Genome-wide association study (GWAS), which depends on the structure of linkage disequilibrium of alleles at different loci, is a powerful strategy for accurate identification and fine mapping of genomic regions underlying quantitative traits (Abou-Elwafa and Shehzad 2018; Simko et al. 2006). This approach is more efficient when the purpose is to identify quantitative traits associated with a single marker (Abou-Elwafa 2016b; Doerge 2002). However, the choice of a population is a pivotal factor in determining the resolution of association analysis in plants. So that, the selected population should exhibit a high degree of diversity with more extensive historical recombination to allow the detection of more alleles (Abou-Elwafa 2016a; Shehzad et al. 2009).
In the current study, the TASSEL software that implements a fixed-effects linear model was employed to identify the association between SNP marker alleles and agronomic quantitative traits under two levels of soil salinity in the highly genetically diverse Wheat Association Mapping Initiative (WAMI) population that is consists of 289 elite breeding lines (Abou-Elwafa and Shehzad 2020). Each association between the SNP markers and phenotypic traits was performed individually. Structure analysis of the WAMI population revealed significant genetic variations either under low or high salinity conditions which are necessary for effective selection for genotypes with improved salinity tolerance. The continuous variations observed in the evaluated agronomic traits phenotypic under both low and high salinity conditions over the two growing seasons indicate polygenic inheritance of all evaluated traits. Besides, the agronomic traits overall evaluated genotypes respond differently to salinity conditions, emphasizing the adverse impacts of high salinity on wheat crop. The linear regression relationship observed between grain yield per plant under low salinity conditions (GYPp) and salt-stressed conditions (GYPs) indicates that indirect selection for salinity tolerance based on the high-yielding potential under low salinity conditions might be efficient (Talebi et al. 2009).
Salt stress tolerance indices were employed for the identification of high-yielding wheat elite lines under low and high salinity conditions. Four stress tolerance indices, i.e., STI, TOL, SSI and YSI, were employed for the identification of high-yielding wheat elite lines under either low and high salinity conditions. Noteworthy, STI is the only salt-stress tolerance index (STI) that revealed positive correlations with the average grain yield per plant under either low and high salinity conditions over the two growing seasons, indicating that STI is the best stress tolerance index that could be efficiently employed for prediction of high-yielding wheat genotypes under salt-stressed conditions. The results further indicate that employing the other stress tolerance indices, i.e., SSI, TOL, and YSI, in prediction for high productivity may not be efficient under variable environmental conditions (Abou-Elwafa and Shehzad 2020). Besides, the linear regression relationship detected between STI and the average grain yield per plant under high salinity conditions over the two growing seasons emphasizes that STI might be employed as a powerful selection index for prediction and selection of high-yielding genotypes under salinity stress conditions. Interestingly, none of the other estimated stress indices, i.e., TOL, SSI or YSI, revealed such a linear regression relationship when plotted against grain yield per plant (Fig. 6), indicating that none of those indices could not be successfully implemented for prediction and selection of high-yielding genotypes under salt-stressed conditions.
Genome-wide association analysis using 15,737 SNP markers identifies 238 significant associations between SNP markers and the seven evaluated traits. The 11 and 28 significant associations identified between the number of days to heading (HD) and SNP markers under low and high salinity conditions, respectively, were clustered to chromosomes 1A, 1B, 7B and 5A. A major heading date QTL (Qhd.4W-5A.1) co-localized with the two major vernalization genes Vrn-A1 and Vrn-A2 on chromosome 5A has been identified (Guedira et al. 2016). Besides, a major cluster of molecular markers located to chromosome 5A has been reported to be significantly associated with heading date (Abou-Elwafa and Shehzad 2020). Additionally, a major heading date QTL associated with the primary photoperiod gene Ppd-B3 on chromosome 7B has been identified and genetically mapped (Fowler et al. 2016). The significant associations between LCC and SNP markers were mainly clustered to chromosomes 2B and 7A. Furthermore, a major genomic region associated with LCC under either low and high salinity conditions located to the telomeric region of chromosome 7A, emphasizing that the LCC is mainly controlled by genetic factors (Dai et al. 2016) which is consistent with the high degree of heritability estimated for this trait (75–88%) under either low and high salinity across the two growing seasons (Table 1). Moreover, a major QTL associated with chlorophyll b designated qChla7A was identified on chromosome 7A (Zhang et al. 2009). SNP markers significantly associated with PH were located to specific genomic regions on chromosomes 2B, 3A, 4B, 5A and 6B. Four genomic regions located on chromosomes 2B, 3A and 6B were significantly associated with PH under either LS and HS conditions, indicating that plant height is mainly controlled by genetic effect. The high heritability degrees observed for plant height (77–83%) under all environmental conditions (Table 1) support this finding. Several PH-associated QTLs have been identified on chromosome 3A (Ali et al. 2011; El-Feki et al. 2018; Rustgi et al. 2013), however, majors plant height QTLs were identified on chromosome 4B (Zhang et al. 2018). The SNP marker RAC875_c24550_1150 located to chromosome 4B and associated with PH under high salinity conditions (Suppl. Table 6) has been previously identified to be associated with plant height QTLs under different environmental conditions (Abou-Elwafa and Shehzad 2020; Zou et al. 2017). Besides, the identification of a cluster of markers on chromosome 4B that are significantly associated with PH is consistent with the identification of a cluster of the gibberellic acid-insensitive reduced height genes (Rht1) that control plant height on chromosome 4B (Cabral et al. 2018; El-Feki et al. 2018). Furthermore, the identification of a cluster of markers co-localized with the genomic region of the Vrn-A1 gene that has a reducing effect on plant height (Chu et al. 2008), suggesting that Vrn-A1 might be a promising candidate for this QTL. A major cluster of the markers significantly associated spike number/ plant (SN) resides chromosome 6A which is consistent with the identification of a genomic in close vicinity to the major tillering promoter Gli-A2 (a multigene protein family gliadin) on chromosome 6A (Li et al. 2002). Furthermore, a major spike number related QTL was mapped to chromosome 7B (Shah et al. 1999) at which three significant marker-trait associations were identified for SN in our study. SNP markers significantly associated with SL were mainly clustered to chromosomes 2A, 2B, 3B, 3D, 5B and 7B, which is consistent with the results of (El-Feki et al. 2018) where 10 spike length QTLs were detected, three of which were mapped to chromosomes 2B, 3D and 7B. A major SL-associated QTL designated (QEl.fcu-3D; (Chu et al. 2008)was mapped to the short arm of chromosome 3D where a major cluster of 13 markers were identified to be significantly associated with SL under low salinity conditions in this study. Moreover, our results are in agreement with the identification of three SL-associated QTLs, i.e., QSl.cau-2B.2, QSl.cau-7B.1 and QSl.cau-7B.2, which are stable across different environmental conditions, on chromosomes 2B and 7B, respectively (Zhai et al. 2016). Additional verification findings for the importance of chromosomes 2B and 7B in the control of SL in wheat are the identification of two common loci associated with SL on chromosomes 2B and 7B, with the chromosome 7B locus was significant and stable across five environments (Li et al. 2019).
Markers revealed significant associations with grain yield per plant (GYP) were mainly clustered to the genomic region of 88–97 cM of chromosome 2B under low salinity conditions, two of which, i.e., wsnp_Ex_c20786_29875033 and BS00003404_51, are associated with SL, whereas under salt-stressed conditions five of the SL significantly associated markers located to chromosomes 2A are also significantly associated with SN. The significant association of markers with more than one phenotypic trait indicates possible pleiotropic or indirect effects of the QTLs harboring those markers. In a similar GWAS, 12 significant associations between SNP markers and grain yield were identified on, from which 2 are located to chromosomes 2A and 2B (Li et al. 2019). The SNP markers significantly associated with TKW under low and high salinity conditions were clustered mainly to chromosomes 1B, 2A and 6A. Two markers, i.e., RFL_Contig785_535 and RFL_Contig785_1700, are located to chromosome 1B and explaining 8.22–13.25% of the phenotypic variance in this trait were identified under both the normal and the stressed environments. Significant associations between those two markers and TKW were previously identified in a GWAS using the same WAMI population (Sukumaran et al. 2018). A major QTL significantly associated with marker BS00023092_51 under the salt-stressed conditions that is closely linked to the marker BS00036878_51 on chromosome 6A identified previously to be associated with TKW-QTL (Zou et al. 2017). In addition, the genomic region of chromosome 6A (78–85 cM) that harbors 7 SNP markers (BS00023092_51, Tdurum_contig76709_195, Excalibur_c56264_188, BobWhite_c17086_197, IAAV1263, BobWhite_c1082_134 and Tdurum_contig47663_321) has been earlier identified to harbor a major QTL for TGW (Sukumaran et al. 2018).
In conclusion, GWAS employing the TASSEL software revealed the identification of 238significant associations among SNP markers and seven phenotypic traits. Besides, the results revealed the significant associations of some SNP markers with more than one phenotypic trait, indicating possible pleiotropic or indirect effects of the QTLs associated with those markers. Our study identified significant associations between SNP markers and phenotypic traits with a high degree of genetic diversity controlling different mechanisms for adaptation to high salinity conditions. The high degree of significant linkage disequilibrium (> 52%) observed among SNP markers on different chromosomes indicates epistatic interaction. The results present the WAMI population as a valuable source for improving yield potential for salt tolerance in wheat. Furthermore, our findings emphasize that GWAS is a powerful tool in promoting wheat breeding through accurate identification of molecular markers significantly associated with agronomic traits, which is essential for marker-assisted breeding.