Mapping QTL For Yield-related Traits Based On DH Population In Brassica Rapa L.

Chinese cabbage (Brassica campestris L. ssp. pekenensis) is one of the most improtant vegetable crops in Crucifer Brassica, originated from China, which has the largest cultivation area. Complex genetic basis of this crop make it difficult to improve some important agronomic traits are quantitative trait for instance yield, quality, growth period and so on. It is necessary to exploit existing genetic variation to dissect agronomic and quality traits in B.rapa at molecular levels for utilising efficiently key genes in breeding. In our study, based on the SSR and SRAP markers, a genetic map was constructed for mapping the important agronomic traits. Marker analysis was performed on192doubled haploid (DH) lines obtained from F1progeny of two homozygous parents. The main results of the present study were as follows:1. A genetic linkage map of Chinese cabbage was constructed, which was based on a DH population with192inividuals. SSR and SRAP were adoptd and233markers including43SSR,190SRAP markers were integrated into the resulted map using Joinmap3.0version. This map consisted of10linages groups, covering1063.8cM with a mean marker intervalof4.6cM, the largest interval between markers was18.0cM. Various linkage groups were feature by14-32markers,38.2-150.5cM length. The10linkage maps can correspond to chromosomes by the anchor SSR markers.2. Based on a linkage map with233markers, QTLNetwork2.0is used to investigate QTL of the major traits related to yield using the data collected under two locations.46QTLs and7paisr of epistatic loci controlling11agronomic traits related to yield traits were mapped on10linkages. These QTLs included7for head length,1for head width,9for head length/head width ratio,4for gross weight,3for head weight,4for head weight/gross weight ratio,4for number of non-wrapper leaves,3for number of head-forming leaves,3for number of all leaves,6for head petiole weight, and2for head non-petiole leaves weight.3. The explained variances were different, ranged from4.85%-25.06%.22QTL can explain more than10.00%. In Shenyang, the detection of co-localised M-QTL occurred in four genomic regions. The first one was for1HLA1(Head length) and1HRA1(Head length/Head width ratio). The second one was for1GWA9a (Gross weight),1NHLA9(Number of head-forming leaves) and1HPWA9a (Head petiole weight). The third one was for1GWA9b (Gross weight),1HWTA9(Head weight) and1HPWA9b (Head petiole weight). The fourth one was for1GWA10(Gross weight) and1HWTA10(Head weight). In Dalian, the detection of co-localised M-QTL occurred on four regions, which were for2HLA1(Head length),2HRA1(Head length/Head width ratio) and2NNLA1(Number of non-wrapper leaves), for2HRA9(Head length/Head width ratio) and2HGRA9(Head weight/Gross weight ratio), for2GWA6(Gross weight) and2HWTA6(Head weight) and for2NHLA10(Number of head-forming leaves) and2NALA10(Number of all leaves).