In this study, we assessed the accuracy of genomic prediction for carcass weight (CWT), marbling score (MS), eye muscle area (EMA) and back fat thickness (BFT) in Hanwoo cattle when using genomic best linear unbiased prediction (GBLUP), weighted GBLUP (wGBLUP), and a BayesR model. For these models, we investigated the potential gain from using pre-selected single nucleotide polymorphisms (SNPs) from a genome-wide association study (GWAS) on imputed sequence data and from gene expression information. We used data on 13,717 animals with carcass phenotypes and imputed sequence genotypes that were split in an independent GWAS discovery set of varying size and a remaining set for validation of prediction. Expression data were used from a Hanwoo gene expression experiment based on 45 animals. Using a larger number of animals in the reference set increased the accuracy of genomic prediction whereas a larger independent GWAS discovery dataset improved identification of predictive SNPs. Using pre-selected SNPs from GWAS in GBLUP improved accuracy of prediction by 0.02 for EMA and up to 0.05 for BFT, CWT, and MS, compared to a 50 k standard SNP array that gave accuracies of 0.50, 0.47, 0.58, and 0.47, respectively. Accuracy of prediction of BFT and CWT increased when BayesR was applied with the 50 k SNP array (0.02 and 0.03, respectively) and was further improved by combining the 50 k array with the top-SNPs (0.06 and 0.04, respectively). By contrast, using BayesR resulted in limited improvement for EMA and MS. wGBLUP did not improve accuracy but increased prediction bias. Based on the RNA-seq experiment, we identified informative expression quantitative trait loci, which, when used in GBLUP, improved the accuracy of prediction slightly, i.e. between 0.01 and 0.02. SNPs that were located in genes, the expression of which was associated with differences in trait phenotype, did not contribute to a higher prediction accuracy. Our results show that, in Hanwoo beef cattle, when SNPs are pre-selected from GWAS on imputed sequence data, the accuracy of prediction improves only slightly whereas the contribution of SNPs that are selected based on gene expression is not significant. The benefit of statistical models to prioritize selected SNPs for estimating genomic breeding values is trait-specific and depends on the genetic architecture of each trait.

中文翻译：

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利用基因表达和全基因组序列信息提高汉宇牛car体性状的基因组预测准确性

在这项研究中，我们使用基因组最佳线性无偏预测（GBLUP）评估了Hanwoo牛的car体体重（CWT），大理石花纹得分（MS），眼肌面积（EMA）和背脂肪厚度（BFT）的基因组预测准确性。 ，加权GBLUP（wGBLUP）和BayesR模型。对于这些模型，我们调查了来自全基因组关联研究（GWAS）的预选单核苷酸多态性（SNP）对估算序列数据和基因表达信息的潜在收益。我们使用了13,717具cas体表型和估算序列基因型动物的数据，这些数据被分成大小不同的独立GWAS发现集，而其余集用于预测预测。从基于45只动物的Hanwoo基因表达实验中获得表达数据。在参考集中使用更多的动物，可以提高基因组预测的准确性，而更大的独立GWAS发现数据集可以提高对预测SNP的识别。与50k标准SNP阵列的精确度分别为0.50、0.47、0.58和0.47相比，在GBLUP中使用GWAS中预先选择的SNP，EMA的预测准确度提高了0.02，BFT，CWT和MS的预测准确度提高了0.05。分别。当将BayesR与50 k SNP阵列（分别为0.02和0.03）一起应用时，BFT和CWT的预测准确性提高，并且通过将50 k阵列与顶部SNP（分别为0.06和0.04）组合而进一步提高。相比之下，使用BayesR对EMA和MS的改进有限。wGBLUP不能提高准确性，但是会增加预测偏差。根据RNA-seq实验，我们确定了信息表达定量性状位点，该位点在GBLUP中使用时略微提高了预测的准确性，即在0.01和0.02之间。位于基因中的SNPs，其表达与性状表型的差异有关，并没有有助于更高的预测准确性。我们的结果表明，在Hanwoo肉牛中，当从估算的序列数据中从GWAS中预先选择SNP时，预测的准确性仅略有提高，而基于基因表达选择的SNP的贡献并不显着。统计模型对选定的SNP进行优先级排序以估计基因组育种值的好处是特定于性状的，并且取决于每个性状的遗传结构。在0.01和0.02之间。位于基因中的SNPs，其表达与性状表型的差异有关，并没有有助于更高的预测准确性。我们的结果表明，在Hanwoo肉牛中，当从估算的序列数据中从GWAS中预先选择SNP时，预测的准确性仅略有提高，而基于基因表达选择的SNP的贡献并不显着。统计模型对选定的SNP进行优先级排序以估计基因组育种值的好处是特定于性状的，并且取决于每个性状的遗传结构。在0.01和0.02之间。位于基因中的SNPs，其表达与性状表型的差异有关，并没有有助于更高的预测准确性。我们的结果表明，在Hanwoo肉牛中，当从估算的序列数据中从GWAS中预先选择SNP时，预测的准确性仅略有提高，而基于基因表达选择的SNP的贡献并不显着。统计模型对选定的SNP进行优先级排序以估计基因组育种值的好处是特定于性状的，并且取决于每个性状的遗传结构。当从估算的序列数据中从GWAS中预选SNP时，预测的准确性仅略有提高，而基于基因表达选择的SNP的贡献并不显着。统计模型对选定的SNP进行优先级排序以估计基因组育种值的好处是特定于性状的，并且取决于每个性状的遗传结构。当从估算的序列数据中从GWAS中预选SNP时，预测的准确性仅略有提高，而基于基因表达选择的SNP的贡献并不显着。统计模型对选定的SNP进行优先级排序以估计基因组育种值的好处是特定于性状的，并且取决于每个性状的遗传结构。