基于高密度遗传图谱的玉米籽粒灌浆特性遗传解析

doi:10.3864/j.issn.0578-1752.2017.21.003

摘要/Abstract

摘要： 【目的】灌浆是玉米籽粒形成的重要生理过程，直接决定了籽粒的最终产量。了解玉米籽粒灌浆特性相关性状对粒重形成的作用，解析灌浆特性的遗传基础，为玉米高产育种实践提供指导。【方法】以中国玉米骨干自交系黄早四（HZS）、旅28（Lv28）为亲本构建的包含172个家系的重组自交系（recombination inbred line，RIL）群体为试验材料。首先，利用Logistic模型与Richards模型，进行玉米籽粒灌浆过程拟合度的比较分析。其次，利用方差分析、相关性分析及回归分析分别比较亲本籽粒灌浆特性的差异，研究群体中不同灌浆特性相关性状的关系及其对百粒重的贡献。然后，利用GBS方法，对群体进行基因型分型，选择亲本间多态性标记，构建遗传图谱。最后，利用完备区间作图法（inclusive composite interval mapping，ICIM）进行灌浆特性与生育期相关性状的QTL分析。【结果】籽粒灌浆一般呈现慢-快-慢的变化趋势，可分为缓增期、快增期以及减缓期3个阶段。通过比较不同灌浆模型的拟合度发现，基于Richards模型的预测值与表型值间的决定系数显著高于Logistic模型。比较亲本间灌浆特性的差异发现，黄早四的平均灌浆速率为旅28的1.28倍，但旅28的灌浆持续时间为黄早四的1.07倍，亲本之间在灌浆特性方面差异明显。群体表型相关性分析发现，除缓增期灌浆持续时间（T₁）外，其他灌浆特性相关性状均与百粒重（HKW）存在显著的正相关关系。回归分析发现，快增期灌浆持续时间（T₂）与灌浆速率（G₂）可分别解释百粒重表型变异的57.50%和30.00%。利用多态性SNP标记构建了全长为1 471 cM，标记间平均遗传图距为1 cM的遗传图谱。多个环境下共检测到26个灌浆特性相关QTL、3个百粒重相关QTL及14个生育期相关的QTL，分布在玉米除第7染色体外的其他染色体上，LOD值介于3.27—9.05，单个QTL贡献率为5.97%—21.16%。同时，利用联合环境分析发现，控制不同性状的QTL定位在染色体相同或相近的位置，形成了多个分布于玉米基因组bin 1.05、bin 2.03、bin 4.05、bin 4.06、bin 7.04、bin 9.04的QTL富集区域。其中，在位于bin 4.05（48.24 Mb—135.73 Mb）和bin 9.04（110.40 Mb—114.73 Mb）的区间之内，共定位到多个仅与灌浆速率相关的主效QTL。【结论】Richards模型能够更好地模拟玉米籽粒的灌浆过程。在灌浆特性相关性状中，快增期灌浆速率与灌浆持续时间对于玉米粒重的增加具有重要作用。单环境检测发现，灌浆持续时间相关位点仅能在单环境中得以检测，表现为环境敏感类型。联合环境分析发现，在bin 4.05和bin 9.04区间内分别检测到仅与灌浆速率相关的主效QTL，可作为玉米籽粒灌浆研究的重点区域。

关键词: 玉米, 灌浆速率, 灌浆持续时间, 数量性状位点

Abstract: 【Objective】The grain filling rate and grain filling duration are major determinants of grain yield. Examination of the contributions of growth period and grain filling related traits to kernel weight, and study of quantitative trait loci (QTL) for grain filling rate and duration to dissect the genetic basis of grain filling are helpful to the practice for the breeding of high yield in maize.【Method】To identify QTL for grain filling rate and duration, a recombinant inbred line (RIL) population including 172 families was developed from the cross between Huangzaosi (HZS) and Lv28 which are foundation parents used in maize breeding of China. Firstly, the differences between the Logistic and the Richards models for the fitting of maize grain filling process were compared and the parameters of the grain filling were calculated. Secondly, the differences between HZS and Lv28 in grain filling characteristic were compared. Correlation analysis and regression analysis were applied to elucidate the relationship among growth period and grain filling related traits and the contribution to hundred-kernel weight (HKW). Thirdly, the approach of genotyping-by-sequencing (GBS) was used to detect polymorphic SNP markers between the parents and among the RILs. Finally, the inclusive composite interval mapping (ICIM) was used to identify QTL of growth period and grain filling related traits.【Result】The genetic map was constructed with 1 471 filtered SNP markers, the total length was 1 471 cM with the average length 1 cM. The grain filling process tended to be a slow-fast-slow pattern and could be divided into three phases: the lag phase, the effective grain filling phase and the maturation drying phase. The use of model fitting of the grain filling process could reflect the dynamic changes of grain filling, but the Richards model and the Logistic model were different in simulating the grain filling dynamic process. The r² measure of predicted values obtained by the Richards model with phenotypic values was higher than that by the Logistic model. The grain filling characteristics showed significant differences between the two parents, since the grain filling rate of HZS was 1.28 times higher than that of Lv28, while the grain filling duration of Lv28 was 1.07 times longer than that of HZS. Correlation analysis showed that the grain filling related traits and HKW reached a significant positive correlation except for the grain filling duration in the lag phase. Regression analysis showed that the grain filling rate and the grain filling duration could explain 57.50% and 30.00% of the phenotypic variation of HKW, respectively. The effective grain filling phase played an important role in the formation of HKW. A total of 26 QTL were detected for grain filling related traits, 3 QTL were detected for HKW and 14 QTL were detected for growth period related traits under the single environment, which were distributed on the chromosomes 1, 2, 3, 4, 5, 6, 8, 9 and 10. The LOD values ranged from 3.27 to 9.05, and the range of phenotypic variation explained was 5.97%-21.16%. Under the joint environments, the QTL controlling the grain filling related traits were located at the same or similar positions of chromosomes and formed some QTL clustering regions on bin 1.05, bin 2.03, bin 4.05, bin 4.06, bin 7.04 and bin 9.04. Importantly, some QTL for grain filling rate in different grain filling phases were detected to be clustered on the region of 48.24 cM-135.72 cM on chromosome 4 and the region of 110.10 cM-114.73 cM on chromosome 9. 【Conclusion】 Maize grain filling dynamic process could be simulated well with the Richards model. The grain filling rate and duration of the effective grain filling phase played an important role in the formation of HKW. The inheritance of grain filling related traits were strongly influenced by the environments, so some QTL could be detected only in one environment. Under the joint environments, some QTL related to grain filling rate were detected on bin 4.05 and bin 9.04.

Key words: maize (Zea mays L.), grain filling rate, grain filling duration, QTL

高星，李永祥，杨明涛，李琲琲，李春辉，宋燕春，张登峰，王天宇，黎裕，石云素. 基于高密度遗传图谱的玉米籽粒灌浆特性遗传解析[J]. 中国农业科学, 2017, 50(21): 4087-4099.

GAO Xing, LI YongXiang, YANG MingTao, LI BeiBei, LI ChunHui, SONG YanChun, ZHANG DengFeng, WANG TianYu, LI Yu, SHI YunSu. Genetic Dissection of Grain Filling Related Traits Based on a High-Density Map in Maize[J]. Scientia Agricultura Sinica, 2017, 50(21): 4087-4099.

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