Genome-wide association mapping of genes for anthocyanin and flavonoid contents in Vietnamese landraces of black rice

Abstract

Pigmented rice is desirable in healthy human diets because of potential health benefits compared to white rice. Based on the high diversity of Vietnamese black rice landrace, mapping the loci and predicting putative candidate genes for anthocyanin and flavonoid contents in these landraces would be useful to support the improvement of pigmented rice. A panel of 94 black rice accessions from the National Plant Genebank of Vietnam was analyzed to determine the anthocyanin and flavonoid levels in the whole grain and genotyped using an improved 7 K SNP array. Phenotypic and genotypic data were integrated in a genome wide association study. This identified 32 SNPs significantly associated with total anthocyanin content and 16 SNPs associated with total flavonoid content located on Chromosomes (Chrs.) 1, 2, 3, 6, 7, 8, 11 and 12 and on Chrs. 1, 3, 4, 6, 7, 8, 10, 11 and 12, respectively. A total of 72 candidate genes were co-located with the significant SNPs, of which 44 were predicted to be related to the pathways of biosynthesis of anthocyanin and flavonoid. Twenty candidate genes first reported in this study including the structural genes, LOC_Os03g62300.1 coding for chalcone isomerase and LOC_Os06g41800.1 coding for dihydroflavonol reductase and the regulatory gene, LOC_Os06g43090.1 coding for a MYB transcription factor, were strongly associated with anthocyanin synthesis. These candidate genes and significant SNPs could be used to analyze the function of genes and factors involved in anthocyanin synthesis and to improve pigmented rice by marker-assisted selection.

Genome-Wide Association Study of Metabolic Traits in the DuckweedSpirodela polyrhiza

“…In line with previous reports (Chen et al 2016;Pham et al 2023), we found that secondary metabolite levels showed the highest average phenotypic variation across all metabolic traits, whereas variation for growth parameters was lowest. For secondary metabolites, high variation in tissue concentration was explained by their low trait complexity (Pham et al 2023;Haghi et al 2022;Chen et al 2014). Whereas primary metabolites and growth seem to be regulated by many small effect loci, secondary metabolites are often controlled by just a few major-effect loci (Chen et al 2014;Chen et al 2016).…”

“…Previous GWAS conducted on complex traits highlighted, that the success in identifying genetic associations greatly depends on sample size and phenotypic variation (Schizophrenia Working Group of the Psychatric Genomics Consortium 2014; Duncan et al 2019;Chen et al 2014). In line with previous reports (Chen et al 2016;Pham et al 2023), we found that secondary metabolite levels showed the highest average phenotypic variation across all metabolic traits, whereas variation for growth parameters was lowest. For secondary metabolites, high variation in tissue concentration was explained by their low trait complexity (Pham et al 2023;Haghi et al 2022;Chen et al 2014).…”