چكيده لاتين :
Background and objective Rapeseed ( Brassica napus L.) is a major crop cultivated worldwide mainly for oil, human consumption and renewable fuel. Plant growth is controlled by several factors, of which water plays a vital role. Large parts of the world are increasingly affected by drought. Drought stress is one of the most important abiotic factors which adversely affect growth, metabolism and yield of crops worldwide. Drought stress, during any particular growth stage of crops results in yield reduction. Under shortage of water, plants accumulate metabolites, such as sugars and inorganic ion, to regulate osmotic potential. Mixed linear model (MLM) methods have proven useful in controlling for population structure and relatedness within genome-wide association studies. Materials and methods In other to identify the informative markers of physiologic traits and indices of 22 canola genotypes under drought stress, 36 microsatellite markers linked to the QTLs controlling morphologic characteristics (shoot dry weight, total dry weight, root dry weight and plant height) under drought stress were used. In order to identify genomic regions involved in controlling physiological traits, GLM and MLM association models were used. Results and discussions Thirteen six selected primers produced 166 bands, with 157 (94.58%) being polymorphic, indicating considerable genetic diversity among lines and genotypes. The polymorphic information content values of loci was varied from 0.043 (CB10502) to 0.398 (CB10234), respectively. The highest PIC values were for CB10234, CB10143, SR94102, NA10-E02 and BRMS-030 primers, and the lowest for CB10502, Ol10-B02, CB1003, and FITO133 primers.The results of Mantel test showed that the correlation coefficient for stress and non-stress conditions was 0.024 and 0.06, respectively. According to this test, the correlation between physiological traits and molecular data matrix was not significant. Based on the 36 microsatellite markers used in this study, population genetic structure subdivided into eight subpopulations (K=8) that barplat results also confirmed it. In association analysis based on GLM and MLM models, 6 and 16 loci showed significant relation with assessed traits at non-stress conditions, respectively. Also based on GLM and MLM models, 42 and 46 loci indicated significant relation with assessed traits at drought stress conditions, respectively. In this study, 16 markers include Ol10-B02, BRAS084, Na10E02B, BRAS074a, CB10526, Na10-C01f, CB10003b, OL11-H06, FITO133, Na14-096, BRAS072a, CB10143, NA10-E02b, BRMS-031, CB10502, CB10010 hadn’t significant relationship with the physiological and morphological traits. The markers BRAS041, CB10597, BRMS-036, Sorf73b, CB10081c, MR013c, Na12-B05 and NA14-E08a were associated with the traits in non-stress conditions. Also, the markers CB10597, BRMS-036, SR94102, BRAS100, CB1059b, BRMS-024, BRMS-096, BRMS-30, MR013c and Pmr52 showed a significant relationship with physiological and morphological traits in drought stress conditions. Conclusion Finally, the markers BRMS-036, SR94102, BRAS100, CB1059b, BRMS-024, BRMS-096, BRMS-30, MR013c and Pmr52 showed the most significant relationship with physiological traits in drought stress conditions. Therefore, it is probably that these markers would be a very suitable and promising candidate for drought stress tolerance programs, such as marker assisted selection for canola. Also, probably the genes associated with these traits are near genes in the chromosomal location and may be useful in providing basic information about the indirect selection of traits through relevant markers
