Multivariate analysis and genetic dissection of staygreen and stem reserve mobilisation under combined drought and heat stress in wheat (Triticum aestivum L.).

Introduction: Abiotic stresses significantly reduce crop yield by adversely affecting many physio-biochemical processes. Several physiological traits have been targeted and improved for yield enhancement in limiting environmental conditions. Amongst them, staygreen and stem reserve mobilisation are two important mutually exclusive traits contributing to grain filling under drought and heat stress in wheat. Henceforth, the present study was carried out to identify the QTLs governing these traits and to identify the superiors' lines through multi-trait genotype-ideotype distance index (MGIDI) Methods: A mapping population consisting of 166 recombinant inbred lines (RILs) developed from a cross between HD3086 and HI1500 was utilized in this study. The experiment was laid down in alpha lattice design in four environmental conditions viz. Control, drought, heat and combined stress (heat and drought). Genotyping of parents and RILs was carried out with 35 K Axiom® array (Wheat breeder array). Results and Discussion: Medium to high heritability with a moderate to high correlation between traits was observed. Principal component analysis (PCA) was performed to derive latent variables in the original set of traits and the relationship of these traits with latent variables.From this study, 14 QTLs were identified, out of which 11, 2, and 1 for soil plant analysis development (SPAD) value, leaf senescence rate (LSR), and stem reserve mobilisation efficiency (SRE) respectively. Quantitative trait loci (QTLs) for SPAD value harbored various genes like Dirigent protein 6-like, Protein FATTY ACID EXPORT 3, glucan synthase-3 and Ubiquitin carboxyl-terminal hydrolase, whereas QTLs for LSR were found to contain various genes like aspartyl protease family protein, potassium transporter, inositol-tetrakisphosphate 1-kinase, and DNA polymerase epsilon subunit D-like. Furthermore, the chromosomal region for SRE was found to be associated with serine-threonine protein kinase. Serine-threonine protein kinases are involved in many signaling networks such as ABA mediated ROS signaling and acclimation to environmental stimuli. After the validation of QTLs in multilocation trials, these QTLs can be used for marker-assisted selection (MAS) in breeding programs.

Phenomics based prediction of plant biomass and leaf area in wheat using machine learning approaches.

Introduction: Phenomics has emerged as important tool to bridge the genotype-phenotype gap. To dissect complex traits such as highly dynamic plant growth, and quantification of its component traits over a different growth phase of plant will immensely help dissect genetic basis of biomass production. Based on RGB images, models have been developed to predict biomass recently. However, it is very challenging to find a model performing stable across experiments. In this study, we recorded RGB and NIR images of wheat germplasm and Recombinant Inbred Lines (RILs) of Raj3765xHD2329, and examined the use of multimodal images from RGB, NIR sensors and machine learning models to predict biomass and leaf area non-invasively. Results: The image-based traits (i-Traits) containing geometric features, RGB based indices, RGB colour classes and NIR features were categorized into architectural traits and physiological traits. Total 77 i-Traits were selected for prediction of biomass and leaf area consisting of 35 architectural and 42 physiological traits. We have shown that different biomass related traits such as fresh weight, dry weight and shoot area can be predicted accurately from RGB and NIR images using 16 machine learning models. We applied the models on two consecutive years of experiments and found that measurement accuracies were similar suggesting the generalized nature of models. Results showed that all biomass-related traits could be estimated with about 90% accuracy but the performance of model BLASSO was relatively stable and high in all the traits and experiments. The R2 of BLASSO for fresh weight prediction was 0.96 (both year experiments), for dry weight prediction was 0.90 (Experiment 1) and 0.93 (Experiment 2) and for shoot area prediction 0.96 (Experiment 1) and 0.93 (Experiment 2). Also, the RMSRE of BLASSO for fresh weight prediction was 0.53 (Experiment 1) and 0.24 (Experiment 2), for dry weight prediction was 0.85 (Experiment 1) and 0.25 (Experiment 2) and for shoot area prediction 0.59 (Experiment 1) and 0.53 (Experiment 2). Discussion: Based on the quantification power analysis of i-Traits, the determinants of biomass accumulation were found which contains both architectural and physiological traits. The best predictor i-Trait for fresh weight and dry weight prediction was Area_SV and for shoot area prediction was projected shoot area. These results will be helpful for identification and genetic basis dissection of major determinants of biomass accumulation and also non-invasive high throughput estimation of plant growth during different phenological stages can identify hitherto uncovered genes for biomass production and its deployment in crop improvement for breaking the yield plateau.