应用随机回归模型估计蛋鸡体重遗传参数

Using Random Regression Models to Estimate Genetic Parameters on Body Weights in Layers

【目的】通过分析勒让德多项式阶数对最大似然值、残差的影响,优化随机回归模型,评估蛋鸡资源群体体重遗传潜能和选择时机,为蛋鸡资源群体育种方案提供参数。【方法】收集东乡绿壳蛋鸡与白莱航鸡F2资源群体体重数据26532条。系谱数据包含5871只鸡,其中4174只鸡有5条记录,802只鸡有4条记录,128只鸡没有记录。数据清洗包括去除离群值数据、去除翅号重复个体、去除性别不明个体、去除少于4条记录个体。经整理,剩余25483条体重数据,其中绿壳蛋鸡2223条,白莱航鸡696条,F1代6002条,F2代16562条。应用SPSS软件中一般线性模型分析非加性遗传因素对体重的影响,确定将批次、性别列入动物模型固定效应。应用随机回归模型分析蛋鸡早期体重方差组分、遗传参数、随机回归系数矩阵特征向量。随机回归动物模型中包括一般固定效应、固定回归项及随机回归项三类效应。研究中,以批次-性别作为固定效应,以周龄体重作为固定回归项,将加性遗传效应和永久环境效应作为随机回归项。经AIC、BIC筛选,随机回归模型中加性遗传效应宜嵌入5阶勒让德多项式、永久环境效应宜嵌入5阶勒让德多项式、固定回归项宜嵌入2阶勒让德多项式。残差做异质化处理,分为5个水平,即每次观测设定一个残差初始值,观测间隔期残差以线性回归计算。【结果】蛋鸡资源群体1—9周龄体重遗传力为0.46—0.63,重复力为0.88—0.92,遗传相关系数为0.32—0.99,永久环境相关系数为0.34—0.99。遗传相关系数随着周龄间隔增大而减小,相邻周龄遗传相关系数较高。遗传方差、永久环境方差以及残差随年龄增加而增加。加性遗传效应随机回归系数矩阵前三个特征值依次为1976.91、161.95、42.22,前三个特征值合计解释99%遗传变异。【结论】随机回归模型可用于蛋鸡早期体重遗传评估及选育。对加性遗传系数矩阵第二特征方程系数进行选择可以改变个体生长曲线,选择时机宜在3—6周龄。蛋鸡资源群体早期体重遗传力略高于其它群体同类研究结果。

【Objective】This study was to assess the effect of the orders of Legendre polynominals on the size of the maximum likelihood and the error,to optimize the random regression model,to evaluate the genetic potential and selection knot of layer resource population and to provide parameters for optimal layer breeding scheme for resource population.【Method】The data set consisted of 26532 items collected from the layer resource population,which set up by White Leghorn reciprocal crossing with the blue eggshell chickens.The pedigree consisted of 5871 individuals,including 4147 chickens with 5 records,802 chickens with 4 records and 128 chickens without records. The standard of data cleaning included: i. removing outlier;ii. eliminating repeatedindividuals;iii. getting rid of unknown sexed individuals;iv. individuals with less than 4 records were also excluded. After datacleaning procedures, 25 483 records on body weight could be used in the next step, 2 223 of which collected from blue shelledchickens, 696 of which collected from White Leghorn, 6 002 of which collected from F1 generation and 16 562 of which collectedfrom F2 generation. The influence of the nongenetic factors on body weights was analyzed by GLM in SPSS. The fixed effects ofanimal model included batch and sex factors. Using the random regression model, variance components, genetic parameters andeigenvectors were obtained. The model included general fixed effect and fixed regression, random regression. In this study, batch-sexwas the fixed effects, and a fixed regression was fitted for week age body weight effects;the direct additive genetic, permanentenvironment were the random effects. Comparing with AIC and BIC values, the best model should embed 2nd Legendre polynomialsinto fixed effects, 5th Legendre polynomials into additive genetic effects and permanent environmental effects. Heterogeneousresidual variance was grouped into 5 levels. Each observation was set an initial estimate. The residual variance between theneighboring observations was treated as a linear regression.【Result】 For the body weights on resource population, heritability wasranged from 0.46 to 0.63, repeatability varied from 0.88 to 0.92, the genetic correlation was ranged from 0.32 to 0.99, and permanentenvironmental correlation was varied from 0.34 to 0.99. The genetic correlations among the weeks reduced with the intervalsincreased, high correlations occurred between the neighboring weeks. The genetic variance, permanent environmental variance andresidual variance increased with ages. The first three eigenvalues of additive genetic effects was 1 976.91, 161.95, and 42.22,respectively, and these eigenvalues could explain 99% of total variations. 【Conclusion】The genetic parameters on the early bodyweights in laying chickens were estimated with a random regression model. The individual growth curve could be altered byselection on the coefficients associated with the second eigenfunction. The right time seemed to select on 3 to 6 week. Estimates ofheritability in the resource population were larger than the results in the literatures.