Association mapping of physiological and biochemical traits of wheat using SNP markers under optimal and zinc deficiency stress conditions

IntroductionEnvironmental stresses such as nutrients deficiency stress are serious threats to agricultural products. Zinc is one of the low-consumption essential nutrients but with high nutritional value, which plays an important role in root growth, increasing yield, plant resistance to diseases, photosynthesis, cell membrane integrity, pollen formation, energy production and increasing antioxidant enzymes and chlorophyll in plant tissues. Furthermore, zinc is essential for production of plant hormones such as abscisic acid, auxin, gibberellins and cytokinin, and its deficiency causes disruption in plant cell reprodution. Wheat grain contains zinc about from 20 to 30 mg/kg. About 50% of the soils used for cereal production in the world do not have enough usable zinc. One of the strategies to compensate for zinc deficiency is to improve zinc-efficient cultivars. Therefore, it is necessary to conduct basic research to identify the genes controlling zinc. In the current research, 64 spring wheat cultivars were studied under normal and zinc deficiency stress conditions, and the objective of this experiment was to identify genomic locations controlling phenological, physiological and biochemical characteristics using LD-based GWAS method based on SNP markers.Materials and methods To genome-wide association study of yield and physiological and biochemical traits in Iranian bread wheat varieties, 64 varieties of spring wheat were cultivated as a pot experiment in a simple lattice design under two normal and zinc deficiency stress conditions in the research farm of Faculty of Agriculture, Urmia University, Urmia, Iran. The studied traits include days to germination, days to booting, days to pollination, days to physiological maturity, grain filling period, canopy temperature, total chlorophyll, leaf area index, fresh and dry shoot weight, relative water content, shoot zinc concentration, grain protein concentration and grain yield. Genotypic evaluation of the population was performed using 36360 SNP markers. To determine the population structure, principal component analysis (PCA) was used and PCA results were considered as covariate variables instead of the Q matrix for association analysis. For association analysis and identification of linked markers to the genes controlling the studied traits, GLM and MLM methods were used and significant marker-trait associations (MTAs) were separately identified for each of the experimental conditions. Research findingsThe results of association analysis using the GLM method identified 145 marker-trait associations (MTAs) under normal conditions and 135 MTAs under zinc deficiency stress conditions, while using the MLM method, 165 and 142 MTAs were identified under normal and zinc deficiency stress conditions, respectively. The highest and lowest number of significant marker-trait associations with both GLM and MLM methods under normal conditions were identified for dry weight and grain filling period, respectively, while under zinc deficiency stress conditions, the highest number of significant MTA with both association analysis methods was observed for leaf relative water content and the lowest number of MTA was observed for grain protein content and shoot zinc concentration. Identified markers can be used in breeding methods such as selection with in breeding programs. The significant marker-trait associations (MTAs) identified in this experiment can be used to increase the efficiency of breeding programs using the marker-assisted selection.ConclusionThe results of the present study showed the efficiency of the association analysis method as well as the GLM and MLM models in identifying markers linked to evaluated traits in wheat. The information obtained from this experiment also showed that SNP markers are a powerful tool for evaluating genetic diversity and preparing the structure of wheat populations and can be used in breeding programs via marker-assisted selection. Although, it is necessary to investigate the markers identified in larger populations to ensure their relationship with the studied traits. In this study, several common and similar gene loci were also identified for the studied traits, which can be used for the simultaneous multi-traits selection in future breeding programs.