New Genetic Loci Associated with Preharvest Sprouting and Its Evaluation Based on the Model Equation in Rice

Abstract

Preharvest sprouting (PHS) in rice panicles is an important quantitative trait that causes both yield losses and the deterioration of grain quality under unpredictable moisture conditions at the ripening stage. However, the molecular mechanism underlying PHS has not yet been elucidated. Here, we explored the genetic loci associated with PHS in rice and formulated a model regression equation for rapid screening for use in breeding programs. After re-sequencing 21 representative accessions for PHS and performing enrichment analysis, we found that approximately 20,000 SNPs revealed distinct allelic distributions between PHS resistant and susceptible accessions. Of these, 39 candidate SNP loci were selected, including previously reported QTLs. We analyzed the genotypes of 144 rice accessions to determine the association between PHS and the 39 candidate SNP loci, 10 of which were identified as significantly affecting PHS based on allele type. Based on the allele types of the SNP loci, we constructed a regression equation for evaluating PHS, accounting for an R 2 value of 0.401 in japonica rice. We validated this equation using additional accessions, which exhibited a significant R 2 value of 0.430 between the predicted values and actual measurements. The newly detected SNP loci and the model equation could facilitate marker-assisted selection to predict PHS in rice germplasm and breeding lines.

Detection of Highly Differentiated Genomic Regions Between Lotus (Nelumbo nucifera Gaertn.) With Contrasting Plant Architecture and Their Functional Relevance to Plant Architecture

“…In the rice, WGRS was performed on 11 preharvest sprouting (PHS) susceptible accessions and 10 PHS resistant accessions, and approximately 20,000 PHS-associated single nucleotide polymorphisms (SNPs) showing distinct allelic distributions between the two groups were detected. Some regions containing high numbers of these SNPs overlapped with previously reported QTLs associated with PHS ( Lee et al, 2017 ). In the peach, chi-square test identified two SNPs in a fruit size candidate gene showing distinct allelic distributions between 30 small and 30 large fruit peach varieties ( Cao et al, 2016 ).…”

Development and application of indica–japonica SNP assays using the Fluidigm platform for rice genetic analysis and molecular breeding

“…Therefore, our indica-japonica SNP sets are suitable for genotyping a small number of SNPs and for changing the SNP combination, based on the objective such as target gene screening, genotyping of unfixed early generation populations (e.g., F 2 and BC n F 1 ), and gene/QTL pyramiding by marker-assisted backcrossing (MABC). The Fluidigm platform has been used for target SNP genotyping in various rice genetic studies (Duitama et al 2015;Hyun et al 2015;Lee et al 2017). However, a genome-wide SNP set using Fluidigm has not been reported to date.…”

Genes Contributing to Domestication of Rice Seed Traits and Its Global Expansion

“…Many QTLs affecting seed dormancy or germination-related traits have been mapped in rice [ 35 , 36 , 37 , 38 , 39 , 40 , 41 ], but only a few QTLs have been cloned in rice ( Table 1 ), such as Sdr4 , encoding a novel protein; qSD7-1/Rc , encoding a bHLH transcription factor; and qSD1-2 , encoding OsGA20ox2 [ 8 , 9 , 10 , 42 ]. Sdr4 acts as a seed dormancy-specific regulator that is under the control of OsVP1 , which is a global positive regulator of seed maturation through its effect on ABA signaling and the regulation of the expression of OsDOG1L-1 , which is a positive regulator of dormancy in rice [ 8 ].…”