Association Analysis for Important Quantitative and Morphological Traits in Cultivars and Advanced Lines of Soybean (Glycine max (L.)) using Microsatellite Markers

IExtended Abstract Introduction and Objective: The economic value of a genotype depends on its various traits and therefore the accurate knowledge of genetic behavior and identification of genomic locus involved in controlling these traits can help the breeder to improve genotypes. Material and Methods: In this study, the relationship between microsatellite markers with some important agronomic and morphological traits in 130 different soybean genotypes through communication models, including the general linear model (GLM) and the mixed linear model (MLM) in 2015 was evaluated. The studied traits were including plant height, pod length, number of pod per plant, number of grain per pod, number of grain per plant, pod weight, grain weight, 100-grain weight and grain yield. Results: According to the molecular diversity among the 130 studied genotypes on the basis of microsatellite markers, 87 alleles were amplified by markers, of which 85 alleles (97.7%) were polymorph. Results of allelic diversity revealed averace number of effective alleles, shannonsʹs index, polymorphic information content and gene diversity Nei equal to 1.7, 0.52, 0.62 and 0.48 respectively. The results of structural analysis showed that there were three sub-populations (k=3) in the studied germplasm, so that from the 130 soybean genotypes, 34 (26.15%) belonged to first structure, 43 (33.07%) to second structure, 44 (33.84%) to third structure and nine genotypes (6.92%) to mixed structure, which were confirmed by the results of the bar plot. The association analysis of microsatellite markers with traits under a mixed linear model (MLM) indicated that 33 and 35 markers had significant relationships with the evaluated characteristics. Some microsatellite markers had significant relationships with the several traits (e.g. the relationships between Satt460 marker with plant height, pod length, number of grain per pod, and 100-grain weight), which can be due to either the pleiotropic effects or the tight linkage of genomic regions controlling these traits. The relationship between several markers with a specific trait (for example relationship between Sct_028, Sat_124, Satt359 and Satt361 markers with trait of grain yield) indicating the quantitative and polygenic inherentance of the evaluated traits. Conclusion: The results of this research showed that in addition to using markers as useful markers in assessing genetic diversity in soybeans, markers that were more closely related to the genomic regions controlling these traits, to modify the population by marker assisted selection.