Abstract
BackgroundAn optimal seedling development of Brassica napus plants leads to a higher yield stability even under suboptimal growing conditions and has therefore a high importance for plant breeders. The objectives of our study were to (i) examine the expression levels of candidate genes in seedling leaves of B. napus and correlate these with seedling development as well as (ii) detect genome regions associated with gene expression levels and seedling development traits in B. napus by genome-wide association mapping.ResultsThe expression levels of the 15 candidate genes examined in the 509 B. napus inbreds showed an averaged standard deviation of 5.6 across all inbreds and ranged from 3.2 to 8.8. The gene expression differences between the 509 B. napus inbreds were more than adequate for the correlation with phenotypic variation of seedling development. The average of the absolute value correlations of the correlation coefficients of 0.11 were observed with a range from 0.00 to 0.39. The candidate genes GER1, AILP1, PECT, and FBP were strongly correlated with the seedling development traits. In a genome-wide association study, we detected a total of 63 associations between single nucleotide polymorphisms (SNPs) and the seedling development traits and 31 SNP-gene associations for the candidate genes with a P-value < 0.0001. For the projected leaf area traits we identified five different association hot spots on the chromosomes A2, A7, C3, C6, and C7.ConclusionA total of 99.4% of the adjacent SNPs on the A genome and 93.0% of the adjacent SNPs on the C genome had a distance smaller than the average range of linkage disequilibrium. Therefore, this genome-wide association study is expected to result on average in 14.7% of the possible power. Compared to previous studies in B. napus, the SNP marker density of our study is expected to provide a higher power to detect SNP-trait/-gene associations in the B. napus diversity set. The large number of associations detected for the examined 14 seedling development traits indicated that these are genetically complex inherited. The results of our analyses suggested that the studied genes ribulose 1,5-bisphosphate carboxylase/oxygenase small subunit (RBC) on the chromosomes A4 and C4 and fructose-1,6-bisphosphatase precursor (FBP) on the chromosomes A9 and C8 are cis-regulated.Electronic supplementary materialThe online version of this article (doi:10.1186/s12870-015-0496-3) contains supplementary material, which is available to authorized users.
Highlights
An optimal seedling development of Brassica napus plants leads to a higher yield stability even under suboptimal growing conditions and has a high importance for plant breeders
Hasan et al [5] identified in an association mapping study in B. napus simple sequence repeat (SSR) markers which were physically linked to candidate genes for glucosinolate biosynthesis in Arabidopsis thaliana, to be associated with variation of the seed glucosinolate content in B. napus
Using the Weighted gene co-expression network analysis (WGCNA) function “chooseTopHubInEachModule”, the top hub unigenes were identified from 15 modules which were highly conserved between the two datasets and eight of these top hub unigenes could be amplified as functional candidate genes by Reverse transcription quantitative polymerase chain reaction (RT-qPCR) in the 509 rapeseed inbred lines
Summary
An optimal seedling development of Brassica napus plants leads to a higher yield stability even under suboptimal growing conditions and has a high importance for plant breeders. The genotyping of such a high number of markers is very expensive To overcome this problem, Honsdorf et al [7] tested the association between 684 genome-wide distributed amplified fragment-length polymorphism (AFLP) markers and 14 traits in a set of 84 canola quality winter rapeseed cultivars. Honsdorf et al [7] tested the association between 684 genome-wide distributed amplified fragment-length polymorphism (AFLP) markers and 14 traits in a set of 84 canola quality winter rapeseed cultivars They identified between one and 22 putative quantitative trait loci (QTL) which explained between 15 and 53% of the phenotypic variance for ten of the 14 traits. A custom SNP array was used in this study to genotype the entire diversity set
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