جستجوی مقالات مرتبط با کلیدواژه "ارتباط نشانگر-صفت" در نشریات گروه "کشاورزی"

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.

The current research was carried out to evaluate genetic diversity of 18 common bean cultivars and promising lines and to determine SSR and SCoT informative markers associated with 14 seed characteristics including the number of seeds per pod, 100 seed weight, seed length, seed width, the contents of crude protein, total soluble sugar, starch content, crude fat, iron, calcium, magnesium, zinc, uronic acid, and mineral ash. The polymorphism information content (PIC) values varied from 0.2 to 0.5 with an average of 0.39 for the SSR markers and from 0.19 to 0.42 with an average of 0.34 for the SCoT markers. The total average resolving power of SSR and SCoT markers were 1.54 and 5.34, respectively, indicating higher efficiency of SCoT markers than SSR markers for the diversity analysis. The common bean studied genotypes were clustered into three distinct groups for both markers based on the Complete Linkage method. Principal coordinate analysis (PCoA) for the SSR markers revealed that the first two principal components justified 59.05% of whole variation. For the SCoT marker, on the other hand, the fraction of variances explained by the first two principal components was equal to 25.43 indicating a better distribution of SCoT markers than SSR markers in the common bean genome. Analysis of molecular variance based on the grouping obtained from cluster analysis showed that the diversity within the group based on SSR and SCoT markers was equal to 89 and 78%, respectively. The results of regression analysis for the studied markers and the bean seed characteristics showed the existence of a significant relationships between a single marker with the several studied traits. This could indicate the association or linkage of the marker locations. Twelve out of 14 of the investigated common bean seed characteristics showed a significant relationship with at least one molecular marker.

Knowledge of genetic diversity and understanding the genetic architecture of complex traits in crop species can play a key role in exploiting genetic diversity and lead to crop cultivation's adaptation, development and expansion in different geographical environments. In this regard, identifying and utilizing informative markers related to quantitative traits is a prerequisite for application in breeding programs and marker-assisted selection (MAS). One of the major uses of these markers is the construction of genome-wide molecular maps and genetic analysis. In genome-wide association study (GWAS) and genomic selection (GS), determining of extent and level of linkage disequilibrium (LD) is critical in sample size and marker density. Moreover, selection for increasing frequency in new mutations advantageous only in a subset of populations leaves some signatures in the genome. Locations of selection signatures are often correlated with genes and QTLs affecting economically important traits. Access to next-generation sequencing technologies, high phenotypic data and a variety of sophisticated statistical tools have enabled LD-based association mapping studies in plants to identify gene loci controlling quantitative traits successfully. Alternatively, the LD-based association mapping (AM) approach can enhance the genetic map resolution to a greater extent due to the representation of a more comprehensive gene pool and more recombination events in history. Association mapping has emerged as a tool to resolve complex trait variation down to the sequence level by exploiting evolutionary recombination events at the population level. Genetic diversity, LD extent in genome and intra-population relationship, determine quality of mapping, marker diversity, statistical methods and power of mapping. LD in population is the foundation of GWAS, whereas it is always affected by genetic drift, population stratification and natural selection. Of the above threats, population stratification is recognized as a major one to the validity of GWAS results. Therefore, knowledge concerning the pattern of LD is essential for performing GWAS and GS. As one of the most important measures in population genetics, LD is the basis of effective population size estimation, genomic selection, GWAS study and QTL mapping. Due to the importance of LD method in mapping studies of the quantitative traits, in this review, we will present LD, its application in population, current status, limitations, and its use in plant breeding, especially in cereals. Finally, this study provides some information about commonly used statistical software and packages in the calculation of LD, and the challenges and perspectives of this approach have been discussed in this study as well.

In this research, 50 quinoa genotypes were investigated using 40 pairs of microsatellite markers (SSR) to identify the superior genotypes in terms of grain yield, the components affecting it, and the features linked to the studied traits were also introduced using Association Analysis. The study of grain yield traits and the factors affecting it showed that the traits of day to physiological maturity, panicle length, number of panicle per plant, and 1000 -grain weight were the most important traits affecting grain yield, and finally genotypes 1, 32 and 49 were the essential traits in terms of these traits. They were introduced as superior genotypes. Studying the population structure using Structure software showed two possible subgroups (K=2) in the studied population and the results of the bar plot confirmed it. The results of the correlation analysis using the general linear model (GLM) and the mixed linear model (MLM) numbers 49, 53 (GLM) and number 48, 52 (MLM) showed a significant indicator-adjective relationship, respectively, for the first year (2018) and the second (2019). Among the significant marker-trait relationships identified for different traits, the highest number of meaningful relationships were identified for grain yield and 1000-grain weight traits with 6 and 5 relationships, respectively. In conclusion, based on the results of the correlation analysis, 12 continuous markers were identified with a significant relationship with the evaluated traits during both years. Therefore, these markers can be used as fixed and stable markers in different quinoa breeding programs.