Detection of quantitative genes controlling of chlorophyll content in rice seedling under normal and salinity stress and comparison of different QTL mapping methods

Introduction Rice (Oryza sativa L.) is a major source of food and energy for more than 2.7 billion people on a daily basis and is planted on approximately one-tenth of the earth's arable land. Rice is one of the most important cereal. Salinity is the second most problem next to drought, in rice growing areas of the world. Soil salinity is a abiotic stress in crop productivity worldwide. The aim of the present study is to identify QTLs related to salt tolerance by using an Iranian rice population and Comparison of different QTL mapping methods. 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 early crosss and segregated generations in the University of Gonbad Kavous were developed. The genetic material involved 96 lines were used to evaluate the salt tolerance. This experiment was conducted at the faculty of agriculture, university of Gonbad-Kavos, in 2016 as hydroponics. The seeds were placed 50 ˚C for 3 d to break dormancy, and then germinated at 25 ˚C for 72 hours. Finally, the germinated seeds were sown in holes of the Styrofoam board with a nylon net bottom, which floated on water for 3 d, and after were transferred to float on Yoshida's nutrient solution for 11 d. two week after sowing, the seedling 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-h photoperiod, temperature of 29/21 ˚C and minimum relative humidity of 70%. The culture solution was renewed weekly and the PH was adjusted daily to 5.5 by adding either NaOH or HCL. Chlorophyll content was measured using a SPAD device. 40 SSR primer pairs, 16 ISSR markers (76 alleles), two IRAP markers (7 alleles) and one iPBS marker (3 alleles) were appropriately distributed on 12 rice chromosomes. Finally, The genes controlling the chlorophyll content located using different QTL mapping methods in clouding SIM-MEL، SIM، CIM، MIM، PMLE، ICIM and STSIM. These methods detected different QTL. Results and discussion In CIM-MLE method, five QTL were detected on chromosomes 3, 5, 6, 7 and 8 in normal condition, and three QTL were detected on chromosome 2 and 6 in salt stress condition. In SIM method, three QTL were identified on chromosomes 2, 3 and 8 in normal condition, and five QTL were identified on chromosomes 4, 6, 7 and 10, in salt stress conditions. In CIM method, three QTL were detected on chromosomes 2, 3 and 8, these QTLs justifying 13-23% of the phenotypic change of trait, in normal condition, but under salt stress condition, six QTL were detected on chromosomes 1, 2, 6 and 7. qCHLN-3, qCHLN-5 and qCHLN-6 were detected by MIM method in normal condition and qCHL-6a and qCHL-6b were detected on chromosome 6 in salt stress condition. Six QTL were detected by PMLE method in normal condition and two QTL detected on chromosomes 6 and 9 in salt stress condition. In ICIM method, three QTL were identified on chromosomes 2, 3 and 8 in normal condition, in salt stress condition were detected five QTL on chromosomes 4, 6, 7 and 10. qCHLN-3, qCHLN-6 and qCHLN-7 were detected by STSIM method in normal condition, andqCHL-6on chromosome 6 and has a LOD of 3.187 and an additive effect of -0.079. Conclusions ICIM, CIM and SIM has most closely in genetic location in normal and salt stress conditions. qCHL-6 was identified in six location method at 52 cM position in chromosome 6. qCHLM-3 and qCHLN-8 were detected in CIM, CIM and SIM on chromosomes 3 and 8 and explaining 18-22% of phenotypic variance chlorophyll content in normal condition. CIM, ICIM and SIM method were detected QTLs on chromosomes 6 and 7. Among the methods used, the CIM has the least error in estimating the original QTL effect and it can be done at any point in the genome that is covered by markers and the performance of the markers is higher in this method. Therefore, the effectiveness of using the markers introduced in this method will be higher. The results of this study can identify the better genotypes in term of chlorophyll content for marker selection programs after validation of QTLs.