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通过考虑单倍型片段的品种起源提高混合群体中多品种预测的准确性。

Improving the Accuracy of Multi-Breed Prediction in Admixed Populations by Accounting for the Breed Origin of Haplotype Segments.

作者信息

Schmid Markus, Stock Joana, Bennewitz Jörn, Wellmann Robin

机构信息

Institute of Animal Science, Department of Animal Genetics and Breeding, University of Hohenheim, Stuttgart, Germany.

出版信息

Front Genet. 2022 Mar 24;13:840815. doi: 10.3389/fgene.2022.840815. eCollection 2022.

Abstract

Numerically small breeds have often been upgraded with mainstream breeds. This historic introgression predisposes the breeds for joint genomic evaluations with mainstream breeds. The linkage disequilibrium structure differs between breeds. The marker effects of a haplotype segment may, therefore, depend on the breed from which the haplotype segment originates. An appropriate method for genomic evaluation would account for this dependency. This study proposes a method for the computation of genomic breeding values for small admixed breeds that incorporate phenotypic and genomic information from large introgressed breeds by considering the breed origin of alleles (BOA) in the evaluation. The proposed BOA model classifies haplotype segments according to their origins and assumes different but correlated SNP effects for the different origins. The BOA model was compared in a simulation study to conventional within-breed genomic best linear unbiased prediction (GBLUP) and conventional multi-breed GBLUP models. The BOA model outperformed within-breed GBLUP as well as multi-breed GBLUP in most cases.

摘要

数量较少的品种常常与主流品种进行杂交改良。这种历史上的基因渗入使得这些品种易于与主流品种进行联合基因组评估。不同品种之间的连锁不平衡结构存在差异。因此,单倍型片段的标记效应可能取决于该单倍型片段所源自的品种。一种合适的基因组评估方法应考虑到这种依赖性。本研究提出了一种计算小型杂交品种基因组育种值的方法,该方法通过在评估中考虑等位基因的品种来源(BOA),将大型渗入品种的表型和基因组信息纳入其中。所提出的BOA模型根据单倍型片段的来源对其进行分类,并对不同来源假设不同但相关的SNP效应。在一项模拟研究中,将BOA模型与传统的品种内基因组最佳线性无偏预测(GBLUP)和传统的多品种GBLUP模型进行了比较。在大多数情况下,BOA模型的表现优于品种内GBLUP以及多品种GBLUP。

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Optimizing genomic prediction for Australian Red dairy cattle.优化澳大利亚红奶牛的基因组预测。
J Dairy Sci. 2020 Jul;103(7):6276-6298. doi: 10.3168/jds.2019-17914. Epub 2020 Apr 22.

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