摘要
采用动物模型 BL UP方法 ,利用连续 2个世代白来航蛋鸡 3个纯系的系谱资料和产蛋量记录 ,对产蛋量的两阶段选择进行了优化研究。在总留种率 30 %的情况下 ,比较了第 1阶段留种率从 30 %到 10 0 %变化时对后代遗传进展的影响。结果表明 ,采用两阶段选择时产蛋量的遗传进展比常规 4 3周龄早期一次选择提高2 2 .89%以上。遗传进展随着第 1阶段留种率的升高而增大。当第 1阶段留种率超过 70 %的时候 ,增长趋势逐渐趋于平缓。青年鸡选择方法 (第 1阶段不选 ,10 0 %留种 ,第 2阶段 30 %留种 ) ,在遗传进展上对两阶段选择只有微弱优势 ,却有很高的育种投入。考虑到育种进展和育种投入 ,两阶段选择中第 1阶段的留种率以 5 0 %～ 70 %为宜。由于亲本对后代的不均等遗传贡献对遗传进展有显著影响 ,结合对家系含量进行优化控制形成的两阶段选择方法可以使遗传进展提高 32 .8%～ 65 .1%
Effects of different allocation of selection proportion between the two selection stages on genetic gain of annual egg production were studied. Data were collected on three White Leghorn pure lines. Individual breeding values of egg production were estimated with Best Linear Unbiased Prediction based on Animal Model. Under the final intensity of 30%, genetic gains were compared while the selection proportion of the first stage was set within the range between 30% to 100%. It was shown that the more chickens selected in the first stage, the more genetic gains could be achieved. Genetic gains of the two stage selection was at least 22.89% more efficient than the routine selection based on early part record (30% selection proportion). However, when the selection proportion of the first stage was over 70%, the increase of genetic gain was getting less. Juvenile scheme showed slightly better genetic gains than the two stage selection when selection proportion in the first stage was over 70%, but the breeding cost was the most among all selection programs studied. Taking the genetic gains and breeding cost into account, the optimum selection proportion was proposed to be 50%～70% in the first stage of the two stage selection scheme. The unequal genetic contributions of parents into their offspring significantly affected the genetic gain. The two stage selection scheme which jointly controls family size within a line could increase genetic gain by 32.8%~65.1%.
出处
《中国农业大学学报》
CAS
CSCD
北大核心
2001年第5期108-112,共5页
Journal of China Agricultural University
基金
教育部高等学校优秀青年教师教学
科研奖励基金
关键词
产蛋量
两阶段选择
遗传进展
优化
蛋鸡
egg production
two stage selection
genetic gain
optimization
