| Brassica juncea(AABB,2n=36)is an allotetraploid species of Brassicaceae family.It is used as a condiment,vegetables and as an oilseed crop especially in semiarid areas around the world.During the breeding history of Brassica juncea,more focus is given to improve yielding ability to meet the increasingly global demands.The present study was carried out to investigate different agronomic traits.To understand the genetic basis of agronomic traits,two parents(7H881 and YufengZC),were used to generate a set of bi-parental population consisting of 197 recombinant inbred lines(RILs).The RIL population and its parents were planted in two consecutive seasons(2019-2020)for phenotyping,and the data was integrated for QTL analysis.The main results are as follows:1.Twelve agronomic traits,including plant height(PH),main stem length(MSL),siliques on main stem(SMS),primary branches(PB),branch angle(BA),stem perimeter(SP),squaring time(ST),bolting time(BT),flowering time(FT),silique length(SL),seeds per silique(SdS)and 1000-seed weight(TSW)were investigated in2019 and 2020.All of those traits displayed a larger variation range and approximately normal distribution in Guiyang environment,which were suitable for further QTL analysis.In addition,the descriptive analysis and correlation analysis of those traits were also performed.2.A total of 1,272 ILP(intron length polymorphism)primers were used to screen the parental lines of 7H881 and YufengZC for polymorphic primers,and 304(24.1%)ones generated clear and scorable polymorphic bands between the parental lines varying in size from 150 to 1250 bp.Among the 304 polymorphic primers,262(86.2%)amplified one locus,35(11.5%)produced two loci,4(1.3%)produced three loci,2(0.6%,PIP1202 and PIPR68)revealed four loci and one(0.3%)revealed five loci.In summary,359 polymorphic markers were amplified by 304 polymorphic primers,including 231 dominant ones and 128 co-dominant ones.The 359 polymorphic markers were used to construct the linkage map using the software of Joinmap 4.0 in Brassica juncea.Finally,one genetic map,including 18 linkage groups designating LG01-LG18 and covering a genetic length of 1671.13 centi-Morgans(cM)with an average marker interval of 5.50 cM,was constructed.The map lengths of the 18 linkage groups ranged from 54.41 cM for LG16 to 180.48 cM for LG08 with an average of 92.84 cM.The marker interval ranged from 0.00 cM to 37.39 cM with an average of 5.75 cM.3.The investigated 12 agronomic traits and the genotype of the RIL population were combined for QTL mapping analysis using MapQTL 6.0 software.Finally,a total67 QTLs were identified in 12 different agronomic traits,all of which were divided into four groups as follows:(1)PH,PB,MSL,SP and BA were included in plant architecture related traits,among which four QTLs for PH explaining a total of variation of 69.76%ranging from 5.58% to 34.43%,four QTLs for PB explaining a total of variation of55.85% ranging from 9.35% to 24.64%,five QTLs for MSL explaining a total of variation of 63.46% ranging from 5.39% to 24.53%,four QTLs for SP explaining a total of variation of 22.24% ranging from 4.73% to 6.49%,four QTLs for BA explaining a total of variation of 28.52% ranging from 4.05% to 15.45%.(2)SMS and SL were included in siliques related traits,three QTLs for BA explaining a total of variation of 37.89% ranging from 7.07% to 16.06%,four QTLs in 2019 for SL explaining a total of variation of 47.85% ranging from 3.52% to 32.12%,four QTLs in2020 for SL explaining a total of variation of 47.66% ranging from 3.78% to 34.06%,and three overlapped QTLs among 2019 and 2020 for SL trait.(3)ST,BT and FT were included in flowering related traits,among which four QTLs for ST explaining a total of variation of 44.20% ranging from 5.50% to 15.30%,four QTLs for BT explaining a total of variation of 41.06% ranging from 5.40% to 14.76%,seven QTLs for FT explaining a total of variation of 65.02% ranging from 4.68% to 17.28%,and four QTLs were overlapped among these three traits.(4)SdS and TSW are included in seed related traits,among which five QTLs for SdS in 2019 explaining a total of variation of 33.91%ranging from 3.30% to 16.50%,five QTLs for SdS in 2020 explaining a total of variation of 35.31% ranging from 3.87% to 17.71%,five QTLs for TSW in 2019 explaining a total of variation of 48.75% ranging from 3.43% to 27.02%,five QTLs for TSW in 2020 explaining a total of variation of 49.12% ranging from 6.93% to 19.53%,and there are four overlapped QTLs for each of SdS and TSW.These results will be helpful for further fine mapping,gene cloning and molecular marker assisted breeding of B.juncea genotypes. |
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