Genome-wide and gene-based association mapping for rice eating and cooking characteristics and protein content

Abstract

Rice eating and cooking quality and protein content (PC) are important properties affecting consumers’ preferences, nutrition and health. Linkage QTL mapping and association studies are usually applied to genetically dissect related traits, which could be further facilitated by high density SNP markers and gene annotation based on reference genome to rapid identify candidate genes associated with interested traits. Here, we carried out an association study for apparent amylose content (AC), gel consistency (GC), gelatinization temperature (GT) and PC evaluated in two environments using a diverse panel of 258 accessions from 3 K Rice Genome Project. Wide phenotypic variations were observed in this panel. Genome-wide association study using 22,488 high quality SNPs identified 19 QTL affecting the four traits. Combining gene-based association study and haplotype analyses plus functional annotation allowed us to shortlist nine candidate genes for four important QTL regions affecting AC, GC and GT, including two cloned genes (Wx and ALK), and seven novels. The research suggested that GWAS and gene-based association analysis followed by haplotype analysis is an effective way to detect candidate genes. The identified genes and QTL provided valuable sources for future functional characterization and genetic improvement of rice eating and cooking quality and PC.

Pleiotropic Effects of Rice Florigen Gene RFT1 on the Amino Acid Content of Unmilled Rice

“…On the other hand, no study has been reported for the pleiotropic effects of heading-date genes on grain quality in rice. Among traits in the four primary categories of rice grain quality (i.e., milling, appearance, eating and cooking, and nutritional qualities), PC and AAC are the major parameters of nutritional quality Wang et al, 2017a). In the present study, a total of 73 QTLs for AAC of unmilled rice were detected using the ZS97/MY46 RIL population, and the largest QTL cluster was validated to be responsible by the RFT1 gene on the short arm of chromosome 6.…”

Genome-wide association studies of ionomic and agronomic traits in USDA mini core collection of rice and comparative analyses of different mapping methods

“…The amylose controlling locus Wax [86] gene was rediscovered by all four analysis methods and the ALK [87] gene was mapped by the two univariate analysis methods. The hull cover genes OcTCL2 [88] and OsWOX3 [89] were mapped by the GLM and MLM methods.…”

“…There were many genes were mapped by only two or three of the four tested methods. For example, the amylose gene ALK [87] on chromosome 6 was mapped by univariate methods GLM and MLM; The kernel rate gene GS3 [66] on chromosome 3 was mapped by GLM, MLMM, and FarmCPU. These results described above clearly demonstrated that all four methods can be effectively used to perform GWAS analysis and able to identify some of the known loci.…”

Evolutionary and ecological functional genomics, from lab to the wild

“…Three RNA‐seq papers (Lister et al ., ; Mortazavi et al ., ; Nagalakshmi et al ., ) on Arabidopsis, yeast and mice, respectively, mark the start of this tool's use for functional genomic studies. Contemporarily, RNA‐seq is used at ever‐larger scales for functional characterization of developmental, environmental response and economically important phenotypes (Becker et al ., ; Feng et al ., ; Giacomello et al ., ; Leydon et al ., ; Liu et al ., ), and is often used to validate epigenomic measurements (Wang et al ., ). There are numerous studies that employ whole‐genome gene expression analysis not just for model plant species and crops, but also for lesser‐known plant species including Japanese lawn grass (Xie et al ., ), Cunninghamia lanceolata (Cao et al ., ), mangrove fern (Zhang et al ., ), wild oil‐tea camelia (Chen et al ., ), curry tree (Meena et al ., ) and Banksia (He et al ., ).…”