Detection of quantitative genes controlling of metabolic in rice seedling under salinity stress

Introduction Rice (Oryza sativa L.) is one of the most important crops in the world, especially in Asian countries that provides energy for more than 2.7 billion people worldwide daily, and is planted on approximately one-tenth of the earth's arable land. Rice is one of the most important cereal and salinity is a major limitation in the development of rice cultivation. Genetically improving salt tolerance of rice is a highly important objective of rice breeding programs. Traits such as salt tolerance are quantitatively inherited. Therefore, mapping quantitative trait loci (QTL) with molecular markers can be very helpful to plant breeders in the field of agricultural genomics. Therefore, the present study was conducted to mapping of genotype code, starch, phenol, proline and chlorophyll content. Materials and methods A F8 RILs population, derived from a cross between a salt tolerance Ahlemi‌ Tarom (ATM) and salt sensitive Neda (NAD) which were used in this study. The genetic material involved 96 lines were used to evaluate the salt tolerance. This experiment was conducted at the Gonbad Kavous University at 2014 under hydroponics condition. The seeds were placed 50 ˚C for 3 days to break dormancy, and then germinated at 25 ˚C for four days. Finally, the germinated seeds were sown in holes of the Styrofoam board with a nylon net bottom and roots were placed in water. which floated on water for 3 days, and after were transferred to float on Yoshida's nutrient solution for 11 days. Two weeks after sowing, the seedlings were transferred to nutrient solution with electrical conductivity 6 dSm-1 for 7 days, then NaCl concentration was increased to 12 dSm-1 for further 7 days. This experiment was conducted in a controlled condition with 16-hours photoperiod, temperature of 29/21 ˚C, and minimum relative humidity of 70%. The culture solution was renewed weekly and pH solution was controlled three times a and was fixed constant by adding either NaOH or HCL. Chlorophyll content was measured using a SPAD device. The polymerase chain reaction was performed in a volume of 10 µL for each reaction. Polymerase chain reaction products were then separated using a 6% polyacrylamide gel and stained with fast nitrate of silver. The 96 lines Genetic linkage maps was prepared using 30 SSR markers and 15 ISSR markers covering 1411.3 cM of the rice genome. The average distance between two adjacent markers was 15.34 cM. Results and discussion For genotyping code, a QTL was detected on chromosome 7, explained 9.3% of phenotypic variation in the trait. For starch content, a gene locus was identified on chromosome 4, which had a LOD of 2.799. The additive effect for this QTL was 6.756 and explained 12.6% of the phenotypic variation in the trait. For the phenol content, a gene location was detected on chromosome 7, which explained 15.2% of the phenotypic variance of the trait, and had LOD and additive effect of 2.728 and 3.4338, respectively. The allels of the parents of Ahlemi‌ Tarom increased this trait. For a chlorophyll content, a QTL was detected on chromosome 5, with an LOD of 2.2. This QTL had an additive effect and a R2 of 0.097 and 9.2, respectively. Conclusion A total of four genetic locations were identified for four traits genotype code, starch, phenol, proline and chlorophyll content, respectively, on chromosomes 7, 4, 7 and 5, and explained 9.3, 12.6, 15.2 and 9.2 % of phenotypic variation of the traits. These locus had a LOD were 2.038, 2.799, 3.438 and 2.02, respectively. No QTL was detected for proline. The results of this study can identify the better genotypes in term of traits checked for marker selection programs.