中国农业科学 ›› 2017, Vol. 50 ›› Issue (13): 2433-2441.doi: 10.3864/j.issn.0578-1752.2017.13.003

• 作物遗传育种·种质资源·分子遗传学 • 上一篇    下一篇

基于表型和SSR分子标记构建芝麻核心种质

刘艳阳1,梅鸿献1,杜振伟1,武轲1,郑永战1,崔向华2,郑磊3

 
  

  1. 1河南省农业科学院芝麻研究中心,郑州 4500022河南省驻马店市农业科学院,河南驻马店 463000;3河南省漯河市农业科学院,河南漯河 462300
  • 收稿日期:2017-01-03 出版日期:2017-07-01 发布日期:2017-07-01
  • 通讯作者: 郑永战,E-mail:sesame168@ 163.com
  • 作者简介:刘艳阳,E-mail:liuyanyang001@163.com。梅鸿献,E-mail:meihx2003@126.com。刘艳阳与梅鸿献为同等贡献作者。
  • 基金资助:
    国家自然科学基金(31301359)、现代农业产业技术体系建设专项资金(CARS-15)、河南省基础与前沿技术研究计划(152300410143,162300410164)

Construction of Core Collection of Sesame Based on Phenotype and Molecular Markers

LIU YanYang1, MEI HongXian1, DU ZhenWei1, WU Ke1, ZHENG YongZhan1, CUI XiangHua2, ZHENG Lei3   

  1. 1Sesame Research Center, Henan Academy of Agricultural Sciences, Zhengzhou 450002; 2Zhumadian Academy of Agricultural Sciences, Zhumadian 463000, Henan; 3Luohe Academy of Agricultural Sciences, Luohe 462300, Henan
  • Received:2017-01-03 Online:2017-07-01 Published:2017-07-01

摘要: 【目的】便于管理、研究和利用芝麻种质资源,为芝麻育种提供优异基因资源。【方法】利用新收集和种质库保存的5 020份芝麻种质资源为基础,首先基于标准化的表型数据按地理来源分组后采用组内比例法聚类抽样构建初级核心种质,然后基于SSR分子标记应用位点优先取样策略逐步聚类,使用t检验检测每次聚类形成的核心种质与初级核心种质的Nei’s基因多样度(He)和Shannon-Wiener指数(I),直到核心种质的遗传多样性与初级核心种质开始有显著差异时,终止多次聚类取样,选择上一个与初级核心资源没有显著差异的核心种质作为最佳核心种质。利用Nei’s 多样性指数、Shannon-Wiener多样性指数、多态条带百分率(PB,%)、多态条带保留率(PBR,%)、变异系数符合率(VR)、极差符合率(CR)、方差差异百分率(VD,%)、均值差异百分率(MD,%)等参数进行核心种质代表性检验和评价。【结果】构建了含有816份资源的初级核心种质和含有501份资源的核心种质,分别占全部种质资源的16.25%和9.98%;核心种质包括国内资源442份,国外资源59份;Nei's基因多样度(0.2789)和Shannon-Wiener指数(0.4243)在P<0.05概率条件下与初级核心资源(He=0.2791,I=0.4302)无显著性差异,多态条带百分率(PB,%)、多态条带保留率(PBR,%)、变异系数符合率(VR)、极差符合率(CR)分别为91.25%、95.23%、99.14%、86.85%。方差差异百分率(VD,%)和均值差异百分率(MD,%)均为0。t测验结果表明,核心种质的遗传多样性指数与原始种质差异不显著。位点优先取样策略构建的核心种质比对照随机取样策略丢失的多态性位点数少,且同一遗传距离下位点优先取样策略构建的核心种质具有更高的遗传多样性,更能构建一个具有代表性的核心种质,Shannon-Wiener多样性指数比Nei’s多样性指数检测效率高。【结论】基于地理来源分组,组内按表型数据聚类按比例法抽样构建芝麻初级核心种质,再结合SSR分子标记数据,采用SM相似系数进行UPGMA逐步聚类是构建芝麻核心种质较适宜的方法,所构建的核心种质较好地代表了基础种质的遗传多样性。

关键词: 芝麻, 种质资源, 核心种质, 代表性检验

Abstract: 【Objective】The objective of this study was to manage, research and utilization of sesame (Sesamum indicum L.) germplasm resources more effectively, and to provide excellent genetic resources for sesame breeding.【Method】In this study, 5 020 accessions of sesame germplasm resources were systematically identified. Firstly, the primary core collections were constructed by using proportion strategy and UPGMA clustering sampling method within subgroups according to geographical origins. Then using an allele preferred sampling strategy and stepwise UPGMA clustering sampling approach according to SSR molecular data, these accessions were further screened to form core collections. The Nei’s gene diversity (He) and the Shannon-Wiener index (I) of the core collection and the primary one were measured by t-test. The cluster sampling was terminated until the genetic diversity of the core collection begun to have a significant difference with the primary one. Then the core collections without a significant difference with the primary core collection were chosen as the best core collections. The representativeness of the core collections was assessed by the Nei’s diversity index, Shannon-Wiener diversity index, percentage of polymorphic bands, polymorphic band retention, variable rate of coefficient of variation, coincidence rate of range, variance difference percentage and mean difference percentage.【Result】The primary core collections containing 816 accessions and core collections with 501 accessions were constructed, accounting for 16.25% and 9.98% of the total germplasm resources, respectively. The core collections consist of 442 Chinese landraces and 59 foreign germplasm resources. The core collection with 0.2989 in Nei’s diversity index and 0.4243 in Shannon-Wiener diversity index, and did not have a significant difference in molecular diversity with primary core collections (He=0.2791, I=0.4302) at P<0.05. The percentage of polymorphic loci and reserved rate of number of polymorphic loci, variable rate of coefficient of variation and coincidence rate of range were 91.25%, 95.23%, 99.14%, 86.85%, respectively. Variance difference percentage and phenotypic indexes of mean difference percentage was 0. Results of t-test showed that no significant difference was found in genetic diversity indexes between the core collections and original collections. Compared with the random sampling strategy, allele preferred sampling strategy could construct more representative core collections with higher values of genetic diversity indexes and fewer loss of allele. The Shannon-Wiener index performed higher identifying efficiency than Nei’s diversity index. 【Conclusion】The primary core collections were constructed by using proportion strategy and clustering sampling method within subgroups according to geographical origin, and then using an allele preferred sampling strategy and stepwise UPGMA clustering sampling approach according to SSR molecular data to form core collections, which is a suitable method for constructing sesame core collections. The core collections of sesame are well representative of the original collections in the phenotypic and molecular genetic diversity.

Key words: sesame, germplasm resources, core collection, representative test