Genome Wide Association Study(GWAS) On Disease Resistance And Late Bolting And Mining Excellent Germplasms In Brassica Rapa L.

Good quality and good resistance to diseases and stresses are elements of a successful vegetable variety. Mining excellent genes is one of the main activities in plant breeding. A total of 203 B. rapa inbred lines, which could be classified to different groups grouped according to different classified methods, were used to evaluated resistance to late bolting, downy mildew, Turnip mosaic virus, black rot, Verticillium wilt, and club root under artificial conditions. A batch of excellent germplasms with comprehensively strong resistance were then screened out. Based on Illumina GA IIx genomic DNA sequence data from 10 materials with a broad genetic background, a total of 1167 single nucleotide polymorphisms(SNPs) were selected out for KASPar assays development, and 1040 were successfully converted and finally selected for germplasm characterization on the basis of reproducibility, missing data rate, and uniform genetic distribution. A genome-wide association study(GWAS) was then performed in order to get the candidate genes and to provide the basis for molecular breeding. Finally,28 inbred lines and the hybrids are assessed on their sensory quality, for the purpose of exploring the relationships between their sensory quality and their nutritional quality, as well as providing a reference for the Chinese cabbage quality breeding. The major results are as follows:1. Comparing the results of the bolting tests, it is found that the bolting indexes of flat-oval types are the lowest. 14 inbred lines were identified to show utmost late bolting in consecutive two years,among which, inbred lines 07-458 and 12-577 show the latest flowering, respectively with 53 and 41 days of flowering since the end of vernalization.2. It is discovered that the number of the materials with high resistance to downy mildew, virus disease, black rot and verticillium wilt and clubroot is 7, 3, 0, 28 and 12, respectively. The number of the materials with resistance to 2 ~ 4 kinds of diseases is 93 in total. And 15 inbred lines with excellent comprehensive resistance were screened out from these materials, such as inbred line 12-85, 13-108,09-894, etc.3. Based on sequencing data of 10 core germplasms, 1040 SNPs, covering the whole genome, were selected. Genetic structure analysis manifest that the population is supposed to be divided into four subgroups: flat-oval type subgroup, deep-plain type subgroup, deep-cylindrical type subgroup and mixed type subgroup. Besides, genetic kinship analysis reveals that the coefficient between each other is less than 0.2 account for about 92%, which indicates that their genetic relationship is relatively distant.4. The genome wide association study(GWAS) analysis revealed that a total of 5, 2, 2, 5, 2, 5 and8 QTLs, respectively, were found to be associated with bolting index, flowering days, downy mildew,Tu MV, black rot, verticillium wilt and clubroot, among which, FLC1 and FD gene are found to be located in the LD decay ranges of the 5 QTLs associated with bolting. That provides a basis for further mining of candidate genes.5. 28 inbred lines and varieties are selected for organoleptic qualities test, and 7 Chinese cabbage inbred lines with good sensory quality are screened out. Multiple regression equations on sensoryquality are obtained as: yr(raw quality) = 0.3103 +0.254x1(juiciness) +0.1762x2(sweetness)+0.2216x3(crispness) +0.3199x4(flavor); yc(cooked quality) = 0.2044 +0.2509x5(residue) +0.2469x6(cooked sweetness) +0.1825x7(softness) +0.3231x8(cooked flavour). The result of path analysis and regression indicated that sweetness and juiciness are the top two factors that impact importantly on the comprehensive quality evaluation of raw Chinese cabbage, and sweetness plays a critical role in the cooked food. Finally, a nutritional regression equation associated with sensory evaluation was obtained as: yr=-32.1920 + 0.3893x1(water content) +1.1698x2(soluble sugars), and yc= 7.4971 + 0.7326x2(soluble sugars)-5.6688x3(organic acid)-2.1763x5(crude fiber). It is indicated that raw quality can be predicted by water content and soluble sugar content, while cooked quality can be predicted by soluble sugar, organic acid and rude fiber.