基于序相关的作物育种评价性状特征选择方法
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北京市科技计划资助项目(D151100004215002、D151100004215004)


Phenotype Feature Selection for Crop Breeding Evaluation Based on Ranking Relevance
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    摘要:

    将育种家对作物性状表现的综合评价融入作物育种评价中,提出了一种基于序相关的作物育种评价性状特征选择方法。首先,从育种数据中筛选训练样本集及候选性状特征集合,计算候选性状特征集合中各性状与育种材料评价结果的相关性和作物在性状特征间表现的相似性,然后,利用计算的相关系数同时综合考虑性状表现的相似性系数,以期望选择的性状特征的相关性最大、相似性最小为目标,建立基于序相关的作物育种评价性状特征选择模型。该模型可为不同育种目标提供重点关注的性状特征集合,为数据化育种提供支持及依据。利用该方法对2013年大豆品系鉴定试验中早熟、中熟、毛豆3类育种材料进行了性状特征选择试验,结果验证了方法的有效性。该方法可以作为育种评价方法的前置步骤,与现有的综合评价、模糊综合评价等方法结合作为性状特征权重的确定手段,提高权重确定的科学性。

    Abstract:

    Traditional breeding evaluation methods focus on information of crop traits, while ignoring the previous evaluation results. In order to enhance the efficiency of material evaluation under the condition of large-scale breeding, the comprehensive evaluation of crop traits was integrated into the breeding evaluation, and a method of phenotype feature selection for crop breeding evaluation based on ranking relevance was proposed. Firstly, the training sample set and the candidate feature set were selected from breeding data, and the correlation between the phenotype feature and the results of evaluation and the similarity of the agronomic traits were calculated. Then, considering the characteristics of the maximum correlation coefficient and the minimum similarity, a model of phenotype feature selection for breeding evaluation based on ranking relevance was constructed. The established model can be used for different breeding objectives focusing on collection of characters, and validity of the model was verified by using three kinds of soybean identification trial in 2013. The model also can be used as the preprocess of breeding evaluation to determine the weights of the traits accurately.

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刘忠强,赵向宇,王开义,李民赞.基于序相关的作物育种评价性状特征选择方法[J].农业机械学报,2015,46(S1):283-289. Liu Zhongqiang, Zhao Xiangyu, Wang Kaiyi, Li Minzan. Phenotype Feature Selection for Crop Breeding Evaluation Based on Ranking Relevance[J]. Transactions of the Chinese Society for Agricultural Machinery,2015,46(S1):283-289.

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  • 收稿日期:2015-10-28
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  • 在线发布日期: 2015-12-30
  • 出版日期: 2015-12-31