| Sorghum (Sorghum bicolor (L.) Moench) is an important field crop widely cultivated in the world, which has"C4"photosynthetic system with high photosynthetic efficiency, and has multiple abiotic stress resistance like drought, lodging, barren land and salkine-alkaline. Sweet sorghum is a variant of common grain sorghum, which has taller and juicer stalks, and higher sugar content compared with grain sorghum. Sugar components in stem juice are predominantly sucrose, glucose and fructose, which can be easily fermented to ethanol. Therefore, sweet sorghum is designed as potentially dedicated energy crop. Using F2 individuals F2:3 progenies derived from a cross of normal sorghum inbred line Shihong 137 and sweet sorghum inbred line L-Tian, a molecular genetic linkage map was constructed with SSR markers, and energy-related traits including plant height, stem diameter, fresh stem and leaves weight, stem fresh weight, stem juice weight and brix in stem juice were QTL mapped. Genetic analysis of plant height and brix value by using major gene plus polygene mixed inheritance model was also conducted in order to providing foundation for marker assisted selection in sweet sorghum breeding and precise mapping of major effect QTL. Following main research results were obtained:1. A linkage map for sorghum spanned a distance of 1884.6 cM with an average distance between markers of 15.97cM was developed with 118 SSR markers, the 186 F2 individuals derived from a cross of normal sorghum inbred line Shihong 137 and sweet sorghum inbred line L-Tian.2. Based on the constructed linkage map, QTL mapping was conducted for six energy related traits of plant height (PHT), stem diameter (SD), fresh stem and leaf weight (SLFW), fresh stem weight (SFW), juice weight (JW) and sugar content (Brix) with composite interval mapping method using F2 and F2:3 populations. Seven major effect QTLs were identified with environment stability and higher phenotypic varance explained. Three QTLs controlling PHT were mapped on SBI-01, SBI-07 and SBI-09 across four environments, which explained 10.16% to 45.29% of phenotypic variance. Two major effect QTLs of PHT on SBI-07 and SBI-09 were consistently detected in four environments, and phenotypic variances were 21.56%-45.29% and 11.32 - 22.89%. Six QTLs controlling SD were mapped on SBI-01, SBI-07, SBI-08 and SBI-09 across three environments, and explained 6.47% to 33.55% of phenotypic variance. The QTL on SBI-07 located in the marker interval of SbAGF06 and Xcup19 could be detected in three environments, explained 11.03% to 17.65% of phenotypic variance, and was a major QTL for SD. Seven QTLs controlling SLFW were mapped on SBI-01, SBI-04, SBI-07, SBI-08 and SBI-09 across three environments, and explained 5.49% to 25.36% of phenotypic variance. QTL on SBI-09 was major effect QTL with higher phenotypic variance (16.17%-25.36%) , and could be detected in three environments. Five QTLs controlling SFW were mapped on SBI-01, SBI-07 and SBI-09 across two environments, and explained 8.65% to 21.52% of phenotypic variance. Each one QTL on SBI-07 and SBI-09 was detected in two environments. One major QTL on SBI-09 located in the marker interval of Sb5-206 and SbAGE03 was identified with higher phenotypic variance of 18.61% and 21.52%. Six QTLs controlling JW were mapped on SBI-01, SBI-04, SBI-07 and SBI-09 across two environments, and explained 7.19% to 23.26% of phenotypic variance. QTLs for JW on SBI-07 and SBI-09 were consistent in two environments showing higher environment stability. The QTL on SBI-09 located in the marker interval of Sb5-206 and SbAGE03 was one with major effect, and explained phenotypic variance 16.86% and 23.26%. Four QTLs for Brix were mapped on SBI-01, SBI-02, SBI-03 and SBI-07 across two environments and explained 11.03% to 17.65% of phenotypic variance. The QTL on SBI-03 could be detected consistently in two environments, was one with major effect, and explained phenotypic variance 11.03% and 17.65%.3. Two QTL clusters for traits collective expression were identified on SBI-07 and SBI-09. QTLs hot expression region on SBI-07 were affecting six tested traits, and additive effect were all from L-Tian except for SD. QTLs hot expression cluster on SBI-09 was affecting PHT, SD, SFLW, SFW and JW, and additive effect were also all from L-Tian, which was mainly partial dominant. Except QTL for SD, the other four QTLs were major effect QTLs. It was indicated that QTLs controlling PHT were simultaneously affecting the other energy related traits like SFLW and Brix, which further verified the pleiotropism of PHT loci.4. It was indicated that there was similarity for gene effect analysis for major gene plus polygene mixed inheritance model and QTL analysis, by comparing the results of joint generation analysis and QTL analysis for PHT and Brix. The difference between the two analysis method was that, QTL mapping could assign QTLs to detailed marker intervals on specific chromosome, whereas, the analysis with major gene plus polygene mixed inheritance model could only verify numbers of major genes, and gene effect of major gene(s) and/or polygenes. |
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