QTL Mapping For Lignocellulose Content In The Stem And Seeds Of Brassica Napus

B. napus is one of the most important oil crops in china. Its seeds can be used to produce biodiesel, at the mean time, its stem can be used to produce bioethanol as lignocellulosic material. For bioethanol production, so far, lignin degradation is still a barrier to make lignocellulose-derived ethanol a cost-competitive process. For biosiesel production, oilseed yield cannot satisfy demand. Lignocellulose is composed of lignin, cellulose and hemicellulose. In order to improve bioethanol production efficiency to reduce cost, dissecting genes controlling lignocellulose biosynthesis to make lignin degrade more easily or decrease total lignin content in plants is meaningful. And at the same time, researches have proved that lignocellulose content has relationship with oilseed yield. Dissecting lignocellulose biosynthesis pathway at the molecular level may provid basic theory support to produce more clean and renewable energy efficiently. Lignocellulose content is a typical quantitative trait, so QTL mapping method can be used and a high-density GLM is considered as a key factor of increasing statistical power and precision for QTL identification, so abundant genetic test between molecular markers and population is necessary. In this research, we detected lignocellulose biosynthesis pathway based on a DH population containing 348 individuals. The results of this research were as follows:(1) GBM, SSR and SNP molecular markers were used to reconstruct the initial GLM. Finally, a resulting map containing 2914 molecular markers was reconstructed, covering a total length of 2306.25 cM. What more important is that the marker interval decreased to 0.79 cM.(2) Based on the density increased GLM, 51 and 76 identified QTLs were detected for lignocellulose content in the stem and seed respetively, and the phenotype variance that each QTL can explain was 4.36%-20.03% and 2.66-42.72%, and the number of major effect QTLs was 4 and 19, respectively. After integration, 109 consensus QTLs were obtained and 11 QTLs can be detected across at least 2 environments. And 10 unique QTLs which may control at least 2 traits were obtained due to the overlapping confidence interval of consensus QTLs.(3) Compared QTLs detected based on the initial and new GLM, the total number of QTLs obtained based on the new GLM increased from 60 to 127, and the mean value of confidence interval decreased from 12.23 cM to 3.85 cM, which suggested we obtained more precise QTL. Third, a GBM was found located in the confidence interval of one QTL for lignin and based on this we found a candidate gene BnaA10g03880 D that may control lignin biosynthesis in the stem of B. napus.(4) To utilize QTLs that we obtained, comparative mapping method was used to identify candidate gens and finally, 1485 candidate B. napus genes were obtained. Among these, 4 genes were firstly chosen to peform further analysis after carrying out comparativemapping with Arabidopsis.