Trait prioritization and genotype selection for heat stress tolerance in wheat via structural equation modeling and principal component analysis (PCA)
Irum Khan, Muhammad Kashif Naeem, Mehraj Abbasov, Muhammad Ramzan Khan, Zi Jin Zhang, Jing Chen, Muhammad SajjadTerminal heat stress is a major limitation to wheat productivity, especially during the reproductive stage. This paper examines the genetic variation and correlations of heat tolerance traits in wheat using structural equation modeling (SEM) as a novel approach to reveal causal relationships among key reproductive traits. Genetic diversity was evaluated in 200 spring wheat genotypes grown under optimal and heat-stressed environments across two consecutive growing seasons. The traits studied included pollen viability (PV), grain number per spike (GpS), spikelet fertility (SpF), fertile florets per spikelet (FFpSp), grain filling duration (GFD), and anther length (AL). Notably, GpS and AL showed high heritability and genetic advancement in both environments, indicating a strong genetic influence and good potential for selection. SEM analysis revealed that SpF was the major direct contributor to GpS under normal conditions, whereas PV was the primary contributor under heat stress—an association not previously well documented in the literature. These findings were supported by principal component analysis (PCA), which showed that the first and second principal components explained 53.1% and 56.3% of the total variance in the first and second years, respectively. Trait correlations differed between environments: under normal conditions, GFD and GpS were closely aligned, while under heat stress, PV, SpF, and FFpSp clustered together, indicating a coordinated physiological response to stress. The results emphasize PV as a critical trait for selecting heat-tolerant wheat lines. Furthermore, three genotypes—Chenab-70, Pak-81, and Frontana—showed consistent tolerance across environments, providing valuable genetic resources for breeding climate-resilient wheat. This study presents a comprehensive analytic model integrating SEM and PCA to improve the accuracy of selecting physiological traits in wheat breeding under terminal heat stress.