| 摘要: |
| 为探究籼稻表型性状遗传多样性信息，以198份籼稻种质资源为试材，进行SNP分子标记和表型性状分析，结果表明:通过2种简化基因组测序技术从198份样本中共识别91 421个SNPs，杂合位点占5.85%。基于Nei’s的遗传距离在0.014~0.596，平均遗传距离为0.284。Bayes算法把198个样本聚为3个亚类。91 421个SNPs构成的总变异中，前3个主成分可分别解释群体变异的10.98%、10.47%、4.81%。15个表型性状的平均变异系数和平均多样性指数分别为30.33%和1.95。15个表型之间的相关性系数在-0.55~0.92。15个表型性状的前3个主成分可分别解释群体变异的29.44%、16.63%、10.59%，对第一主成分贡献大的性状包括穗长、株高、穗总粒数、播始历期、穗实粒数、叶长、垩白粒率和垩白度，8个性状对第一主成分的贡献值绝对值都在0.6以上，是籼稻表型性状变异的主要因素。基于表型的前5个主成分反映总信息量的73.003%，前2个主成分将198份资源分为2个亚组。Mantel检验表明，SNPs和表型性状的遗传距离矩阵之间的r为0.041。综上，SNPs和15个表型性状的多样性分析之间相关性很低，SNPs聚类比表型性状聚类更接近系谱分析。秦巴地区198份籼稻种质资源SNPs构成的群体遗传结构相对简单。表型性状变异较丰富，多样性程度高，群体间性状差异显著。综上，穗长、株高、穗总粒数、播始历期、穗实粒数、叶长、垩白粒率和垩白度这8个性状可作为秦巴地区籼稻种质资源表型性状的综合评定指标。 |
| 关键词: 籼稻 表型性状 遗传多样性 群体结构 SNP |
| DOI：10.11841/j.issn.1007-4333.2023.08.07 |
| 投稿时间：2022-10-04 |
| 基金项目:陕西省科学技术厅科技成果转移与推广计划-百项科技成果转化行动项目(2022CGBX-01) |
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| Genetic diversity of Indica rice germplasm resources based on SNP and phenotypic markers |
|
HE Qiaoqiao1,ZHOU Xixi1,WANG Yewen2,LI Peijiang2,WANG Shengbao2,ZHANG Yu1*
|
| (1.College of Biological science and Engineering/Shaanxi Province Key Laboratory of Bio-resources, Shaanxi University of Technology, Hanzhong 723000, China;2.Shaanxi Rice Research Institute, Hanzhong 723000, China) |
| Abstract: |
| The aims of this study were to enrich the genetic diversity information of Indica rice phenotypic traits and excavate excellent genetic materials. The results showed that: A total of 91 421 SNPs were obtained from 198 samples using two simplified genome sequencing techniques. The heterozygous sites accounted for 5. 85%. The genetic distance based on Nei's ranged from 0. 014 to 0. 596, with an average genetic distance of 0. 284. The 198 samples were clustered into three subclasses by Bayes algorithm. Among the total variation of 91 421 SNPs, the first three PC could explain 10. 98%, 10. 47% and 4. 81% of the population variation, respectively. The average coefficient of variation and the average diversity index based on the 15 phenotypic traits were 30. 33% and 1. 95, respectively. The correlation coefficients between the 15 phenotypes ranged from -0. 55 to 0. 92. The first three PCs of 15 phenotypic traits could explain 29. 44%, 16. 63% and 10. 59% of the population variation, respectively. The traits that had significant contributions on the first principal component included spike lengths, plant heights, kernel numbers per spike, the period from seeding to heading, grain number, leaf length, chalky rice rate and chalkiness. The contribution values of 8 traits to the first principal component were all above 0. 6, and were the main factor for the variation of Indica phenotypic traits. The first five principal components of the phenotype group reflected 73. 003% of the total information, and the first two principal components divided the 198 resources into two subgroups. The Mantel test showed that the coefficient of correlation between the genetic distance matrix based on SNPs and phenotypic traits was r=0. 041. In summary, the correlation between SNPs and the diversity analysis of 15 phenotypic traits was low, and the SNPs cluster was closer to the genealogical analysis than the phenotypic trait cluster. The genetic structure of the population consisting of the 198 SNPs of Indica rice germplasm resources in the Qinling-Ba region was relatively simple. Eight traits could be used as comprehensive evaluation indexes for the phenotypic traits in Indica rice. |
| Key words: Indica rice phenotypic traits genetic diversity population structure SNP |
