Bioinformatic And Genetic Basis Analysis Of Rice Metabolome

Bioinformatics plays an increasingly important role in the study of rice metabolomics.Over the past decade,with the development of the high-throughput and high-resolution metabolic detection technology,a variety of biological information and statistical methods were needed to analyze these high-throughput metabolic data effectively to unravel metabolic diversity and its underlying genetic variation in rice.Based on the bioinformatics,we have also combined the metabolomic with the genomic,transcriptomic,proteomic and other ‘omic’ data to promote the functional genomics of rice research.In this study,we used bioinformatics methods such as principal component analysis(PCA),hierarchical clustering analysis(HCA),and correlation analysis to study the natural variation of rice metabolism,and combined with metabolic genome-wide association analysis(m GWAS)to explore the underlying genetic basis of these metabolites.First,we analyzed the accumulation of metabolites between different sub-populations(Indica,Japonica)and different tissues(levaves and grains)of rice,and found that rice metabolites were not only different in indica-japonica,Inter-organization is also specific.To explain this phenomenon from the genetic point of view,we use m GWAS to compare and analyze different populations and different tissues respectively.There has been a significant difference in genetic control between the two populations.It also proves that the genetic regulation of rice metabolites in different tissues is specific,and we believe that this specificity is derived from the tissue-specific expression of the genes.We used a variety of bioinformatic methods to effectively combine the results of m GWAS with genomic,transcriptomic,metabolic and other omic data to facilitate the study of functional genomics in rice,including analysis of population genetic variation,co-expression analysis based on transcriptome data,sequence similarity comparison,and the construction of metabolic networks based on the GGM model.Based on this,we identified 30 new candidate genes and 40 unknown metabolites from the significant loci of rice seed m GWAS,and verified the function of Os04g11970,and quantified the derivatives of tryptamine and serotonin.In addition,we co-locates the homologous sites of m GWAS to reveal the common genetic regulation of metabolites with the same or similar structure between rice and maize.This method,which combined the large effect loci in rice m GWAS and the high resolution of maize m GWAS,which greatly improved our genetic basis of rice metabolism.Finally,we were able to generate 20 testable candidate genes and experimentally further validate a function for Os06g18670.Finally,using parallel metabolic and phenotypic genome-wide studies(m GWAS and p GWAS)have identified new candidate genes potentially responsible for variation in traits such as grain colour and size,and demonstrate that Os02g57760 had a common effect on the trigonelline and grain width,which provides evidence of metabotype-phenotype linkage.In summary,bioinformatic plays an important role on the metabonomic research.We have developed a powerful analytical tool for studying the interaction between plant functional genomes and metabolomics,especially the identification of minor QTLs for complex phenotypic traits,which will provide a new sight into the research of important phenotypic in rice and crop genetic improvement.