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以对大豆猝死病抗性不同的大豆品系U01-390489×E07080所衍生的F4:5代96个家系的C2连锁群上50个多态性SNP标记位点为基础数据,对比分析了利用Joinmap4.0及Icimapping 2个软件不同计算方法所构建的遗传图谱的异同及优劣.结果表明,Icimapping构建的图谱长度较Joinmap4.0所建图谱短,Icimapping包含的几种算法构建的图谱长度相近,Joinmap中Regression的计算方法所构建的遗传图谱与soybase上的一致图谱长度最为接近,Maximum Likelihood算法得出的图谱长度与Fixed Order算法图谱长度相近.Icimapping的构建的图谱中,较多的标记聚集于同一位点,其中的几种算法相近.Joinmap中regression算法的图谱结果乱序标记较多,Maximum Likelihood构建的图谱乱序标记较少,与Fixed Order的结果更为接近.研究结果表明,Joinmap的Maximum Likelihood算法更适合于高密度的SNP标记的图谱构建.
Based on the 50 polymorphic SNP loci in the C2 linkage group of 96 F4: 5 generations derived from the soybean line U01-390489 × E07080, which had different resistance to soybean sudden-death disease, the data of Joinmap4 were comparatively analyzed. 0 and Icimapping2.The results show that the length of the map constructed by Icimapping is shorter than that of Joinmap4.0, and the lengths of the maps constructed by Icimapping are similar, Joinmap The results showed that the genetic map constructed by Regression was closest to that of soybase, and the Maximum Likelihood algorithm had the same length as that of the Fixed Order algorithm.Icimapping constructed more maps in the same And some of these algorithms are similar.Joinmap regression algorithm has many outlier markers and Maximum Likelihood is less than that of Fixed Order.The results show that Joinmap Maximum Likelihood algorithm is more suitable for high-density SNP marker map construction.