Center for Quantitative Genetics and Genomics, Department of Molecular Biology and Genetics, Aarhus University, Blichers Allé 20, P,O, BOX 50, DK-8830 Tjele, Denmark.
Genet Sel Evol. 2012 Dec 3;44(1):37. doi: 10.1186/1297-9686-44-37.
Single-step methods provide a coherent and conceptually simple approach to incorporate genomic information into genetic evaluations. An issue with single-step methods is compatibility between the marker-based relationship matrix for genotyped animals and the pedigree-based relationship matrix. Therefore, it is necessary to adjust the marker-based relationship matrix to the pedigree-based relationship matrix. Moreover, with data from routine evaluations, this adjustment should in principle be based on both observed marker genotypes and observed phenotypes, but until now this has been overlooked. In this paper, I propose a new method to address this issue by 1) adjusting the pedigree-based relationship matrix to be compatible with the marker-based relationship matrix instead of the reverse and 2) extending the single-step genetic evaluation using a joint likelihood of observed phenotypes and observed marker genotypes. The performance of this method is then evaluated using two simulated datasets.
The method derived here is a single-step method in which the marker-based relationship matrix is constructed assuming all allele frequencies equal to 0.5 and the pedigree-based relationship matrix is constructed using the unusual assumption that animals in the base population are related and inbred with a relationship coefficient γ and an inbreeding coefficient γ / 2. Taken together, this γ parameter and a parameter that scales the marker-based relationship matrix can handle the issue of compatibility between marker-based and pedigree-based relationship matrices. The full log-likelihood function used for parameter inference contains two terms. The first term is the REML-log-likelihood for the phenotypes conditional on the observed marker genotypes, whereas the second term is the log-likelihood for the observed marker genotypes. Analyses of the two simulated datasets with this new method showed that 1) the parameters involved in adjusting marker-based and pedigree-based relationship matrices can depend on both observed phenotypes and observed marker genotypes and 2) a strong association between these two parameters exists. Finally, this method performed at least as well as a method based on adjusting the marker-based relationship matrix.
Using the full log-likelihood and adjusting the pedigree-based relationship matrix to be compatible with the marker-based relationship matrix provides a new and interesting approach to handle the issue of compatibility between the two matrices in single-step genetic evaluation.
单步方法为将基因组信息纳入遗传评估提供了一种连贯且概念简单的方法。单步方法的一个问题是基于标记的关系矩阵与基于系谱的关系矩阵之间的兼容性。因此，有必要调整基于标记的关系矩阵以适应基于系谱的关系矩阵。此外，使用常规评估中的数据，这种调整原则上应基于观察到的标记基因型和观察到的表型，但到目前为止，这一点一直被忽视。在本文中，我提出了一种新的方法来解决这个问题，方法是 1）调整基于系谱的关系矩阵以与基于标记的关系矩阵兼容，而不是相反，2）通过观察到的表型和观察到的标记基因型的联合似然来扩展单步遗传评估。然后使用两个模拟数据集评估该方法的性能。
这里得到的方法是一种单步方法，其中基于标记的关系矩阵是在假设所有等位基因频率均为 0.5 的情况下构建的，而基于系谱的关系矩阵是使用一种不寻常的假设构建的，即基础群体中的动物具有关系并且近亲繁殖，关系系数γ和近亲系数γ/2。总体而言，此γ参数和一个缩放基于标记的关系矩阵的参数可以处理基于标记和基于系谱的关系矩阵之间的兼容性问题。用于参数推断的完整对数似然函数包含两个项。第一项是基于观察到的标记基因型对表型的 REML 对数似然，第二项是观察到的标记基因型的对数似然。使用此新方法对两个模拟数据集的分析表明，1）调整基于标记和基于系谱的关系矩阵的参数可以取决于观察到的表型和观察到的标记基因型，2）这两个参数之间存在很强的关联。最后，该方法的性能至少与基于调整基于标记的关系矩阵的方法一样好。
使用完整的对数似然和调整基于系谱的关系矩阵以适应基于标记的关系矩阵，为处理单步遗传评估中两个矩阵之间的兼容性问题提供了一种新的有趣方法。
