ArticlePloS one2016
Seed Quality Traits Can Be Predicted with High Accuracy in Brassica napus Using Genomic Data.
Article in PloS one, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed.
- Correlation and path analysis in winter oilseed rape (Brassica napus L.) cultivars based on yield and phenotypic traits.Journal of applied genetics · 2026Article
- Review
- Improving hybrid breeding efficiency in winter oilseed rape under sparse multi-environment testing.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2026Article
- Identification of candidate genes regulating seed oil and protein content in Brassica napus via integrated genome-wide association and transcriptome analysis.BMC plant biology · 2026Article
- Harnessing genomic prediction in Brassica napus through a nested association mapping population.The plant genome · 2025Article
- Optimization and application of genome prediction model in rapeseed: flowering time, yield components, and oil content as examples.Horticulture research · 2025Article
- Multifactor Analysis of a Genome-Wide Selection System inPlants (Basel, Switzerland) · 2025Article
- Natural variation and artificial selection at the BnaC2.MYB28 locus modulate Brassica napus seed glucosinolate.Plant physiology · 2023Article
- Genome-Wide Association Analysis Combined With Quantitative Trait Loci Mapping and Dynamic Transcriptome Unveil the Genetic Control of Seed Oil Content inFrontiers in plant science · 2022Article
- Construction of a Quantitative Genomic Map, Identification and Expression Analysis of Candidate Genes for Agronomic and Disease-Related Traits inFrontiers in plant science · 2022Article
- Training set design in genomic prediction with multiple biparental families.The plant genome · 2021Article
- Exploring the gene pool of Brassica napus by genomics-based approaches.Plant biotechnology journal · 2021Review
- Genomics Armed With Diversity Leads the Way inFrontiers in genetics · 2021Review
- Enhancing the Nutritional Quality of Major Food Crops Through Conventional and Genomics-Assisted Breeding.Frontiers in nutrition · 2020Review
- Finding invisible quantitative trait loci with missing data.Plant biotechnology journal · 2018Article
- Genomic Prediction Using Prior Quantitative Trait Loci Information Reveals a Large Reservoir of Underutilised Blackleg Resistance in Diverse Canola (The plant genome · 2018Article
- Effective Genomic Selection in a Narrow-Genepool Crop with Low-Density Markers: Asian Rapeseed as an Example.The plant genome · 2018Article
- Incorporating pleiotropic quantitative trait loci in dissection of complex traits: seed yield in rapeseed as an example.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2017Article
- Hybrid Performance of an Immortalized FFrontiers in plant science · 2017Article
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
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