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
National Agrobiodiversity Center, National Institute of Agricultural SciencesJeonju, South Korea.
Department of Plant Science, Plant Genomics and Breeding Institute, and Research Institute of Agriculture and Life Sciences, Seoul National UniversitySeoul, South Korea.
Front Plant Sci. 2017 Aug 8;8:1393. doi: 10.3389/fpls.2017.01393. eCollection 2017.
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 value of 0.401 in rice. We validated this equation using additional accessions, which exhibited a significant 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.
水稻穗部的收获前发芽（PHS）是一个重要的数量性状，在成熟阶段不可预测的水分条件下，它会导致产量损失和谷粒品质下降。然而，PHS潜在的分子机制尚未阐明。在此，我们探索了水稻中与PHS相关的遗传位点，并建立了一个模型回归方程，用于在育种计划中进行快速筛选。在对21个具有代表性的PHS材料进行重测序并进行富集分析后，我们发现约20,000个单核苷酸多态性（SNP）在抗PHS和感PHS材料之间呈现出不同的等位基因分布。其中，选择了39个候选SNP位点，包括先前报道的数量性状基因座（QTL）。我们分析了144份水稻材料的基因型，以确定PHS与这39个候选SNP位点之间的关联，基于等位基因类型，其中10个被确定为对PHS有显著影响。基于SNP位点的等位基因类型，我们构建了一个评估PHS的回归方程，在水稻中其决定系数（R²）值为0.401。我们使用其他材料验证了该方程，预测值与实际测量值之间的R²值为0.430，具有显著性。新检测到的SNP位点和模型方程有助于在水稻种质和育种系中进行标记辅助选择以预测PHS。
