Biochemical Networks and Epistasis Shape the Plant Metabolome
نویسندگان
چکیده
Genomic approaches have accelerated the study of quantitative genetics underlying phenotypic variation. These approaches associate genome-scale analyses such as transcript profiling with targeted phenotypes such as measurements of specific metabolites. Additionally, these approaches have potential utility in identifying uncharacterized networks/pathways. However, little is known about the complexity of genomic architecture underlying genomic datasets such as metabolomics or the potential of such datasets to reveal networks. To describe the genetic control of variation in the Arabidopsis metabolome and test our ability to integrate unknown metabolites into biochemical networks, we conducted a replicated metabolomic analysis on 210 lines of the Arabidopsis thaliana Bay × Sha RIL population that were previously used for targeted metabolite QTL and global eQTL analysis. Metabolic traits were less heritable than the average transcript, suggesting that there are differences in the power to detect QTL between transcript and metabolite traits. Statistical analysis identified a large number of metabolite QTLs with moderate phenotypic effects and frequent epistatic interactions. The distribution of metabolite QTLs across the genome included eleven QTL clusters; eight clusters associated in an epistatic network that regulates plant central metabolism. We also generated two de novo biochemical network models from the available data, one of unknown function and the other associated with central plant metabolism. Detailed analysis of plant metabolism will expand understanding of known metabolic pathways and identify new relationships among pathways. Analyses of larger populations will unravel the complex epistatic interactions revealed in this study.
منابع مشابه
Biochemical networks and epistasis shape the Arabidopsis thaliana metabolome.
Genomic approaches have accelerated the study of the quantitative genetics that underlie phenotypic variation. These approaches associate genome-scale analyses such as transcript profiling with targeted phenotypes such as measurements of specific metabolites. Additionally, these approaches can help identify uncharacterized networks or pathways. However, little is known about the genomic archite...
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