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利用系谱关系或单步基因组评估法估计大西洋鲑(Salmo salar)生长均匀度的育种值。

Estimation of breeding values for uniformity of growth in Atlantic salmon (Salmo salar) using pedigree relationships or single-step genomic evaluation.

作者信息

Sae-Lim Panya, Kause Antti, Lillehammer Marie, Mulder Han A

机构信息

Nofima Ås, Osloveien 1, P.O. Box 210, 1431, Ås, Norway.

Biometrical Genetics, Natural Resources Institute Finland, 31600, Jokioinen, Finland.

出版信息

Genet Sel Evol. 2017 Mar 7;49(1):33. doi: 10.1186/s12711-017-0308-3.

Abstract

BACKGROUND

In farmed Atlantic salmon, heritability for uniformity of body weight is low, indicating that the accuracy of estimated breeding values (EBV) may be low. The use of genomic information could be one way to increase accuracy and, hence, obtain greater response to selection. Genomic information can be merged with pedigree information to construct a combined relationship matrix ([Formula: see text] matrix) for a single-step genomic evaluation (ssGBLUP), allowing realized relationships of the genotyped animals to be exploited, in addition to numerator pedigree relationships ([Formula: see text] matrix). We compared the predictive ability of EBV for uniformity of body weight in Atlantic salmon, when implementing either the [Formula: see text] or [Formula: see text] matrix in the genetic evaluation. We used double hierarchical generalized linear models (DHGLM) based either on a sire-dam (sire-dam DHGLM) or an animal model (animal DHGLM) for both body weight and its uniformity.

RESULTS

With the animal DHGLM, the use of [Formula: see text] instead of [Formula: see text] significantly increased the correlation between the predicted EBV and adjusted phenotypes, which is a measure of predictive ability, for both body weight and its uniformity (41.1 to 78.1%). When log-transformed body weights were used to account for a scale effect, the use of [Formula: see text] instead of [Formula: see text] produced a small and non-significant increase (1.3 to 13.9%) in predictive ability. The sire-dam DHGLM had lower predictive ability for uniformity compared to the animal DHGLM.

CONCLUSIONS

Use of the combined numerator and genomic relationship matrix ([Formula: see text]) significantly increased the predictive ability of EBV for uniformity when using the animal DHGLM for untransformed body weight. The increase was only minor when using log-transformed body weights, which may be due to the lower heritability of scaled uniformity, the lower genetic correlation of transformed body weight with its uniformity compared to the untransformed traits, and the small number of genotyped animals in the reference population. This study shows that ssGBLUP increases the accuracy of EBV for uniformity of body weight and is expected to increase response to selection in uniformity.

摘要

背景

在养殖的大西洋鲑中,体重均匀度的遗传力较低,这表明估计育种值(EBV)的准确性可能较低。利用基因组信息可能是提高准确性的一种方法,从而获得更大的选择响应。基因组信息可以与系谱信息合并,构建一个用于单步基因组评估(ssGBLUP)的组合关系矩阵([公式:见原文]矩阵),除了分子系谱关系([公式:见原文]矩阵)之外,还可以利用已分型动物的实际关系。我们比较了在遗传评估中使用[公式:见原文]或[公式:见原文]矩阵时,大西洋鲑体重均匀度的EBV预测能力。我们对体重及其均匀度使用了基于父本 - 母本(父本 - 母本双层次广义线性模型)或动物模型(动物双层次广义线性模型)的双层次广义线性模型(DHGLM)。

结果

使用动物双层次广义线性模型时,对于体重及其均匀度,使用[公式:见原文]而非[公式:见原文]显著提高了预测的EBV与调整后的表型之间的相关性,这是预测能力的一种度量(从41.1%提高到78.1%)。当使用对数变换后的体重来考虑尺度效应时,使用[公式:见原文]而非[公式:见原文]使预测能力有小幅且不显著的提高(从1.3%提高到13.9%)。与动物双层次广义线性模型相比,父本 - 母本双层次广义线性模型对均匀度的预测能力较低。

结论

当对未变换的体重使用动物双层次广义线性模型时,使用分子与基因组组合关系矩阵([公式:见原文])显著提高了EBV对均匀度的预测能力。当使用对数变换后的体重时,提高幅度较小,这可能是由于尺度化均匀度的遗传力较低、变换后的体重与其均匀度的遗传相关性低于未变换性状,以及参考群体中已分型动物数量较少。本研究表明,ssGBLUP提高了EBV对体重均匀度的准确性,并有望提高对均匀度的选择响应。

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