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Identification of molecular markers associated with sweet potato resistance to sweet potato virus disease in Kenya

机译:肯尼亚与甘薯抗甘薯病毒病相关的分子标记的鉴定

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Sweet potato virus disease (SPVD), a result of the co-infection of whitefly transmitted Sweet potato chlorotic stunt virus (genus Crinivirus, family Closteroviridae) and the aphid transmitted Sweet potato feathery mottle virus (genus Potyvirus, family Potyviridae), is the most destructive disease of sweet potato in East Africa. A study was conducted to establish if genotypes identified as resistant or susceptible to SPVD in Kenya could be distinguished using molecular markers. A total of 47 unrelated sweet potato genotypes were selected from germplasm collections and classified into two phenotypic groups as resistant or susceptible to SPVD. Genotype selection was based on disease severity or days to symptom development in plants following graft inoculation. Amplified fragment length polymorphism (AFLP) marker profiles were generated for each individual and used in association studies to identify markers suitable for classifying the two pre-defined phenotypic groups. Analysis of molecular variance showed significant (P < 0.002) variation between the two groups using 206 polymorphic AFLP markers. Discriminant analysis and logistic regression statistical methods were used to select informative markers, and to develop models that would classify the two phenotypic groups. A training set of 30 genotypes consisting of 15 resistant and 15 susceptible were used to develop classification models. The remaining 17 genotypes were used as a test set. Four markers, which gave 100% correct classification of the training set and 94% correct classification of the test set, were selected by both statistical methods.
机译:烟粉虱传播的红薯叶绿体特技病毒(crinivirus属,Closteroviridae家族)和蚜虫传播的红薯羽毛状斑驳病毒(potyvirus属,Potyviridae家族)共同感染的结果是甘薯病毒病(SPVD)东非地瓜的破坏性疾病。进行了一项研究,以确定是否可以使用分子标记区分在肯尼亚被鉴定为对SPVD有抗药性或易感性的基因型。从种质资源库中选择了总共47种无关的甘薯基因型,并将其分为对SPVD耐药或易感的两个表型组。基因型选择基于疾病严重程度或移植后植株出现症状的天数。为每个个体生成扩增的片段长度多态性(AFLP)标记物谱并将其用于关联研究中,以鉴定适合于对两个预定义表型组进行分类的标记物。分子差异分析显示,使用206种多态性AFLP标记物,两组之间存在显着(P <0.002)差异。判别分析和逻辑回归统计方法用于选择信息标记,并开发出将两个表型组分类的模型。由30种基因型组成的训练集(包括15种抗药性和15种易感性)用于建立分类模型。其余的17个基因型用作测试集。通过两种统计方法均选择了四个标记,分别对训练集进行了100%正确分类和对测试集进行了94%正确分类。

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