Escuela de Ciencias del Mar, Pontificia Universidad Católica de Valparaíso, Valparaíso, Chile.
Genet Sel Evol. 2010 Jun 11;42(1):19. doi: 10.1186/1297-9686-42-19.
In this study, we used different animal models to estimate genetic and environmental variance components on harvest weight in two populations of Oncorhynchus kisutch, forming two classes i.e. odd- and even-year spawners.
The models used were: additive, with and without inbreeding as a covariable (A + F and A respectively); additive plus common environmental due to full-sib families and inbreeding (A + C + F); additive plus parental dominance and inbreeding (A + D + F); and a full model (A + C + D + F). Genetic parameters and breeding values obtained by different models were compared to evaluate the consequences of including non-additive effects on genetic evaluation.
Including inbreeding as a covariable did not affect the estimation of genetic parameters, but heritability was reduced when dominance or common environmental effects were included. A high heritability for harvest weight was estimated in both populations (even = 0.46 and odd = 0.50) when simple additive models (A + F and A) were used. Heritabilities decreased to 0.21 (even) and 0.37 (odd) when the full model was used (A + C + D + F). In this full model, the magnitude of the dominance variance was 0.19 (even) and 0.06 (odd), while the magnitude of the common environmental effect was lower than 0.01 in both populations. The correlation between breeding values estimated with different models was very high in all cases (i.e. higher than 0.98). However, ranking of the 30 best males and the 100 best females per generation changed when a high dominance variance was estimated, as was the case in one of the two populations (even).
Dominance and common environmental variance may be important components of variance in harvest weight in O. kisutch, thus not including them may produce an overestimation of the predicted response; furthermore, genetic evaluation was seen to be partially affected, since the ranking of selected animals changed with the inclusion of non-additive effects in the animal model.
本研究采用不同动物模型,对虹鳟两个繁殖群体(奇数和偶数产卵年份)的收获体重进行遗传和环境方差分量估计,构建两类模型,即加性模型、加性模型加近交(A+F 和 A);加性模型加全同胞家庭和近交的共同环境(A+C+F);加性模型加亲本显性和近交(A+D+F);以及完全模型(A+C+D+F)。比较不同模型获得的遗传参数和育种值,以评估包括非加性效应对遗传评估的影响。
将近交作为协变量不会影响遗传参数的估计,但当包含显性或共同环境效应时,遗传力会降低。当使用简单的加性模型(A+F 和 A)时,两个群体的收获体重遗传力均较高(偶数=0.46,奇数=0.50)。当使用完全模型(A+C+D+F)时,遗传力降低至 0.21(偶数)和 0.37(奇数)。在完全模型中,显性方差的大小为 0.19(偶数)和 0.06(奇数),而两个群体的共同环境效应大小均低于 0.01。在所有情况下,不同模型估计的育种值之间的相关性都非常高(即高于 0.98)。然而,当估计出较高的显性方差时,不同世代的 30 只最佳雄性和 100 只最佳雌性的排名会发生变化,在两个群体中的一个群体(偶数)中就是如此。
显性和共同环境方差可能是虹鳟收获体重方差的重要组成部分,因此不包括这些方差可能会导致预测响应的高估;此外,遗传评估受到一定程度的影响,因为在动物模型中加入非加性效应会改变所选动物的排名。