The characterization of different germplasms of cotton using iPBS markers and the association of these markers with wilt diseases

Cotton (Gossypium spp.) cultivation is significantly affected by wilt diseases, caused by Verticillium dahliae Kleb and Fusarium oxysporum f. sp. vasinfectum. This study was conducted using 128 cotton genotypes,belonging to the Gossypium ssp L., collected from the different regions of the world. The tolerances of cotton genotypes to the wilt diseases were investigated by using inter primer binding site (iPBS) molecular markers. Also, tolerances of the genotypes to wilt diseases were associated with the markers used in the study. The trials were conducted with augmented trial design under naturally infested trial fields of Faculty of Agriculture at Dicle University, Diyarbakir, Turkey, between 2019 and 2020 growing seasons. The disease scoring was made at two different time periods according to the disease severity indices. The first disease evaluation was performed at 50-60% of ball opening period of cotton, based on 0-4 scale, whilst the second disease evaluation was carried out relying on 0-3 scale, after harvesting, according to color alterations in cross-sections of stems. As a result, 286 polymorphic bands were identified, and rate of polymorphism was found 99.64 %. Polymorphic information content of markers (PIC), the average gene diversity (H), Shannon index (I), effective number of alleles (NE), and overall gene diversity (HT) were calculated as in turn 0.30, 0.29, 0.44. 1,48 0,27. Nei's genetic distance of cotton genotypes were between 0.0574 and 0.996, and the average distance among the genotypes was found 0.325. whereas 128th (Dp-499) and 42nd (Giza7) genotypes were the farthest cotton genotypes (0,996), the closest distance (0.0574) was identified between 126th (Es-1) and 125th (St-468) genotypes. Phylogenetic analysis by UPGMA clustering method indicated that 128 cotton genotypes were separated into two main groups, called A and B. Only three genotypes (Giza7 with number 42, Nazili87 with 72 number, and Agala sindou with number 2) were present in the group A whereas 125 other genotypes were in the group B. The main group B was divided into two subclades called B1 and B2. B1 was consisted of only one genotype (Aşkabat100 with number 7) whereas all other cotton genotypes were clustered under B2 subclade. According to Population structure analysis, the studied 128 cotton genotypes were consisted of four subpopulations (K=4). 35 (27.34%), 26 (20.31%), 29 (22.66%), and 22 (17.19%) of 128 cotton genotypes were clustered in A, B, C, and D subpopulations, respectively. 16 genotypes (12.5%) were grouped as admix according to 80% of belongingness coefficient. Markers used in the study were associated with phenotypic traits of interest according to Association mapping generated using General Linear Model (GLM) and Mixed Linear Model (MLM) methods. According to MLM, whereas nine markers were associated with 20 loci in 50-60% ball opening period (p<0.05) in the leaves, four markers were found to be associated with four loci (P<0.01). The phenotypic variations (r2) of these significant marker-trait associations were found to change between 0.0125-0.1879. Based on the result of GLM analysis, conducted in 50-60% ball opening period (p<0.05) in the leaves, whereas nine markers along with all other markers were found to be linked to 51 loci, six markers were associated with 12 loci. The r2 of the significant marker-trait associations identified in GLM analysis were between 0.0973-0.078. Markers linked to wilt diseases in the cross-sections of cotton stems were also investigated in the study. The MLM results indicated that 10 markers together with all other markers were linked to 18 loci (P<0.05) whilst only four markers were associated with four loci (P<0.01). r2 values were between 0.1043-0.1771. In GLM analysis, no markers are found to be associated with any loci in terms of wilt diseases. In conclusion, the loci, significantly associated with wilt diseases, were identified by available markers in the present study. These markers are suggested to be used for Marker-Assisted Selection (MAS) as well.