Project description:<p>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.</p><p><br></p>
