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Öğe Genetic Diversity and Population Structure in Turkiye Bread Wheat Genotypes Revealed by Simple Sequence Repeats (SSR) Markers(Mdpi, 2023) Turkoglu, Aras; Haliloglu, Kamil; Mohammadi, Seyyed Abolgahasem; Ozturk, Ali; Bolouri, Parisa; Ozkan, Guller; Bocianowski, JanWheat genotypes should be improved through available germplasm genetic diversity to ensure food security. This study investigated the molecular diversity and population structure of a set of Turkiye bread wheat genotypes using 120 microsatellite markers. Based on the results, 651 polymorphic alleles were evaluated to determine genetic diversity and population structure. The number of alleles ranged from 2 to 19, with an average of 5.44 alleles per locus. Polymorphic information content (PIC) ranged from 0.031 to 0.915 with a mean of 0.43. In addition, the gene diversity index ranged from 0.03 to 0.92 with an average of 0.46. The expected heterozygosity ranged from 0.00 to 0.359 with a mean of 0.124. The unbiased expected heterozygosity ranged from 0.00 to 0.319 with an average of 0.112. The mean values of the number of effective alleles (Ne), genetic diversity of Nei (H) and Shannon's information index (I) were estimated at 1.190, 1.049 and 0.168, respectively. The highest genetic diversity (GD) was estimated between genotypes G1 and G27. In the UPGMA dendrogram, the 63 genotypes were grouped into three clusters. The three main coordinates were able to explain 12.64, 6.38 and 4.90% of genetic diversity, respectively. AMOVA revealed diversity within populations at 78% and between populations at 22%. The current populations were found to be highly structured. Model-based cluster analyses classified the 63 genotypes studied into three subpopulations. The values of F-statistic (Fst) for the identified subpopulations were 0.253, 0.330 and 0.244, respectively. In addition, the expected values of heterozygosity (He) for these sub-populations were recorded as 0.45, 0.46 and 0.44, respectively. Therefore, SSR markers can be useful not only in genetic diversity and association analysis of wheat but also in its germplasm for various agronomic traits or mechanisms of tolerance to environmental stresses.Öğe Identification of Novel QTLs Associated with Frost Tolerance in Winter Wheat (Triticum aestivum L.)(Mdpi, 2023) Bolouri, Parisa; Haliloglu, Kamil; Mohammadi, Seyyed Abolghasem; Turkoglu, Aras; Ilhan, Emre; Niedbala, Gniewko; Szulc, PiotrLow temperature (cold) and freezing stress is a major problem during winter wheat growth. Low temperature tolerance (LT) is an important agronomic trait in winter wheat and determines the plants' ability to cope with below-freezing temperatures; thus, the development of cold-tolerant cultivars has become a major goal of breeding in various regions of the world. In this study, we sought to identify quantitative trait loci (QTL) using molecular markers related to freezing tolerance in winter. Thirty-four polymorphic markers among 425 SSR markers were obtained for the population, including 180 inbred lines of F-12 generation wheat, derived from crosses (Norstar x Zagros) after testing with parents. LT50 is used as an effective selection criterion for identifying frost-tolerance genotypes. The progeny of individual F-12 plants were used to evaluate LT50. Several QTLs related to wheat yield, including heading time period, 1000-seed weight, and number of surviving plants after overwintering, were identified. Single-marker analysis illustrated that four SSR markers with a total of 25% phenotypic variance determination were linked to LT50. Related QTLs were located on chromosomes 4A, 2B, and 3B. Common QTLs identified in two cropping seasons based on agronomical traits were two QTLs for heading time period, one QTL for 1000-seed weight, and six QTLs for number of surviving plants after overwintering. The four markers identified linked to LT50 significantly affected both LT50 and yield-related traits simultaneously. This is the first report to identify a major-effect QTL related to frost tolerance on chromosome 4A by the marker XGWM160. It is possible that some QTLs are closely related to pleiotropic effects that control two or more traits simultaneously, and this feature can be used as a factor to select frost-resistant lines in plant breeding programs.Öğe Modeling Callus Induction and Regeneration in Hypocotyl Explant of Fodder Pea (Pisum sativum var. arvense L.) Using Machine Learning Algorithm Method(Mdpi, 2023) Turkoglu, Aras; Bolouri, Parisa; Haliloglu, Kamil; Eren, Baris; Demirel, Fatih; Isik, Muhammet Islam; Piekutowska, MagdalenaA comprehensive understanding of genetic diversity and the categorization of germplasm is important to effectively identify appropriate parental candidates for the goal of breeding. It is necessary to have a technique of tissue culture that is both effective and reproducible to perform genetic engineering on fodder pea genotypes (Pisum sativum var. arvense L.). In this investigation, the genetic diversity of forty-two fodder pea genotypes was assessed based on their ability of callus induction (CI), the percentage of embryogenic callus by explant number (ECNEP), the percentage of responding embryogenic calluses by explant number (RECNEP), the number of somatic embryogenesis (NSE), the number of responding somatic embryogenesis (RSE), the regeneration efficiency (RE), and the number of regenerated plantlets (NRP). The findings of the ANOVA showed that there were significant differences (p < 0.001) between the genotypes for all in vitro parameters. The method of principal component analysis (PCA) was used to study the correlations that exist between the factors associated with tissue culture. While RE and NRP variables were most strongly associated with Do & gbreve;ruyol, Ova & ccedil;evirme-4, Do & scedil;eli-1, Yolge & ccedil;mez, and Incili-3 genotypes, RECNEP, NSE, RDE, and RECNEP variables were strongly associated with Avc & imath;lar, Ova & ccedil;evirme-3, and Ardahan Merkez-2 genotypes. The in vitro process is a complex multivariate process and more robust analyses are needed for linear and nonlinear parameters. Within the scope of this study, artificial neural network (ANN), random forest (RF), and multivariate adaptive regression spline (MARS) algorithms were used for RE estimation, and these algorithms were also compared. The results that we acquired from our research led us to the conclusion that the employed ANN-multilayer perceptron (ANN-MLP) model (R-2 = 0.941) performs better than the RF model (R-2 = 0.754) and the MARS model (R-2 = 0.214). Despite this, it has been shown that the RF model is capable of accurately predicting RE in the early stages of the in vitro process. The current work is an inquiry regarding the use of RF, MARS, and ANN models in plant tissue culture, and it indicates the possibilities of application in a variety of economically important fodder peas.