大麦4个穗部性状的关联分析

doi:10.3969/j.issn.1004-1524.2020.11.03

浙江农业学报 ›› 2020, Vol. 32 ›› Issue (11): 1941-1953.DOI: 10.3969/j.issn.1004-1524.2020.11.03

大麦4个穗部性状的关联分析

胡倩文¹, 徐延浩¹, 王容¹, 张文英¹, 华为^2,*, 吕超³

1.长江大学农学院,涝渍灾害与湿地农业湖北省重点实验室/主要粮食作物产业化湖北省协同创新中心,湖北荆州 434000;
2.浙江省农业科学院作物与核技术利用研究所,浙江杭州310021;
3.江苏省作物遗传生理重点实验室,扬州大学农学院,江苏扬州 225009

收稿日期:2019-05-30 出版日期:2020-11-25 发布日期:2020-12-02
通讯作者: * 华为,E-mail: huaweicau@hotmail.com
作者简介:胡倩文(1993—),女,湖北荆州人,硕士研究生,主要从事植物分子育种研究。E-mail: 383008546@qq.com
基金资助:
浙江省自然科学基金(LY20C130005); 江苏省作物遗传生理重点实验室开放课题(YCSL201910)

Association analysis of four spike traits in barley

HU Qianwen¹, XU Yanhao¹, WANG Rong¹, ZHANG Wenying¹, HUA Wei^2,*, LYU Chao³

1. Hubei Key Laboratory of Waterlogging Disaster and Agriculture Use of Wetland, College of Agriculture, Yangtze University/Hubei Collaborative Innovation Centre for Grain Industry, Jingzhou 434000, China;
2. Institute of Crop and Nuclear Technology Utilization, Zhejiang Academy of Agricultural Sciences, Hangzhou 310021, China;
3. Jiangsu Key Laboratory of Crop Genetics and Physiology, Agricultural College, Yangzhou University, Yangzhou 225009, China

Received:2019-05-30 Online:2020-11-25 Published:2020-12-02

摘要/Abstract

摘要： 穗部性状是大麦产量和品质的重要决定因素,良好的穗部结构应有一定的穗长基础、适合的小穗密度和每穗小穗数。以137份来源广泛的大麦材料为关联群体,利用224对SSR标记获取群体基因型,在2个环境试点下对穗粒数、小穗密度、小穗数、穗长进行性状观察,利用一般线性模型(GLM)和混合线性模型(MLM)两种模型进行4个穗部性状与SSR标记的关联分析。供试大麦群体的4个穗部性状差异显著,穗粒数与小穗数、穗长与小穗密度显著相关。224对SSR标记共检测出479个等位变异,平均有效等位基因数、Shannon's指数和PIC分别为1.765、0.612和0.327。供试材料的遗传相似系数(GS)变化范围为0.486~0.891,并在GS值为0.580时,将材料分为2个类群。群体结构分析也将供试材料分为2个亚群。GLM分析显示,与穗粒数、小穗密度、小穗数、穗长相关联的标记位点数量分别有33、13、34、6,这些标记遍布大麦7条染色体,单个标记对表型变异的解释率为8.65%~50.08%(P<0.001);MLM分析显示,与上述4个性状相关联的标记位点数量分别为4、3、4、1,这些标记位于大麦1H、2H和4H染色体,单个标记对表型变异的解释率为3.12%~16.95%(P<0.001)。Ind1003、Ind2030、Ind2055、Ind4012这4个标记在2个环境中都能被2种模型检测到,且同时与穗粒数和小穗数相关联。

关键词: 大麦, SSR, 穗部性状, 遗传多样性, 关联分析

Abstract: Spike traits are important determinants of barley yield and quality. A good spike structure should have a certain spike length, suitable spikelet density and spikelet number per spike. In this study, 137 barley materials from a wide range were used as the population for association analysis. A total of 224 pairs of SSR markers were used to analyze the genotype of the population. Under two environmental experiments, grain number per spike, spikelet density, spikelet number per spike and spike length were recorded. General linear model (GLM) and mixed linear model (MLM) models were used to analyze the association between four spike traits and SSR markers. The results showed that there were significant differences among the four spike traits of the tested barley population. Grain number per spike was significantly related to spikelet number per spike, and spike length was significantly related to spikelet density. A total of 479 alleles were detected for SSR markers. The average effective allele number, Shannon's index and PIC were 1.765, 0.612 and 0.327, respectively. The genetic similarity coefficient (GS) of the tested materials ranged from 0.486 to 0.891. The barley materials were divided into two groups at GS value level of 0.580. Population structure analysis also divided the materials into two subgroups. The results of GLM analysis showed that there were 33, 13, 34 and 6 loci associated with grain number per spike, spikelet density, spikelet number per spike and spike length, respectively, and the phenotypic variation explained by a single marker ranged from 8.65% to 50.08% (P<0.001). These markers were located on seven chromosomes of barley. The results of MLM analysis showed that there were 4, 3, 4 and 1 marker sites associated with the four spike traits described above, respectively. The phenotypic variation explained by a single marker ranged from 3.12% to 16.95% (P<0.001). These markers were located on the chromosome 1H, 2H and 4H of barley. The four markers Ind1003, Ind2030, Ind2055 and Ind4012, which associated to both grain number per spike and spikelet number per spike, could be detected by two models in two tests.

Key words: barley, SSR, spike trait, genetic diversity, association analysis

中图分类号:

S512.3

胡倩文, 徐延浩, 王容, 张文英, 华为, 吕超. 大麦4个穗部性状的关联分析[J]. 浙江农业学报, 2020, 32(11): 1941-1953.

HU Qianwen, XU Yanhao, WANG Rong, ZHANG Wenying, HUA Wei, LYU Chao. Association analysis of four spike traits in barley[J]. , 2020, 32(11): 1941-1953.

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大麦4个穗部性状的关联分析

Association analysis of four spike traits in barley

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