Carillier-Jacquin Céline, Larroque Hélène, Robert-Granié Christèle
GenPhySE, INRA, INPT, ENVT, Université de Toulouse, 31326, Castanet-Tolosan, France.
Genet Sel Evol. 2016 Aug 4;48(1):54. doi: 10.1186/s12711-016-0233-x.
Genomic best linear unbiased prediction methods assume that all markers explain the same fraction of the genetic variance and do not account effectively for genes with major effects such as the α s1 casein polymorphism in dairy goats. In this study, we investigated methods to include the available α s1 casein genotype effect in genomic evaluations of French dairy goats.
First, the α s1 casein genotype was included as a fixed effect in genomic evaluation models based only on bucks that were genotyped at the α s1 casein locus. Less than 1 % of the females with phenotypes were genotyped at the α s1 casein gene. Thus, to incorporate these female phenotypes in the genomic evaluation, two methods that allowed for this large number of missing α s1 casein genotypes were investigated. Probabilities for each possible α s1 casein genotype were first estimated for each female of unknown genotype based on iterative peeling equations. The second method is based on a multiallelic gene content approach. For each model tested, we used three datasets each divided into a training and a validation set: (1) two-breed population (Alpine + Saanen), (2) Alpine population, and (3) Saanen population.
The α s1 casein genotype had a significant effect on milk yield, fat content and protein content. Including an α s1 casein effect in genetic and genomic evaluations based only on male known α s1 casein genotypes improved accuracies (from 6 to 27 %). In genomic evaluations based on all female phenotypes, the gene content approach performed better than the other tested methods but the improvement in accuracy was only slightly better (from 1 to 14 %) than that of a genomic model without the α s1 casein effect.
Including the α s1 casein effect in a genomic evaluation model for French dairy goats is possible and useful to improve accuracy. Difficulties in predicting the genotypes for ungenotyped animals limited the improvement in accuracy of the obtained estimated breeding values.
基因组最佳线性无偏预测方法假定所有标记解释相同比例的遗传方差,并且不能有效考虑具有主要效应的基因,如奶山羊中的αs1酪蛋白多态性。在本研究中,我们探究了在法国奶山羊的基因组评估中纳入可用的αs1酪蛋白基因型效应的方法。
首先,仅基于在αs1酪蛋白位点进行基因分型的公羊,将αs1酪蛋白基因型作为固定效应纳入基因组评估模型。表型母羊中不到1%在αs1酪蛋白基因上进行了基因分型。因此,为了将这些母羊的表型纳入基因组评估,研究了两种允许大量αs1酪蛋白基因型缺失的方法。首先基于迭代剥离方程为每个未知基因型的母羊估计每种可能的αs1酪蛋白基因型的概率。第二种方法基于多等位基因基因含量方法。对于测试的每个模型,我们使用三个数据集,每个数据集都分为训练集和验证集:(1)两个品种群体(阿尔卑斯山羊 + 萨能山羊),(2)阿尔卑斯山羊群体,以及(3)萨能山羊群体。
αs1酪蛋白基因型对产奶量、脂肪含量和蛋白质含量有显著影响。在仅基于雄性已知αs1酪蛋白基因型的遗传和基因组评估中纳入αs1酪蛋白效应提高了准确性(从6%提高到27%)。在基于所有母羊表型的基因组评估中,基因含量方法比其他测试方法表现更好,但准确性的提高仅略优于没有αs1酪蛋白效应的基因组模型(从1%提高到14%)。
在法国奶山羊的基因组评估模型中纳入αs1酪蛋白效应是可行的,并且有助于提高准确性。预测未进行基因分型动物的基因型存在困难,限制了所获得估计育种值准确性的提高。