Genetic diversity of weedy rice (Oryza sativa f. spontanea) populations in Sri Lanka: An application of Self Organizing Map (SOM)

IF 0.5 Q4 AGRONOMY
S. Weerakoon, S. Somaratne
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引用次数: 2

Abstract

Abstract. Weerakoon SR, Somaratne S. 2021. Genetic diversity of weedy rice (Oryza sativa f. spontanea) populations in Sri Lanka: An application of Self Organizing Map (SOM). Asian J Agric 5: 35-43. Weedy rice (WR) (Oryza sativa f. spontanea) has become a major threat in rice cultivation. Discrimination of WR from cultivated rice is difficult since agro-morphology of WR and cultivated rice are overlapping. Molecular markers are useful and can be an informative tool for estimating genetic diversity and relationships in closely related WR eco-types. Self-Organizing Maps (SOM) is an interesting and promising classification tool employing an innovative and data-driven classification method based on unsupervised artificial neural networks. The present study focused on exploring the potential use of SOM to classify WR populations of different eco-climatic zones in Sri Lanka using agro-morphological and molecular data. Separate SOMs for each set of variables, agro-morphological and molecular data were developed. The best SOM was chosen based on the error performance. Findings of SOM analyses showed that certain morphological characters (seedling height, leaf blade width, leaf blade length, culm strength, panicle shattering, seed coat color and leaf angle) and certain molecular characters detected from SSR primers (RM 11, RM 21, RM 14, and RM 280) are important in separation of different WR eco-types satisfactorily. SOM clustering of cultivated, wild, and WR eco-types indicated specific patterns of grouping with respect to climatic conditions of the country. WR eco-types in dry zone and wet zone of the country are closely related to Oryza nivara and O. rufipogon respectively.
斯里兰卡杂草稻(Oryza sativa f. spontanea)居群遗传多样性:自组织图谱(SOM)的应用
摘要苏玛拉特尼。2021。斯里兰卡杂草稻(Oryza sativa f. spontanea)居群遗传多样性:自组织图谱(SOM)的应用。农业学报,5:35-43。杂草稻(Oryza sativa f. spontanea)已成为水稻种植的主要威胁。WR与栽培稻的农业形态存在重叠,因此很难从栽培稻中进行区分。分子标记是一种有用的信息工具,可用于估计密切相关的WR生态型的遗传多样性和关系。自组织地图(SOM)是一种基于无监督人工神经网络的创新的数据驱动分类方法,是一种有趣且有前途的分类工具。本研究的重点是探索SOM在斯里兰卡利用农业形态学和分子数据对不同生态气候带的野生动物种群进行分类的潜力。为每组变量开发了单独的SOMs,农业形态和分子数据。根据误差性能选择最佳SOM。SOM分析结果表明,从SSR引物(RM 11、RM 21、RM 14和RM 280)中检测到的某些形态特征(幼苗高度、叶片宽度、叶片长度、茎秆强度、穗裂、种皮颜色和叶片角度)和某些分子特征(RM 11、RM 21、RM 14和RM 280)对不同生态类型的WR分离具有重要意义。栽培、野生和野生生态类型的SOM聚类显示了与该国气候条件相关的特定分组模式。旱区和湿区WR生态类型分别与水稻(Oryza nivara)和水稻(O. rufipogon)密切相关。
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