Dataset Information
PREDICT: untargeted metabolomic profiling in a Brassica napus (canola) breeding population by GC-MS
Ontology highlight
ABSTRACT: Genome-wide association studies (GWAS) identified thousands of genetic loci associated with complex plant traits, including many traits of agronomical importance. However, functional interpretation of GWAS results remains challenging because of large candidate regions due to linkage disequilibrium. High-throughput omics technologies, such as genomics, transcriptomics, proteomics, and metabolomics open new avenues for integrative systems biological analyses and help to nominate systems information supported (prime) candidate genes. In the present study, we capitalize on a diverse canola population with spring-type 477 lines which was previously analysed by high-throughput phenotyping (Knoch et al., 2020), and by RNA sequencing and metabolite profiling for multi-omics-based hybrid performance prediction (Knoch et al., 2021). We deepened the phenotypic data analysis, now providing 123 time-resolved image-based traits, to gain insight into the complex relations during early vegetative growth and re-analysed the transcriptome data based on the latest Darmor-bzh v10 genome assembly (Rousseau-Gueutin et al., 2020). Genome-wide association testing revealed 61,298 robust quantitative trait loci (QTL) including 187 metabolite-QTL, 56,814 expression-QTL, and 4,297 phenotypic QTL, many clustered in pronounced hotspots. Combining information about QTL colocalisation across omics layers and correlations between omics features allowed us to discover prime candidate genes for metabolic and vegetative growth variation. Prioritized candidate genes for early biomass accumulation include A06p05760.1_BnaDAR (PIAL1), A10p16280.1_BnaDAR, C07p48260.1_BnaDAR (PRL1), and C07p48510.1_BnaDAR (CLPR4). Moreover, we observed unequal effects of the Brassica A and C subgenomes on early biomass production.
INSTRUMENT(S): Gas Chromatography MS - positive
SUBMITTER: Dominic Knoch
PROVIDER: MTBLS8056 | MetaboLights | 2023-11-20
REPOSITORIES: MetaboLights
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| MTBLS8056 | Other | |||
| FILES | Other | |||
| a_MTBLS8056_GC-MS_positive__metabolite_profiling.txt | Txt | |||
| i_Investigation.txt | Txt | |||
| m_MTBLS8056_GC-MS_positive__metabolite_profiling_v2_maf.tsv | Tabular |
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Publications
Multi-omics-based prediction of hybrid performance in canola.
TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik 20210201 4
<h4>Key message</h4>Complementing or replacing genetic markers with transcriptomic data and use of reproducing kernel Hilbert space regression based on Gaussian kernels increases hybrid prediction accuracies for complex agronomic traits in canola. In plant breeding, hybrids gained particular importance due to heterosis, the superior performance of offspring compared to their inbred parents. Since the development of new top performing hybrids requires labour-intensive and costly breeding programm ...[more]
