基因聚合是通过优化设计杂交方案, 选择利用目标基因或与其紧密连锁的分子标记, 通过世代选择实现将来源于多个不同群体的优势目标基因或基因型聚合到同一个理想个体中, 进而达到生产出超级经济性状个体的目的。针对聚合不同目标基因个数, 设计4类杂交方案——两群体、三群体、四群体级联、四群体对称。在相同的杂交方案中, 比较基因型选择和表型选择策略, 分析不同杂交组合、性状遗传力、初始基因频率、基础群体规模对聚合设计的影响, 并筛选出最佳的聚合方案。研究结果表明, 在较大的基础群体规模和较高初始群体基因频率下, 获得聚合多个目标基因的理想个体的可能性较大。在四群体杂交方案中, 亲本的杂交次序对于级联杂交比对称杂交的影响较大。模拟结果表明, 运用基因型选择进行聚合育种优于表型选择。文章所开发设计的聚合模拟育种的统计分析方法和相应软件为指导杂交育种方案和选择策略的设计提供理论参考, 同时, 为进一步设计开发聚合设计模拟育种平台奠定基础。
Gene pyramiding aims at producing individuals with one superior economic trait according to the optimal breeding scheme involving selection of favorable target alleles or linked markers after crossing basal populations and pyramiding them into a single individual. In consideration of animal traditional cross program along with the features of animal segregating population, four types of cross programs and two types of selection strategies for gene pyramiding are performed from practice perspective of view, two population cross for pyramiding two genes (denoted II), three populations cascading cross for pyramiding three genes (denoted III), four population symmetrical (denoted IV-S) and cascading cross for pyramiding four genes (denoted IV-C), and various schemes (denoted cross program-A-E) were designed for each cross program with different levels of initial favorable allele frequencies, basal population sizes, and trait heritabilities. The process of gene pyramiding for various schemes were simulated and compared based on the population hamming distance, average superior genotype frequencies, and average phenotypic values. By simulation, the results showed that larger base population size and higher initial favorite allele frequency resulted in higher efficiency of gene pyramiding. The order of parent crossing was shown to be the most important factor in cascading cross, but had no significant influence on the symmetric cross. The results also showed that genotypic selection strategy was superior to phenotypic selection in accelerating gene pyramiding. The method and corresponding software would be used to compare different cross schemes and selection strategies. Moreover, our study would help to build the optimal gene pyramiding simulation platform.
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