학술논문
Integrated multi‐omics analyses and genome‐wide association studies reveal prime candidate genes of metabolic and vegetative growth variation in canola.
Document Type
Article
Author
Source
Subject
*GENOME-wide association studies
*BIOLOGICAL systems
*LOCUS (Genetics)
*MULTIOMICS
*PLASMODIOPHORA brassicae
*CANOLA
*METABOLOMICS
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Language
ISSN
0960-7412
Abstract
SUMMARY: 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 capitalise on a diverse canola population with 477 spring‐type lines which was previously analysed by high‐throughput phenotyping of growth‐related traits and by RNA sequencing and metabolite profiling for multi‐omics‐based hybrid performance prediction. 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 reanalysed the transcriptome data based on the latest Darmor‐bzh v10 genome assembly. Genome‐wide association testing revealed 61 298 robust quantitative trait loci (QTL) including 187 metabolite QTL, 56814 expression QTL and 4297 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. Prioritised 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. Significance Statement: In crop plants, deciphering the genetic basis of complex traits, and the identification of causal genes based on GWAS results is a major challenge. Here, we demonstrate the power of combining extensive multi‐omics data, genome‐wide associations, QTL co‐localisations and system‐genetic analyses to pinpoint well supported candidate genes for metabolic variation, early vegetative growth and biomass accumulation despite extended regions of linkage disequilibrium typically present in an elite canola breeding population. [ABSTRACT FROM AUTHOR]