中国农业科学 ›› 2018, Vol. 51 ›› Issue (15): 2846-2859.doi: 10.3864/j.issn.0578-1752.2018.15.002

• 作物遗传育种·种质资源·分子遗传学 • 上一篇    下一篇

基于SSR标记的黍稷种质资源遗传多样性及亲缘关系研究

薛延桃1,2,陆平2,乔治军3,刘敏轩2,王瑞云1,3

 
  

  1. 1山西农业大学农学院,山西太谷 030801;2中国农业科学院作物科学研究所,北京 100081;3山西省农业科学院农作物品种资源研究所/农业部黄土高原作物基因资源与种质创制重点实验室/杂粮种质资源发掘与遗传改良山西省重点实验室,太原 030031
  • 收稿日期:2018-05-07 出版日期:2018-08-01 发布日期:2018-08-01
  • 通讯作者: 刘敏轩,Tel:010-62159962;E-mail:liuminxuan@caas.cn。王瑞云,Tel:15234420135;E-mail:wry925@126.com
  • 作者简介:薛延桃,Tel:18234839761;E-mail:yantaoxue305@163.com
  • 基金资助:
    国家自然科学基金(31271791)、山西省回国留学人员科研资助项目(2016-066)、国家现代农业产业技术体系建设专项(CARS-06-13.5-A16)、农业部谷子高粱产业体系(CARS-07-12[1].5-A1)、农业部作物种质资源保护项目(NB2012-2130135-25-06-1)

Genetic Diversity and Genetic Relationship of Broomcorn Millet (Panicum miliaceum L.) Germplasm Based on SSR Markers

XUE YanTao1,2, LU Ping2, QIAO ZhiJun3, LIU MinXuan2, WANG RuiYun1,3   

  1. 1 College of Agriculture, Shanxi Agricultural University, Taigu 030801, Shanxi; 2 Institute of Crop Science, Chinese Academy of Agricultural Sciences, Beijing 100081; 3 Institute of Crop Germplasm Resources, Shanxi Academy of Agricultural Sciences/Key Laboratory of Crop Gene Resources and Germplasm Enhancement on Loess Plateau, Ministry of Agriculture/Shanxi Key Laboratory of Genetic Resources and Genetic Improvement of Minor Crops, Taiyuan 030031
  • Received:2018-05-07 Online:2018-08-01 Published:2018-08-01

摘要: 【目的】利用SSR标记,分析黍稷种质资源(野生材料和地方品种)的遗传多样性水平,揭示不同来源黍稷种质资源的亲缘关系和遗传群体结构差异,为黍稷起源进化研究奠定基础。【方法】用6份地理差异显著的黍稷种质资源对137对小宗作物课题组开发的具有多态性的SSR引物进行初步筛选,最终筛选103对条带清晰、扩增良好且多态性稳定的SSR引物,利用这103对多态性SSR标记对146份黍稷材料进行PCR扩增,通过遗传参数、聚类、遗传结构等分析,评估不同个体间及不同群体间的遗传多样性,探讨遗传结构差异。【结果】103对SSR标记共检测出308个等位基因(Na),平均值为2.99,平均Shannon-Weaver指数(I)为0.8478,平均期望杂合度为0.3642,平均多态性信息含量指数(PIC)为0.5544。103对SSR标记的分布区间为0—1、1—2、2—3、3—4和4—5,分辨率范围为0.334—4.002,77.67%的标记分布于区间1—4,具有适度分辨力。国内资源的观测等位基因数(2.9126)、多样性指数(0.8302)、期望杂合度(0.5023)、多态性信息含量指数(0.5278)均高于国外资源,遗传多样性更丰富。12个群体的遗传距离的变化范围为0.0783—0.5762,均值为0.2938;遗传一致度变化范围为0.5620—0.9247,均值为0.75,遗传相似性与地理分布具有一定相关性,地理分布越近,遗传距离越小,遗传一致度越高。聚类分析在遗传距离为0.15处可以把12个群体分为4个组群,其中南美洲和山西资源各自独立分为一支,与其他资源亲缘关系较远。个体间聚类中,国内外资源划分非常显著,在遗传距离为0.63处,146份黍稷资源可分为3大组群,组群Ⅰ和组群Ⅱ为国外资源,组群Ⅲ为国内资源。组群Ⅱ在遗传距离为0.39处又分为3个亚群,组群Ⅲ在遗传距离为0.45处分为5个亚群,其中亚洲与欧洲资源、中国河北与中国山西、中国内蒙古资源的遗传关系较近。遗传结构分析结果显示国内外群体间存在明显的遗传分化,其中5个组群(组群2、组群5、组群6、组群7和组群9)为国内野生资源特有基因型,分布较为分散;2个组群(组群1和组群4)为国外资源特有基因型,分布较为集中。中国宁夏、南美洲资源的群体结构趋向单一化,中国河北、中国黑龙江、亚洲资源的群体结构趋向多元化。UPGMA聚类结果与遗传结构分析结果一致,且不同地区黍稷资源群体间遗传关系远近均与其地理分布相关。【结论】野生资源的遗传多样性高于国外资源,其中中国河北群体的遗传多样性最丰富,中国河北可能是黍稷的起源中心。

关键词: 黍稷, 野生糜子, 国外品种, SSR标记, 遗传多样性, 群体结构

Abstract: 【Objective】The objective of this study is to analyze the genetic diversity and relationship of broomcorn millet landraces and wild materials by SSR markers, to provide available data for further evolutionary study of broomcorn millet.【Method】137 SSR primers are used to identify polymorphisms in six representatives which selected randomly from the total of accessions. A total of 103 primers produce clear and reproducible polymorphic fragments among the six accessions and then are used to amplify 146 broomcorn millet accessions. Genetic diversity and relationship between different individuals and populations is evaluated by analyzing genetic parameter, clustering, and genetic structure.【Result】 103 SSR markers detect a total of 308 alleles (Na) with an average of 2.99 for each SSR and the mean values of Shannon-Weaver index (I), Nei and PIC were 0.8478, 0.3642 and 0.5544, respectively. Their resolution range was 0.334-4.002 and more than 60% distribution at intervals of 1-4, indicated the moderate resolving power of these SSR. The observed number of alleles (2.9126), Shannon-Weaver index (0.8302), expected heterozygosity (0.5023), and PIC value (0.5278) of broomcorn millet accessions in China were all higher than those in abroad, indicated more abundant genetic diversity in Chinese samples. The genetic distance of the 12 populations ranged from 0.0783 to 0.5762 with a mean of 0.2938. The genetic identity ranged from 0.5620 to 0.9247 with a mean of 0.75. We found that the genetic similarity had a certain correlation with geographical distribution. The closer geographical distribution, the smaller genetic distance, the higher genetic identity. Cluster analysis divided 12 populations into 4 groups at a genetic distance of 0.15. Among them, resources in South America and Shanxi were each independently divided into one group, which had a far-distance relationship with other resources. In the inter-individual clustering, the division of resources at home and abroad was very significant. At a genetic distance of 0.63, 146 broomcorn millet accessions could be divided into three groups. Group Ⅰand group Ⅱ were foreign accessions, and group Ⅲ was domestic accessions. Further, group Ⅱ was divided into three subpopulations at a genetic distance of 0.39, and group Ⅲ was divided into five subpopulations at a genetic distance of 0.45. There had closer genetic relationship between Asia and European resources, as well as Hebei, Shanxi and Inner Mongolia in China resources than other populations. The result of genetic structure analysis showed that there is obvious genetic differentiation between the domestic populations and foreign populations. Five groups (Group 2, Group 5, Group 6, Group 7 and Group 9) were unique genotypes which owned by Chinese wild resources and distributed more widely, 2 groups (Group 1 and Group 4) were unique genotypes of foreign resources and have a relative narrow distribution. The population structures of Ningxia and South America tend to be independent, and the population structures of Hebei, Heilongjiang and Asia tend to be diversified. The UPGMA clustering results were consistent with the results of genetic structure analysis, and the genetic relationships were related to their geographical distribution.【Conclusion】The genetic diversity of wild accessions is higher than that of landraces, of which Hebei population has the most abundant genetic diversity, so we suppose Hebei province may be the sub-origin center of broomcorn millet.

Key words: Panicum miliaceum L, wild broomcorn millet, foreign germplasm, SSR markers, genetic diversity, population structure