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

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 exp...

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Detalles Bibliográficos
Autores principales: Lee, Gi-An, Jeon, Young-Ah, Lee, Ho-Sun, Hyun, Do Yoon, Lee, Jung-Ro, Lee, Myung-Chul, Lee, Sok-Young, Ma, Kyung-Ho, Koh, Hee-Jong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5550670/
https://www.ncbi.nlm.nih.gov/pubmed/28848592
http://dx.doi.org/10.3389/fpls.2017.01393
Descripción
Sumario: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.