Accuracy of Whole-Genome Prediction Using a Genetic Architecture-Enhanced Variance-Covariance Matrix
Accuracy of Whole-Genome Prediction Using a Genetic Architecture-Enhanced Variance-Covariance Matrix
复制标题
使用遗传架构增强方差-协方差矩阵进行全基因组预测的准确性
DOI：
10.1534/g3.114.016261
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发表时间：
2015-02
影响因子：
2.6
通讯作者：
Li Jiaqi
中科院分区：
文献类型：
--
作者：
Zhang Zhe;Erbe Malena;He Jinlong;Ober Ulrike;Gao Ning;Zhang Hao;Simianer Henner;Li Jiaqi
通过对基因型和表型数据的综合分析，全基因组预测（WGP）可以准确预测动物、植物和人类群体中未观察到的复杂性状的遗传或表型值。由于感兴趣特征的潜在遗传结构是影响模型选择的重要因素，我们提出了一种新的策略，称为BLUP|GA (BLUP-given genetic architecture)，该策略可以使用手头数据集中的遗传结构信息，而不是来自公共资源。这是通过使用特定性状的协方差矩阵(T)来实现的，它是遗传结构部分（S矩阵）和实现的关系矩阵(G)的加权和。给出了BLUP|遗传算法（BLUP给定遗传结构），并用实际数据和模拟数据进行了说明。利用奶牛数据集的3个模型性状和3个具有不同遗传结构的公共数据集的11个性状验证了BLUP|遗传算法的预测能力，并与GBLUP和其他方法进行了比较。结果表明，在奶牛数据集的21个场景中，BLUP|GA在20个场景中优于GBLUP，在分析的公共数据集的13个特征中，有12个特征优于GBLUP、BayesA和BayesB。进一步分析表明，BLUP|GA和GBLUP的精度差异与T和G矩阵之间的距离显著相关。在BLUP|遗传算法中应用的新策略是标准GBLUP模型的一个有利和灵活的替代方案，允许在必要时考虑所考虑的数量性状的遗传结构。这一特征主要是由于性状特异性关系矩阵（T矩阵）和遗传关系矩阵在未观察到的因果位点之间的相似性增加。在WGP中应用BLUP|遗传算法可以减轻模型选择的负担。
Obtaining accurate predictions of unobserved genetic or phenotypic values for complex traits in animal, plant, and human populations is possible through whole-genome prediction (WGP), a combined analysis of genotypic and phenotypic data. Because the underlying genetic architecture of the trait of interest is an important factor affecting model selection, we propose a new strategy, termed BLUP|GA (BLUP-given genetic architecture), which can use genetic architecture information within the dataset at hand rather than from public sources. This is achieved by using a trait-specific covariance matrix (T), which is a weighted sum of a genetic architecture part (S matrix) and the realized relationship matrix (G). The algorithm of BLUP|GA (BLUP-given genetic architecture) is provided and illustrated with real and simulated datasets. Predictive ability of BLUP|GA was validated with three model traits in a dairy cattle dataset and 11 traits in three public datasets with a variety of genetic architectures and compared with GBLUP and other approaches. Results show that BLUP|GA outperformed GBLUP in 20 of 21 scenarios in the dairy cattle dataset and outperformed GBLUP, BayesA, and BayesB in 12 of 13 traits in the analyzed public datasets. Further analyses showed that the difference of accuracies for BLUP|GA and GBLUP significantly correlate with the distance between the T and G matrices. The new strategy applied in BLUP|GA is a favorable and flexible alternative to the standard GBLUP model, allowing to account for the genetic architecture of the quantitative trait under consideration when necessary. This feature is mainly due to the increased similarity between the trait-specific relationship matrix (T matrix) and the genetic relationship matrix at unobserved causal loci. Applying BLUP|GA in WGP would ease the burden of model selection.
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影响因子：
3.3
作者：
T. Meuwissen;B. Hayes;M. Goddard
通讯作者：
T. Meuwissen;B. Hayes;M. Goddard
DOI：
10.1186/1297-9686-43-19
发表时间：
2011-05-17
期刊：
Genetics, selection, evolution : GSE
影响因子：
--
作者：
Liu Z;Seefried FR;Reinhardt F;Rensing S;Thaller G;Reents R
通讯作者：
Reents R
影响因子：
3.3
作者：
Gianola, Daniel;de los Campos, Gustavo;Fernando, Rohan
通讯作者：
Fernando, Rohan
影响因子：
3.5
作者：
Aguilar, I.;Misztal, I.;Lawlor, T. J.
通讯作者：
Lawlor, T. J.
影响因子：
3.5
作者：
Misztal, I.;Legarra, A.;Aguilar, I.
通讯作者：
Aguilar, I.