Jiangsu Provincial Key Laboratory of Crop Genetics and Physiology, Co-Innovation Center for Modern Production Technology of Grain Crops, Key Laboratory of Plant Functional Genomics of Ministry of Education, Yangzhou University, 225009, Yangzhou, China.
Jiangsu Yanjiang Institute of Agricultural Sciences, 226541, Nantong, China.
Heredity (Edinb). 2020 Jan;124(1):122-134. doi: 10.1038/s41437-019-0251-x. Epub 2019 Jul 29.
Seed filling is a dynamic process that determines seed size and nutritional quality. This time-dependent trait follows a logistic (S-shaped) growth curve that can be described by a logistic function, with parameters of biological relevance. When compared between genotypes, the filling dynamics variations are explained by the differences of parameter values; as such, the parameter estimates can be considered as "traits" for genetic analysis to identify loci that are associated with the seed-filling process. We carried out genetic and genomic analysis of the seed-filling process in maize, using a recombinant inbred line (RIL) population derived from the two inbred lines with contrasting seed-filling dynamics. We recorded seed dry weight at 14 time points after pollination, spanning the early filling phases to the late maturation stages. Fitting these data to a logistic model allowed for estimating 12 characteristic parameters that can be used to meaningfully describe the seed-filling process. Quantitative trait locus (QTL) mapping of these parameters identified a total of 90 nonredundant loci. Using bulked segregant RNA-sequencing (BSR-seq) analysis, we identified eight genes that showed differential gene expression patterns at multiple time points between the extreme pools, and these genes co-localize with the mapped QTL regions. Two of the eight genes, GRMZM2G391936 and GRMZM2G008263, are implicated in starch and sucrose metabolism, and biosynthesis of secondary metabolites that are well known for playing a vital role in seed filling. This study suggests that the logistic model-based approach can efficiently identify genetic loci that regulate dynamic developing traits.
种子灌浆是一个决定种子大小和营养品质的动态过程。这个时间依赖的特征遵循逻辑斯蒂（S 形）生长曲线，可以用逻辑斯蒂函数来描述，其中包含具有生物学意义的参数。在比较基因型之间的差异时，填充动态的变化可以用参数值的差异来解释；因此，参数估计可以被认为是用于遗传分析的“特征”，以鉴定与种子灌浆过程相关的基因座。我们使用来自具有相反灌浆动态的两个自交系的重组自交系（RIL）群体，对玉米的灌浆过程进行了遗传和基因组分析。我们在授粉后 14 个时间点记录种子的干重，涵盖了早期灌浆阶段到后期成熟阶段。将这些数据拟合到逻辑模型中，可以估计 12 个特征参数，这些参数可用于有意义地描述种子灌浆过程。对这些参数进行数量性状位点（QTL）作图，总共鉴定到 90 个非冗余基因座。使用混池分离 RNA 测序（BSR-seq）分析，我们在两个极端群体之间的多个时间点鉴定到了 8 个表现出差异基因表达模式的基因，这些基因与映射的 QTL 区域共定位。这 8 个基因中的两个，GRMZM2G391936 和 GRMZM2G008263，与淀粉和蔗糖代谢以及次生代谢物的生物合成有关，这些代谢物在种子灌浆中起着至关重要的作用。这项研究表明，基于逻辑模型的方法可以有效地鉴定调节动态发育特征的遗传基因座。
