Development of genic cleavage markers in association with seed glucosinolate content in canola

Fu, Ying; Lü, Kun; Qian, Lunwen; Mei, Jiaqin; Wei, Dongfeng; Peng, Xuhui; Xu, Xinhua; Li, Jiana; Frauen, Martin; Dreyer, Felix; Snowdon, Rod J.; Qian, Wei

doi:10.1007/s00122-015-2487-z

Cited by 13 publications

(15 citation statements)

References 43 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The major QTL located on C03 and explaining 16.44% of the phenotypic variance for oil content was identified in both of TN DH population and KN DH population [74]. The QTL with large genetic effects for total seed glucosinolates located in A08, C01 and C03, were also identified previously in this and other mapping populations [9, 19, 75]. These QTLs are interesting targets for marker-assisted selection, which can be applied in rapeseed breeding in combination with the enrichment of target alleles for F 2 populations prior to producing DH populations.…”

Section: Discussionsupporting

confidence: 61%

Seed Quality Traits Can Be Predicted with High Accuracy in Brassica napus Using Genomic Data

et al. 2016

View full text Add to dashboard Cite

Improving seed oil yield and quality are central targets in rapeseed (Brassica napus) breeding. The primary goal of our study was to examine and compare the potential and the limits of marker-assisted selection and genome-wide prediction of six important seed quality traits of B. napus. Our study is based on a bi-parental population comprising 202 doubled haploid lines and a diverse validation set including 117 B. napus inbred lines derived from interspecific crosses between B. rapa and B. carinata. We used phenotypic data for seed oil, protein, erucic acid, linolenic acid, stearic acid, and glucosinolate content. All lines were genotyped with a 60k SNP array. We performed five-fold cross-validations in combination with linkage mapping and four genome-wide prediction approaches in the bi-parental population. Quantitative trait loci (QTL) with large effects were detected for erucic acid, stearic acid, and glucosinolate content, blazing the trail for marker-assisted selection. Despite substantial differences in the complexity of the genetic architecture of the six traits, genome-wide prediction models had only minor impacts on the prediction accuracies. We evaluated the effects of training population size, marker density and phenotyping intensity on the prediction accuracy. The prediction accuracy in the independent and genetically very distinct validation set still amounted to 0.14 for protein content and 0.17 for oil content reflecting the utility of the developed calibration models even in very diverse backgrounds.

show abstract

Section: Discussionsupporting

confidence: 61%

Seed Quality Traits Can Be Predicted with High Accuracy in Brassica napus Using Genomic Data

et al. 2016

View full text Add to dashboard Cite

show abstract

“…DNA isolation, development of molecular markers and construction of genetic linkage groups were described in previous study 38 , where 293 SSR markers were arranged into 19 B. napus chromosomes, spanning a genetic distance of 1,188 cM with an average distance of 4.05 cM between adjacent markers. Besides, the sequence of target region was downloaded from the reference genomes ( http://brassicadb.org/brad/index.ph ), and screened for SSR loci using the software “SSR Locator” 39 .…”

Section: Methodsmentioning

confidence: 99%

Comparative quantitative trait loci for silique length and seed weight in Brassica napus

Wei

Dong

et al. 2015

Sci Rep

Self Cite

View full text Add to dashboard Cite

Silique length (SL) and seed weight (SW) are important yield-associated traits in rapeseed (Brassica napus). Although many quantitative trait loci (QTL) for SL and SW have been identified in B. napus, comparative analysis for those QTL is seldom performed. In the present study, 20 and 21 QTL for SL and SW were identified in doubled haploid (DH) and DH-derived reconstructed F2 populations in rapeseed, explaining 55.1–74.3% and 24.4–62.9% of the phenotypic variation across three years, respectively. Of which, 17 QTL with partially or completely overlapped confidence interval on chromosome A09, were homologous with two overlapped QTL on chromosome C08 by aligning QTL confidence intervals with the reference genomes of Brassica crops. By high density selective genotyping of DH lines with extreme phenotypes, using a Brassica single-nucleotide polymorphism (SNP) array, the QTL on chromosome A09 was narrowed, and aligned into 1.14-Mb region from 30.84 to 31.98 Mb on chromosome R09 of B. rapa and 1.05-Mb region from 27.21 to 28.26 Mb on chromosome A09 of B. napus. The alignment of QTL with Brassica reference genomes revealed homologous QTL on A09 and C08 for SL. The narrowed QTL region provides clues for gene cloning and breeding cultivars by marker-assisted selection.

show abstract

“…Side chain elongation and hydroxylation in seeds or leaves are both controlled by two loci in populations derived from crosses between oilseed rape cultivars and synthetic B. napus lines (Magrath et al , ; Parkin et al , ). Three to five major QTLs control the total seed GSL content (Seed‐GSL) in several bi‐parental linkage populations derived from crosses between low‐ and high‐GSL accession, and 43 QTLs control this trait in a low‐GSL genetic background in B. napus (Fu et al , ; Howell et al , ; Toroser et al , ; Uzunova et al , ; Zhao and Meng, ). Feng et al () identified 105 metabolite QTLs for GSL compounds, among which 71 and 17 were seed and leaf specific, respectively.…”

Section: Introductionmentioning

confidence: 99%

Dissection of genetic architecture for glucosinolate accumulations in leaves and seeds of Brassica napus by genome‐wide association study

Liu

Huang

et al. 2019

Plant Biotechnology Journal

View full text Add to dashboard Cite

Glucosinolates (GSLs), whose degradation products have been shown to be increasingly important for human health and plant defence, compose important secondary metabolites found in the order Brassicales. It is highly desired to enhance pest and disease resistance by increasing the leaf GSL content while keeping the content low in seeds of Brassica napus, one of the most important oil crops worldwide. Little is known about the regulation of GSL accumulation in the leaves. We quantified the levels of 9 different GSLs and 15 related traits in the leaves of 366 accessions and found that the seed and leaf GSL content were highly correlated (r = 0.79). A total of 78 loci were associated with GSL traits, and five common and eleven tissue-specific associated loci were related to total leaf and seed GSL content. Thirty-six candidate genes were inferred to be involved in GSL biosynthesis. The candidate gene BnaA03g40190D (BnaA3.MYB28) was validated by DNA polymorphisms and gene expression analysis. This gene was responsible for high leaf/low seed GSL content and could explain 30.62% of the total leaf GSL variation in the low seed GSL panel and was not fixed during double-low rapeseed breeding. Our results provide new insights into the genetic basis of GSL variation in leaves and seeds and may facilitate the metabolic engineering of GSLs and the breeding of high leaf/low seed GSL content in B. napus. 1472

show abstract

Development of genic cleavage markers in association with seed glucosinolate content in canola

Cited by 13 publications

References 43 publications

Seed Quality Traits Can Be Predicted with High Accuracy in Brassica napus Using Genomic Data

Seed Quality Traits Can Be Predicted with High Accuracy in Brassica napus Using Genomic Data

Comparative quantitative trait loci for silique length and seed weight in Brassica napus

Dissection of genetic architecture for glucosinolate accumulations in leaves and seeds of Brassica napus by genome‐wide association study

Contact Info

Product

Resources

About