High-density Genetic Map Construction And QTL Mapping Of Important Agronomic Traits In TN DH Population

After decades of development, new progresses have been got for improving the performance of agronomic traits in Brassica napus L., while there is still great potential remained. At present, the molecular improvement of crops depends on genetic mechanisms analysis of variation for these important agronomic traits, and QTL mapping is the first step to analyze these variations. High-density genetic linkage map from mapping population is the most important factor which influences the QTL mapping accuracy. High-throughput SNP chip brings opportunities for high-density linkage map construction.In this study, SNP based high-density genetic linkage map was constructed based on the6K SNP chip analysis for TN DH mapping population which included376DH lines. Meanwhile, phenotype variations were investigated for14important agronomic traits in Wuhan, including developmental traits, yield and yield-associated traits and seed quality traits. Then QTL mapping analysis was done for these traits. In this study, we tried to explore how large mapping population and high throughput SNP affect QTL mapping. The main results are as follows:(1) High-density genetic linkage map construction:A genetic linkage map including1629molecular markers was constructed by analyzing2413polymorphic molecular markers using software JoinMap4.0. Total genetic distance is2127.89cM, including1199SNP markers and430SSR markers. Meanwhile, SNP can provide more genetic information because each SNP marker carries sequence. Compared with786map and621map,1629map has almost twice in marker number and density.(2) Phenotype performance of14traits between parents and population:The phenotype performance and variation of14traits for parents and population is basically consistent with that got from previous’results. Phenotype variations of14traits in population show continuous distribution and transgressive segregation. Correlation analysis shows a high positive correlation among yield and yield-associated traits, but for seed quality traits both positive and negative correlation were found.(3) QTL mapping results and comparison with previous’QTL mapping results: One hundred ninety six QTLs for14traits were detected by using WinQTL cartographer2.5, including93significant QTLs and103suggestive QTLs. The number of QTL for flowering time is28. After compared with QTL mapping results in previous study,13QTLs were newly detected and4micro-real QTLs were found,11QTLs could be detected repeatedly. It indicated that QTL detection accuracy will be enhanced by using high-density genetic map, because most of QTLs that could be detected repeatedly in this study have less confidence interval than those got in previous’results.