摘要
将连续性的基因型值数据和间断性的分子标记数据整合建立混合遗传距离,对比了应用混合遗传距离和单纯应用基因型遗传距离构建植物遗传资源核心子集的效果.应用混合线性模型中的调整无偏预测法(AUP)预测基因型值,结合不加权类平均法(UPGMA)逐步聚类构建遗传资源群体的核心子集,并检测一系列核心子集的代表性评价参数.采用包含8个农艺性状和60个SSR标记信息的水稻群体数据验证混合遗传距离的有效性.结果表明,采用混合数据构建的核心子集比单纯的基因型值数据构建的核心子集更有代表性.主成分分析结果验证了该结论的可靠性.
In present paper, core subset constructed by mixed data of continuous genotypic values and discrete molecular marker information was compared with that by single data of genotypic values. The mixed genetic distance was adopted to combine the continuous data and the discrete data for clustering. The method of stepwise clusters was used for constructing core subset. Adjusted unbiased prediction （AUP） method of mixed linear model was adopted to unbiasedly predict genotypic values. The unweighted pair-group average method （UPGMA） was used to perform stepwise clusters to construct core subset. A series of parameters for evaluating the representativeness of core subset were suggested. A rice germplasm group with 8 quantitative traits and information of 60 SSR markers was used to evaluate the validity of the new strategy. The results suggested that core subset constructed based on mixed data was more representative than that constructed based on genotypic values. The principal components analysis validated the results.
出处
《生物数学学报》
CSCD
北大核心
2011年第2期269-278,共10页
Journal of Biomathematics
基金
National Natural Science Foundation of China(No.30270759)
the Natural Science Foundation of Shandong Province,China(No.ZR2010CQ016)
the Scientific Research Foundation for PhDs in Shandong Academy of Agricultural Sciences(No. 2007YBS005)
关键词
核心子集
逐步聚类法
混合线性模型
基因型值
分子标记信息
Core subset
Stepwise cluster
Mixed linear model
Genotypic value
Molecular marker information
