Genomic best linear-unbiased prediction (GBLUP) assumes equal variance for all marker effects, which is suitable for traits that conform to the infinitesimal model. For traits controlled by major genes, Bayesian methods with shrinkage priors or genome-wide association study (GWAS) methods can be used to identify causal variants effectively. The information from Bayesian/GWAS methods can be used to construct the weighted genomic relationship matrix (G). However, it remains unclear which methods perform best for traits varying in genetic architecture. Therefore, we developed several methods to optimize the performance of weighted GBLUP and compare them with other available methods using simulated and real data sets. First, two types of methods (marker effects with local shrinkage or normal prior) were used to obtain test statistics and estimates for each marker effect. Second, three weighted G matrices were constructed based on the marker information from the first step: (1) the genomic-feature-weighted G, (2) the estimated marker-variance-weighted G, and (3) the absolute value of the estimated marker-effect-weighted G. Following the above process, six different weighted GBLUP methods (local shrinkage/normal-prior GF/EV/AEWGBLUP) were proposed for genomic prediction. Analyses with both simulated and real data demonstrated that these options offer flexibility for optimizing the weighted GBLUP for traits with a broad spectrum of genetic architectures. The advantage of weighting methods over GBLUP in terms of accuracy was trait dependant, ranging from 14.8% to marginal for simulated traits and from 44% to marginal for real traits. Local-shrinkage prior EVWGBLUP is superior for traits mainly controlled by loci of a large effect. Normal-prior AEWGBLUP performs well for traits mainly controlled by loci of moderate effect. For traits controlled by some loci with large effects (explain 25-50% genetic variance) and a range of loci with small effects, GFWGBLUP has advantages. In conclusion, the optimal weighted GBLUP method for genomic selection should take both the genetic architecture and number of QTLs of traits into consideration carefully.

中文翻译：

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适用于数量性状遗传结构的基因组最佳线性无偏预测 (BLUP) 的有效加权方法

基因组最佳线性无偏预测 (GBLUP) 假设所有标记效应的方差相等，这适用于符合无穷小模型的性状。对于主要基因控制的性状，可以使用具有收缩先验的贝叶斯方法或全基因组关联研究（GWAS）方法来有效地识别因果变异。来自贝叶斯/GWAS 方法的信息可用于构建加权基因组关系矩阵 (G)。然而，尚不清楚哪种方法对遗传结构中不同的性状表现最好。因此，我们开发了几种方法来优化加权 GBLUP 的性能，并将它们与使用模拟和真实数据集的其他可用方法进行比较。第一的，使用两种类型的方法（局部收缩的标记效应或正常先验）来获得每个标记效应的测试统计数据和估计值。其次，基于第一步的标记信息构建三个加权 G 矩阵：（1）基因组特征加权 G，（2）估计的标记方差加权 G，和（3）绝对值估计标记效应加权 G。按照上述过程，提出了六种不同的加权 GBLUP 方法（局部收缩/正常先验 GF/EV/AEWGBLUP）用于基因组预测。对模拟数据和真实数据的分析表明，这些选项为优化具有广泛遗传结构的性状的加权 GBLUP 提供了灵活性。在准确性方面，加权方法优于 GBLUP 的优势取决于特征，范围为 14。模拟性状为 8% 至边缘，真实性状为 44% 至边缘。局部收缩先验 EVWGBLUP 对于主要由大效应位点控制的性状具有优势。Normal-prior AEWGBLUP 对主要受中等效应位点控制的性状表现良好。对于一些影响较大的基因座（解释25-50%的遗传变异）和一系列影响较小的基因座控制的性状，GFWGBLUP具有优势。总之，用于基因组选择的最佳加权 GBLUP 方法应仔细考虑遗传结构和性状 QTL 的数量。对于一些影响较大的基因座（解释25-50%的遗传变异）和一系列影响较小的基因座控制的性状，GFWGBLUP具有优势。总之，用于基因组选择的最佳加权 GBLUP 方法应仔细考虑遗传结构和性状 QTL 的数量。对于一些影响较大的基因座（解释25-50%的遗传变异）和一系列影响较小的基因座控制的性状，GFWGBLUP具有优势。总之，用于基因组选择的最佳加权 GBLUP 方法应仔细考虑遗传结构和性状 QTL 的数量。