Integrating Agro-Morpho-Physiological Traits and SSR Markers for Detecting the Salt Tolerance of Advanced Spring Wheat Lines under Field Conditions

Abstract

To successfully enhance the salt tolerance of genotypes, it is crucial to conduct field-based trials, establish effective screening criteria and analysis tools, evaluate salt tolerance at various growth stages, and integrate phenotypic assessment-based traits with molecular markers. This study aimed to assess the salt tolerance of 16 F8 recombinant inbred lines (RILs) and eight genotypes by analyzing 13 agro-morpho-physiological traits using various analysis tools and SSR markers under both control and high salinity levels (15 dS m−1) in real field conditions. Analysis of variance (ANOVA), comparison of mean values, calculation of reduction percentage, and multivariate analysis were used to compare the assessed traits among genotypes and identify which traits are the most effective ones in describing the salt tolerance of these genotypes. A heatmap cluster analysis (HMCA) was also employed to categorize the salt tolerance of genotypes into different clusters based on the stress tolerance index (STI) for all traits. The ANOVA results revealed significant statistical differences (p ≤ 0.05) between the genotypes and salinity levels for all assessed traits in each season and their combined data. Moreover, the 150 mM NaCl treatment led to decreases in the assessed traits by 10.2% to 36.9% when compared to the control treatments. Furthermore, the mean values of assessed traits for certain genotypes were approximately one to three times greater than those of other genotypes. Principal component analysis has identified plant dry weight, green leaf area, leaf area index, and grain yield per hectare as effective screening criteria for explaining the substantial variation observed among the genotypes. The HMCA successfully grouped genotypes into three distinct clusters and distinguished the salt-tolerant genotypes from the salt-sensitive and intermediate ones. The 24 genotypes/RILs were classified into three main groups according to the allelic data of 40 SSRs associated with salt-tolerant genes. A weak yet significant correlation was observed between the similarity coefficients of agro-morpho-physiological traits and SSR markers, as determined by the Mantel test (r = 0.13, p < 0.03, and alpha = 0.05). In conclusion, this study has successfully identified several traits, particularly those associated with SSR markers, that greatly contribute to our understanding of the phenotypic and genotypic basis influencing the salt tolerance of wheat genotypes in real field conditions. Consequently, assessing these traits for a large number of wheat plant materials in a rapid and cost-effective manner will be greatly importance in breeding programs aimed at improving salt stress tolerance in this vital food crop. This will be the main focus of our forthcoming research.

Combining Genetic and Phenotypic Analyses for Detecting Bread Wheat Genotypes of Drought Tolerance through Multivariate Analysis Techniques

“…These positive correlations are necessary, highlighting SSR markers as an efficient tool for accessing tolerant genotypes in the early stages of a breeding program. Our results reinforce similar findings from many studies [17,[78][79][80][81]. Two genotypes (16HTWYT-12 and KSU105) within the categories showed greater distance in the genotyping analysis compared to the categories based on phenotyping analysis, while the genotype (ksu106) showed even greater distancing.…”

“…To overcome these limitations, a complementary approach was urgently required to combine accurate evaluation with rapid and cost-effective methods. Molecular markers linked to QTLs in MAS are responsible for key agro-physio-biochemical traits under drought stress as SSR markers and are the best tool to detect genotypes with drought tolerance [17,33,62,79]. In this study, HC analysis based on SSR markers categorized 13 genotypes into three main tolerance categories (Figure 5).…”

“…But cross-validation showed differences in the compatibility proportion of prior and posterior categories, with compatibility observed in nine genotypes (% correct = 69.23%) and incompatible in four genotypes (16HTWYT-30, 16HTWYT-20, 16HTWYT-12, and 16HTWYT-22). Therefore, LD analysis is regarded as a distinct statistical tool (as a selection criterion for accuracy and credibility) for verifying genotype resources of drought tolerance [62,78,79].…”