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邀请评论:单步基因组 BLUP 中的未知亲本群体和元发现者。

Invited review: Unknown-parent groups and metafounders in single-step genomic BLUP.

机构信息

Department of Animal and Dairy Science, University of Georgia, Athens 30602.

Animal Genomics and Improvement Laboratory, Agricultural Research Service, US Department of Agriculture, Beltsville, MD 20705.

出版信息

J Dairy Sci. 2022 Feb;105(2):923-939. doi: 10.3168/jds.2021-20293. Epub 2021 Nov 17.

Abstract

Single-step genomic BLUP (ssGBLUP) is a method for genomic prediction that integrates matrices of pedigree (A) and genomic (G) relationships into a single unified additive relationship matrix whose inverse is incorporated into a set of mixed model equations (MME) to compute genomic predictions. Pedigree information in dairy cattle is often incomplete. Missing pedigree potentially causes biases and inflation in genomic estimated breeding values (GEBV) obtained with ssGBLUP. Three major issues are associated with missing pedigree in ssGBLUP, namely biased predictions by selection, missing inbreeding in pedigree relationships, and incompatibility between G and A in level and scale. These issues can be solved using a proper model for unknown-parent groups (UPG). The theory behind the use of UPG is well established for pedigree BLUP, but not for ssGBLUP. This study reviews the development of the UPG model in pedigree BLUP, the properties of UPG models in ssGBLUP, and the effect of UPG on genetic trends and genomic predictions. Similarities and differences between UPG and metafounder (MF) models, a generalized UPG model, are also reviewed. A UPG model (QP) derived using a transformation of the MME has a good convergence behavior. However, with insufficient data, the QP model may yield biased genetic trends and may underestimate UPG. The QP model can be altered by removing the genomic relationships linking GEBV and UPG effects from MME. This altered QP model exhibits less bias in genetic trends and less inflation in genomic predictions than the QP model, especially with large data sets. Recently, a new model, which encapsulates the UPG equations into the pedigree relationships for genotyped animals, was proposed in simulated purebred populations. The MF model is a comprehensive solution to the missing pedigree issue. This model can be a choice for multibreed or crossbred evaluations if the data set allows the estimation of a reasonable relationship matrix for MF. Missing pedigree influences genetic trends, but its effect on the predictability of genetic merit for genotyped animals should be negligible when many proven bulls are genotyped. The SNP effects can be back-solved using GEBV from older genotyped animals, and these predicted SNP effects can be used to calculate GEBV for young-genotyped animals with missing parents.

摘要

单步基因组最佳线性无偏预测(ssGBLUP)是一种基因组预测方法,它将系谱(A)和基因组(G)关系矩阵整合到一个单一的统一加性关系矩阵中,该矩阵的逆矩阵被纳入一组混合模型方程(MME)中,以计算基因组预测值。奶牛的系谱信息通常是不完整的。缺失的系谱可能会导致使用 ssGBLUP 获得的基因组估计育种值(GEBV)产生偏差和膨胀。与缺失的系谱相关的三个主要问题是:选择引起的预测偏差、系谱关系中缺失的近交以及水平和规模上 G 和 A 的不兼容性。可以使用未知亲本群体(UPG)的适当模型来解决这些问题。UPG 在系谱 BLUP 中的使用理论已经得到很好的建立,但在 ssGBLUP 中没有。本研究回顾了 UPG 模型在系谱 BLUP 中的发展、UPG 模型在 ssGBLUP 中的性质以及 UPG 对遗传趋势和基因组预测的影响。还回顾了 UPG 和元创始人(MF)模型之间的相似性和差异,MF 模型是一种广义的 UPG 模型。使用 MME 的变换得到的 UPG 模型具有良好的收敛行为。然而,在数据不足的情况下,QP 模型可能会产生遗传趋势的偏差,并可能低估 UPG。可以通过从 MME 中删除链接 GEBV 和 UPG 效应的基因组关系来改变 QP 模型。与 QP 模型相比,这种改变的 QP 模型在遗传趋势中表现出较小的偏差,在基因组预测中表现出较小的膨胀,尤其是在数据集较大的情况下。最近,在模拟的纯种群体中提出了一种新的模型,该模型将 UPG 方程封装到已分型动物的系谱关系中。MF 模型是解决缺失系谱问题的综合解决方案。如果数据集允许为 MF 估计合理的关系矩阵,则该模型可以作为多品种或杂交品种评估的选择。缺失的系谱会影响遗传趋势,但当许多经过验证的公牛被基因分型时,它对基因分型动物遗传优势的可预测性的影响应该可以忽略不计。可以使用来自较老基因分型动物的 GEBV 回溯求解 SNP 效应,并且可以使用这些预测的 SNP 效应来计算具有缺失亲本的年轻基因分型动物的 GEBV。

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