the root, but not in the leaf. The results demonstrated that the roots and shoots have a different metabolic composition, and shoot metabolome is more variable than the root metabolome. Though the present study demonstrated that the metabolic response of shoots to drought contrasts with that of roots, some growth metabolites (protein, sugar, etc) showed a mirror increase in both parts. Protein synthesis and energy cycle was active in both organs, and the organs were metabolically activated to enhance water uptake and maintain growth to mitigate the effect of drought.

Moderate heat stress accompanied by short episodes of extreme heat during the post-anthesis stage is common in most US wheat growing areas and causes substantial yield losses. Sink strength (grain number) is a key yield limiting factor in modern wheat varieties. Increasing spike fertility (SF) and improving the partitioning of assimilates can optimize sink strength which is essential to improve wheat yield potential under a hot and humid environment. A genome-wide association study (GWAS) allows identification of novel quantitative trait loci (QTLs) associated with SF and other partitioning traits that can assist in marker assisted breeding. In this study, GWAS was performed on a soft wheat association mapping panel (SWAMP) comprised of 236 elite lines using 27,466 single nucleotide polymorphisms (SNPs). The panel was phenotyped in two heat stress locations over 3 years. GWAS identified 109 significant marker-trait associations (MTAs) (p ≤ 9.99 x 10−5) related to eight phenotypic traits including SF (a major component of grain number) and spike harvest index (SHI, a major component of grain weight). MTAs detected on chromosomes 1B, 3A, 3B, and 5A were associated with multiple traits and are potentially important targets for selection. More than half of the significant MTAs (60 out of 109) were found in genes encoding different types of proteins related to metabolism, disease, and abiotic stress including heat stress. These MTAs could be potential targets for further validation study and may be used in marker-assisted breeding for improving wheat grain yield under post-anthesis heat stress conditions. This is the first study to identify novel QTLs associated with SF and SHI which represent the major components of grain number and grain weight, respectively, in wheat.

The performance of genomic prediction (GP) on genetically correlated traits can be improved through an interdependence multi-trait model under a multi-environment context. In this study, a panel of 237 soft facultative wheat (Triticum aestivum L.) lines was evaluated to compare single- and multi-trait models for predicting grain yield (GY), harvest index (HI), spike fertility (SF), and thousand grain weight (TGW). The panel was phenotyped in two locations and two years in Florida under drought and moderately drought stress conditions, while the genotyping was performed using 27,957 genotyping-by-sequencing (GBS) single nucleotide polymorphism (SNP) makers. Five predictive models including Multi-environment Genomic Best Linear Unbiased Predictor (MGBLUP), Bayesian Multi-trait Multi-environment (BMTME), Bayesian Multi-output Regressor Stacking (BMORS), Single-trait Multi-environment Deep Learning (SMDL), and Multi-trait Multi-environment Deep Learning (MMDL) were compared. Across environments, the multi-trait statistical model (BMTME) was superior to the multi-trait DL model for prediction accuracy in most scenarios, but the DL models were comparable to the statistical models for response to selection. The multi-trait model also showed 5 to 22% more genetic gain compared to the single-trait model across environment reflected by the response to selection. Overall, these results suggest that multi-trait genomic prediction can be an efficient strategy for economically important yield component related traits in soft wheat.

An integration of field-based phenotypic and genomic data can potentially increase the genetic gain in wheat breeding for complex traits such as grain and biomass yield. To validate this hypothesis in empirical field experiments, we compared the prediction accuracy between multi-kernel physiological and genomic best linear unbiased prediction (BLUP) model to a single-kernel physiological or genomic BLUP model for grain yield (GY) using a soft wheat population that was evaluated in four environments. The physiological data including canopy temperature (CT), SPAD chlorophyll content (SPAD), membrane thermostability (MT), rate of senescence (RS), stay green trait (SGT), and NDVI values were collected at four environments (2016, 2017, and 2018 at Citra, FL; 2017 at Quincy, FL). Using a genotyping-by-sequencing (GBS) approach, a total of 19,353 SNPs were generated and used to estimate prediction model accuracy. Prediction accuracies of grain yield evaluated in four environments improved when physiological traits and/or interaction effects (genotype × environment or physiology × environment) were included in the model compared to models with only genomic data. The proposed multi-kernel models that combined physiological and genomic data showed 35 to 169% increase in prediction accuracy compared to models with only genomic data included when heading date was used as a covariate. In general, higher response to selection was captured by the model combing effects of physiological and genotype × environment interaction compared to other models. The results of this study support the integration of field-based physiological data into GY prediction to improve genetic gain from selection in soft wheat under a multi-environment context.

Background: Climate change, including higher temperatures (HT) has a detrimental impact on wheat productivity and modeling studies predict more frequent heat waves in the future. Wheat growth can be impaired by high daytime and nighttime temperature at any developmental stage, especially during the grain filling stage. Leaf chlorophyll content, leaf greenness, cell membrane thermostability, and canopy temperature have been proposed as candidate traits to improve crop adaptation and yield potential of wheat under HT. Nonetheless, a significant gap exists in knowledge of genetic backgrounds associated with these physiological traits. Identifying genetic loci associated with these traits can facilitate physiological breeding for increased yield potential under high temperature stress condition in wheat. Results: We conducted genome-wide association study (GWAS) on a 236 elite soft wheat association mapping panel using 27,466 high quality single nucleotide polymorphism markers. The panel was phenotyped for three years in two locations where heat shock was common. GWAS identified 500 significant marker-trait associations (MTAs) (p ≤ 9.99 × 10 − 4). Ten MTAs with pleiotropic effects detected on chromosomes 1D, 2B, 3A, 3B, 6A, 7B, and 7D are potentially important targets for selection. Five MTAs associated with physiological traits had pleiotropic effects on grain yield and yield-related traits. Seventy-five MTAs were consistently expressed over several environments indicating stability and more than half of these stable MTAs were found in genes encoding different types of proteins associated with heat stress. Conclusions: We identified 500 significant MTAs in soft winter wheat under HT stress. We found several stable loci across environments and pleiotropic markers controlling physiological and agronomic traits. After further validation, these MTAs can be used in marker-assisted selection and breeding to develop varieties with high stability for grain yield under high temperature.

Background Recently genomic selection (GS) has emerged as an important tool for plant breeders to select superior genotypes. Multi-trait (MT) prediction model provides an opportunity to improve the predictive ability of expensive and labor-intensive traits. In this study, we assessed the potential use of a MT genomic prediction model by incorporating two physiological traits (canopy temperature, CT and normalized difference vegetation index, NDVI) to predict 5 complex primary traits (harvest index, HI; grain yield, GY; grain number, GN; spike partitioning index, SPI; fruiting efiiciency, FE) using two cross-validation schemes CV1 and CV2. Results In this study, we evaluated 236 wheat genotypes in two locations in 2 years. The wheat genotypes were genotyped with genotyping by sequencing approach which generated 27,466 SNPs. MT-CV2 (multi-trait cross validation 2) model improved predictive ability by 4.8 to 138.5% compared to ST-CV1(single-trait cross validation 1). However, the predictive ability of MT-CV1 was not significantly different compared to the ST-CV1 model. Conclusions The study showed that the genomic prediction of complex traits such as HI, GN, and GY can be improved when correlated secondary traits (cheaper and easier phenotyping) are used. MT genomic selection could accelerate breeding cycles and improve genetic gain for complex traits in wheat and other crops.

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Sugarcane (Saccharum spp.) is an important perennial grass crop for both sugar and biofuel industries. The Louisiana sugarcane breeding program is focused on improving sugar yield by incrementally increasing genetic gain. With the advancement in genotyping and (highthroughput) phenotyping techniques, genomic selection is a promising marker-assisted breeding tool. In this study, we assessed ridge regression best linear unbiased prediction (rrBLUP) and various Bayesian models to evaluate genomic prediction accuracy using a 10-fold cross validation on 95 commercial and elite parental clones from the Louisiana sugarcane breeding program. Datasets (individual and pooled in various combinations) were constructed based on soil type (light—Commerce silty loam, heavy—Sharkey clay) and crop (plant cane, ratoon). A total of 3906 SNPs were used to predict the genomic estimated breeding values (GEBVs) of the clones for sucrose content and cane and sugar yield. Prediction accuracy was estimated by both Spearman’s rank correlation and Pearson’s correlation between phenotypic breeding values and GEBVs. All traits showed significant variation with moderate (42% for sucrose content) to high (85% for cane and sugar yield) heritability. Prediction accuracy based on rank correlation was high (0.47–0.80 for sucrose content; 0.61–0.69 for cane yield, and 0.56–0.72 for sugar yield) in all cross-effect prediction models where soil and crop types were considered as fixed effects. In general, Bayesian models demonstrated a higher correlation than rrBLUP. The Pearson’s correlation without soil and crop type as fixed effects was lower with no clear pattern among the models. The results demonstrate the potential implementation of genomic prediction in the Louisiana sugarcane variety development program.