| Oilseed rape is one of the most important vegetable oils worldwide. The critical breeding objective in rapeseed is obviously for high oil production per unit area. Thus, the conduction of research focusing QTL analysis on seed oil content and yield related traits set up the basis for developing molecμlar breeding strategy.Based on the previously constructed popμlation of SGDH-14/Express617 DH at institute of plant Breeding, Goettingen University, Germany, we performed QTL analysis referring the oil content, silique length, seed number per silique, silique density and thousand seeds weight using the phenotypic data from six environments and new developed SGE map. Further, in order to fine mapping the two major QTL for oil content in linkage group 6 (qQilA6) and silique length/seeds per silique on linkage group A8(qSSA8 and qSLA8), we developed a large number of locus specific markers and integrated into the target QTL regions, thus greatly reduced confidential intervals. The main resμlts are summarized as following:1. Among five traits, only seed number per silique showed significant difference between two parents (SGDH-14 and Express617), the other four traits displayed no significant difference between parents. In the DH popμlation, the five traits showed continuous distribution in all environments, indicating that they were typical quantitative traits.2. Mutual correlation were found among oil content. Silique length, seed number per silique,silique density and thousand-seeds weight. Silique density was significantly positive correlated with thousand seed weight and with oil content(P<0.01), while showed negative correlation with seed number per silique. Silique length was positively correlated with seed number per silique but negatively correlated with oil content. Seed number per silique were negatively correlated with oil content and thousand seed weight.3. Using the composite interval mapping approach, a genome-wide scan for QTL was conducted. A total of 22 QTLs for the 5 traits were detected in six environments. For Seed number per silique (SS),3 QTLs were detected on LGs:A5, A6 and A8, with the individual QTL explaining 4.6%-14.2% of the phenotypic variation. For thousand seed weight(SW),4QTLs were detected on LGs:A6, A7, A9 and C8, with the individual QTL explaining 5.0%-11.3% of the phenotypic variation. For Seed density (SD),4 QTLs were detected on LGs:Al, A2, C3 and C6, with the individual QTL explaining 5.6%-7.6% of the phenotypic variation. For oil content (Oilc),6 QTLs were detected on LGs:A6, A7, A8, C3 and C5, with the individual QTL explaining 3.6%-17.9% of the phenotypic variation.4. Based on the constructed SGE map, using Brassica.rapa genomic sequence resources, we developed locus specific markers for LGs A6 and A8.29 markers integrated to the LG A6,which make the average distance among markers decrease from 8.4 cM to 2.5 cM.12 markers integrated to the LG A8, the average distance decrease from 4.8 cM to 1.4 cM. Using the new integrated A6 and A8 maps, mapping data of oil contend, silique length and seed number per silique over six locations were re-analysed in SGE popμlation. The resμlt showed that the QTL regions for qOilA6 reduced from 15.9 cM to 7.9 cM, the QTL regions for qSLA8 reduced from 6.0cM to 4.8 cM. and the QTL regions for qSSA8 reduced from 6.0cM to 5.7 cM.5. Comparative common QTL analysis in SG population and SGE population:qSSA6, qOilA7, qOilC3 and qSWA7 were detected in both popμlations, and addictive effect of the similar QTLs are usually the same. We conclude that the similar QTLs can be controlled by the same gene; Compared with other studies, the seed number per silique QTL on LG A5 and the silique length QTL on LG A10 were less detected. The rest of QTLs for oil content and yield traits and other studies are comparable.6. Association between QTL genotypes finking four related QTLs and the corresponding phenotypes of Oil content and yield related traits indicated that combination of high oil content alleles from two QTL loci (qOilA6 and qOilC5) by marker assiatant selection of A6Na-36, A6SS-51, A6SS-28, Bnp50816, brPb840901, brPb810005 and Bnp269557 singnificantly improved the oil content. Long silique length alleles from qSLA8 loci by marker assistant selection of A8-72, CB10364, A8-79 and BRAS039 increased the silique length. More seed per silique alleles from qSSA6 loci by marker ssistant selection of Bnp23776151 and E32M47-241D increased the seed per silique. Taken together, we suggest the importance of these QTL and markers for yield breeding purpose in Brassica napus. |
PDF Full Text Request