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利用微卫星和基于基因的标记对小麦种质资源的遗传变异性和群体结构进行分析

An Analysis of Genetic Variability and Population Structure in Wheat Germplasm Using Microsatellite and Gene-Based Markers.

作者信息

Pour-Aboughadareh Alireza, Poczai Peter, Etminan Alireza, Jadidi Omid, Kianersi Farzad, Shooshtari Lia

机构信息

Seed and Plant Improvement Institute, Agricultural Research, Education and Extension Organization (AREEO), Karaj P.O. Box 31587-77871, Iran.

Botany Unit, Finnish Museum of Natural History, University of Helsinki, P.O. Box 7, FI-00014 Helsinki, Finland.

出版信息

Plants (Basel). 2022 Apr 29;11(9):1205. doi: 10.3390/plants11091205.

Abstract

Knowledge of the natural patterns of genetic variation and their evolutionary basis is required for sustainable management and conservation of wheat germplasm. In the current study, the genetic diversity and population structure of 100 individuals from four and species (including , , , and ) were investigated using two gene-based markers (start codon targeted (SCoT) polymorphism and CAAT-box derived polymorphism (CBDP)) and simple-sequence repeats (SSRs). The SCoT, CBDP, and SSR markers yielded 76, 116, and 48 polymorphism fragments, respectively. The CBDP marker had greater efficiency than the SCoT and SSR markers due to its higher polymorphism content information (PIC), resolving power (Rp), and marker index (MI). Based on an analysis of molecular variance (AMOVA) performed using all marker systems and combined data, there was a higher distribution of genetic variation within species than among them. and had the highest values for all genetic variation parameters. A cluster analysis using each marker system and combined data showed that the SSR marker had greater efficiency in grouping of tested accessions, such that the results of principal coordinate analysis (PCoA) and population structure confirmed the obtained clustering patterns. Hence, combining the SCoT and CBDP markers with polymorphic SSR markers may be useful in genetic fingerprinting and fine mapping and for association analysis in wheat and its germplasm for various agronomic traits or tolerance mechanisms to environmental stresses.

摘要

为了可持续管理和保护小麦种质资源,需要了解遗传变异的自然模式及其进化基础。在本研究中,利用两种基于基因的标记(起始密码子靶向(SCoT)多态性和CAAT框衍生多态性(CBDP))以及简单序列重复(SSR),对来自四个 和 物种(包括 、 、 和 )的100个个体的遗传多样性和群体结构进行了研究。SCoT、CBDP和SSR标记分别产生了76、116和48个多态性片段。由于其较高的多态性含量信息(PIC)、分辨能力(Rp)和标记指数(MI),CBDP标记比SCoT和SSR标记具有更高的效率。基于使用所有标记系统和组合数据进行的分子方差分析(AMOVA),物种内的遗传变异分布高于物种间。 和 在所有遗传变异参数上具有最高值。使用每个标记系统和组合数据进行的聚类分析表明,SSR标记在测试材料分组方面具有更高的效率,因此主坐标分析(PCoA)和群体结构的结果证实了所获得的聚类模式。因此,将SCoT和CBDP标记与多态性SSR标记相结合,可能有助于小麦及其种质资源的遗传指纹识别、精细定位以及各种农艺性状或环境胁迫耐受机制的关联分析。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d2fa/9103345/f13cb31a58aa/plants-11-01205-g001.jpg

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