Norwegian University of Life Sciences, Faculty of Biosciences, Ås, Norway.
Norsvin SA, Hamar, Norway.
J Anim Breed Genet. 2022 Nov;139(6):654-665. doi: 10.1111/jbg.12729. Epub 2022 Jun 27.
The aim of this study was to compare three methods of genomic prediction: GBLUP, BayesC and BayesGC for genomic prediction of six maternal traits in Landrace sows using a panel of 660 K SNPs. The effects of different priors for the Bayesian methods were also investigated. GBLUP does not take the genetic architecture into account as all SNPs are assumed to have equally sized effects and relies heavily on the relationships between the animals for accurate predictions. Bayesian approaches rely on both fitting SNPs that describe relationships between animals in addition to fitting single SNP effects directly. Both the relationship between the animals and single SNP effects are important for accurate predictions. Maternal traits in sows are often more difficult to record and have lower heritabilities. BayesGC was generally the method with the higher accuracy, although its accuracy was for some traits matched by that of GBLUP and for others by that of BayesC. For piglet mortality within 3 weeks, BayesGC achieved up to 9.2% higher accuracy. For many of the traits, however, the methods did not show significant differences in accuracies.
GBLUP、BayesC 和 BayesGC，使用 660K SNPs 面板对长白母猪的六个母本性状进行基因组预测。还研究了贝叶斯方法不同先验的影响。GBLUP 没有考虑遗传结构，因为所有 SNP 都假定具有相同大小的效应，并且严重依赖于动物之间的关系以进行准确预测。贝叶斯方法不仅依赖于拟合描述动物之间关系的 SNP，还直接拟合单个 SNP 效应。动物之间的关系和单个 SNP 效应对于准确预测都很重要。母猪的母本性状通常更难记录，遗传力也更低。BayesGC 通常是准确性更高的方法，尽管对于某些性状，其准确性与 GBLUP 相当，而对于其他性状，则与 BayesC 相当。对于 3 周内的仔猪死亡率，BayesGC 可实现高达 9.2%的更高准确性。然而，对于许多性状，这些方法的准确性并没有显著差异。
