Association analysis of agronomic traits using microsatellite markers (SSR) in different rice genotypes in terms of flooding and drought

Rice is an important crop that is considered a staple meal for 2.7 billion people worldwide. Therefore, the demand for it will increase with the increase of population. Environmental constraints always pose a serious threat to crop production, including rice. Drought is one of the most important challenges that limits the production of high-yielding cultivars in arid and rainfed areas. Global warming has also become a factor in limiting rice production in rain-dependent areas. Therefore, researchers are looking for a way to stabilize rice production in arid regions. In this study, informative markers related to the desired agronomic traits were identified in 59 rice genotypes using microsatellite marking system.

In order to evaluate the tolerance of rice genotypes to drought stress and to identify tolerant and sensitive genotypes, 59 genotypes received from the National Rice Research Institute and the International Rice Research Institute in a randomized complete block design with three replications in two separate conditions, without Stress (flood) and drought stress were performed in a research farm located in Aliabad Katoul city in 2013. In both conditions (normal and drought stress), the genotypes were planted in five rows of 25 × 25 cm in rows one meter long. Thirty days after planting in the nursery, healthy and strong seedlings were transferred to the main land. The required agronomic operations were carried out equally during the growth and development period of the plants under stress and normal conditions and only in terms of irrigation of the experimental field in both flood and stress environments, until the tillering stage of the cultivars were equally flooded. Then, to create stress, irrigation was done from 40 days after transplanting (maximum tillering stage) to the end of the growing season at 25-day intervals. Phenotypic values of grain yield and 1000-grain weight were measured under two conditions according to standard guidelines for evaluation of traits in rice. In order to investigate the relationship between agronomic traits and microsatellite markers with 59 rice genotypes out of 36 microsatellite molecular markers were performed in the Plant Breeding and Genetics Laboratory of Gonbad Kavous University, Faculty of Agriculture and Natural Resources. Young leaves of 21-day-old seedlings were extracted in four-leaf stage using CTAB method. Touchdown PCR reaction was studied and evaluated randomly using 36 microsatellite primers for 3 markers from each chromosome. To separate PCR products, 6% polyacrylamide gel electrophoresis was used and to reveal the banding pattern, silver nitrate staining method was used. The content of polymorphic information was calculated. The relationship between molecular data and traits of studied rice genotypes was investigated using multiple regression. Thus, each quantitative trait was considered as a dependent variable and microsatellite markers were considered as independent variables.

The average content of polymorphic corrections (PIC) was estimated to be 0.58, which showed RM 5647 with 0.81 the highest and RM 6022 with 0.32 the lowest polymorphism (PIC). The results of stepwise regression analysis showed that a total of 90 markers for normal conditions and 69 markers for drought stress conditions for morphological traits were identified. Under normal conditions, the number of spikes and the number of days to flowering with 9 markers and under drought stress, the weight of the cluster with 9 markers showed the most positive markers. The most explanation for variation in normal conditions is related to the total number of grains (0.83) by gene loci RM6324-E, RM5652-E, RM5761-D, RM6179-F, RM549-B, RM462-B, RM7420-D Explained. In drought stress conditions, the most explanation for variation related to panicle weight (0.70) by gene loci RM519-D, RM7545-A, RM6179-E, RM7118-G, RM3525-B, RM5761-B, RM38-C, RM7091-A, RM5647-B explained.

The results showed that some markers are associated with more than one trait, which indicates that these traits are very closely related to each other or may be influenced by multi-effect genes. To understand this, it is necessary to develop transgressive generations and linkage.