Combining Genetic and Multidimensional Analyses to Identify Interpretive Traits Related to Water Shortage Tolerance as an Indirect Selection Tool for Detecting Genotypes of Drought Tolerance in Wheat Breeding

Abstract

Water shortages have direct adverse effects on wheat productivity and growth worldwide, vertically and horizontally. Productivity may be promoted using water shortage-tolerant wheat genotypes. High-throughput tools have supported plant breeders in increasing the rate of stability of the genetic gain of interpretive traits for wheat productivity through multidimensional technical methods. We used 27 agrophysiological interpretive traits for grain yield (GY) of 25 bread wheat genotypes under water shortage stress conditions for two seasons. Genetic parameters and multidimensional analyses were used to identify genetic and phenotypic variations of the wheat genotypes used, combining these strategies effectively to achieve a balance. Considerable high genotypic variations were observed for 27 traits. Eleven interpretive traits related to GY had combined high heritability (h2 > 60%) and genetic gain (>20%), compared to GY, which showed moderate values both for heritability (57.60%) and genetic gain (16.89%). It was determined that six out of eleven traits (dry leaf weight (DLW), canopy temperature (CT), relative water content (RWC), flag leaf area (FLA), green leaves area (GLA) and leaf area index (LAI)) loaded the highest onto PC1 and PC2 (with scores of >0.27), and five of them had a positive trend with GY, while the CT trait had a negative correlation determined by principal component analysis (PCA). Genetic parameters and multidimensional analyses (PCA, stepwise regression, and path coefficient) showed that CT, RWC, GLA, and LAI were the most important interpretive traits for GY. Selection based on these four interpretive traits might improve genetic gain for GY in environments that are vulnerable to water shortages. The membership index and clustering analysis based on these four traits were significantly correlated, with some deviation, and classified genotypes into five groups. Highly tolerant, tolerant, intermediate, sensitive and highly sensitive clusters represented six, eight, two, three and six genotypes, respectively. The conclusions drawn from the membership index and clustering analysis, signifying that there were clear separations between the water shortage tolerance groups, were confirmed through discriminant analysis. MANOVA indicated that there were considerable variations between the five water shortage tolerance groups. The tolerated genotypes (DHL02, DHL30, DHL26, Misr1, Pavone-76 and DHL08) can be recommended as interesting new genetic sources for water shortage-tolerant wheat breeding programs.

Stability, the Last Frontier: Forage Yield Dynamics of Peas under Two Cultivation Systems

“…The genetic variability and the GCV of these two traits are the highest among all other traits. This combination of high heritability (H 2 ) and high GCV is an indication that the variation among genotypes was largely due to the additive genetic part [ 43 ]. Abebe et al [ 44 ] suggested that high heritability in these values, in a broad sense, indicate that the characters under study are less influenced by the environment in their expression.…”

Assessing the correlations and selection criteria between different traits in wheat salt-tolerant genotypes

“…This methodology is time-consuming and expensive because it requires more than one field assessment, which must be conducted during different seasons and in various locations ( Urrea‐Gómez et al, 1996Urrea‐Gómez et al, 1996Urrea‐Gómez et al, 1996 , Grzesiak et al, 2019 ). To reduce this difficult process of selection, preferably using rapid, convenient, and cost-neutral methods, heritability should have high genetic correlation and thereby be a selection tool using which breeders can screen genotypes and the selection process can proceed rapidly ( Reynolds et al, 1999 , Jackson, 2001 , Al-Ashkar et al, 2021a ).…”

“…This may cause overfitting of the analysis model and a broad probability of spurious errors ( Sainani, 2014 ) because they collectively contribute to explain linear relationships. Principal component analysis (PCA) is a method that can narrow the number of correlated variables, wherein predictors are summarized into a new set of unrelated variables (principal components [PCs]) with minimal loss of data ( Abdi and Williams, 2010 , Al-Ashkar et al, 2021a ). Therefore, PCA could be used as a method for data dimension reduction, which has been previously used in plant breeding with positive results ( El-Dien et al, 2015 ).…”

“…Therefore, PCA could be used as a method for data dimension reduction, which has been previously used in plant breeding with positive results ( El-Dien et al, 2015 ). Multicollinearity is one of the outcomes of PCs that explain linear relationships in a multivariate analysis and are used when it is difficult to individually estimate the relationships of explanatory traits because they are associated and because they collectively contribute to explain linear relationships ( Olivoto et al, 2017 , Al-Ashkar et al, 2021a ). When this problem is detected at moderate or high degrees, the variance accompanying the estimates of path coefficients reach very high values, reducing the credibility of the findings that are incompatible with biological expectation ( Cruz et al, 2014 ).…”

Modified Group Method of Data Handling for Flood Quantile Prediction at Ungauged Site

“…Usually, for multivariate problems, multicollinearity exists in the data set, making the prediction performance of a particular hydrological model performance poor. Applying PCA in the data set removes the multicollinearity in the data set and is expected to improve the prediction performance of the hydrological model (Barth et al, 2021;Al-Ashkar et al, 2021). The study done by showed that the combination of PCA and various transfer functions outperformed other machine learning models such as the Artificial Neural Network (ANN) model.…”