Sharing individual phenotype and genotype data between countries is complex and fraught with potential errors, while sharing summary statistics of genome-wide association studies (GWAS) is relatively straightforward, and thus would be especially useful for traits that are expensive or difficult-to-measure, such as feed efficiency. Here we examined: (1) the sharing of individual cow data from international partners; and (2) the use of sequence variants selected from GWAS of international cow data to evaluate the accuracy of genomic estimated breeding values (GEBV) for residual feed intake (RFI) in Australian cows. GEBV for RFI were estimated using genomic best linear unbiased prediction (GBLUP) with 50k or high-density single nucleotide polymorphisms (SNPs), from a training population of 3797 individuals in univariate to trivariate analyses where the three traits were RFI phenotypes calculated using 584 Australian lactating cows (AUSc), 824 growing heifers (AUSh), and 2526 international lactating cows (OVE). Accuracies of GEBV in AUSc were evaluated by either cohort-by-birth-year or fourfold random cross-validations. GEBV of AUSc were also predicted using only the AUS training population with a weighted genomic relationship matrix constructed with SNPs from the 50k array and sequence variants selected from a meta-GWAS that included only international datasets. The genomic heritabilities estimated using the AUSc, OVE and AUSh datasets were moderate, ranging from 0.20 to 0.36. The genetic correlations (rg) of traits between heifers and cows ranged from 0.30 to 0.95 but were associated with large standard errors. The mean accuracies of GEBV in Australian cows were up to 0.32 and almost doubled when either overseas cows, or both overseas cows and AUS heifers were included in the training population. They also increased when selected sequence variants were combined with 50k SNPs, but with a smaller relative increase. The accuracy of RFI GEBV increased when international data were used or when selected sequence variants were combined with 50k SNP array data. This suggests that if direct sharing of data is not feasible, a meta-analysis of summary GWAS statistics could provide selected SNPs for custom panels to use in genomic selection programs. However, since this finding is based on a small cross-validation study, confirmation through a larger study is recommended.

中文翻译：

---

共享表型或遗传变异可以提高饲料效率基因组预测的准确性


在国家之间共享个体表型和基因型数据非常复杂，并且充满潜在错误，而共享全基因组关联研究（GWAS）的汇总统计数据则相对简单，因此对于昂贵或难以测量的性状特别有用，例如饲料效率。在这里，我们研究了：（1）来自国际合作伙伴的个体奶牛数据的共享； (2) 使用从国际奶牛数据 GWAS 中选择的序列变异来评估澳大利亚奶牛剩余采食量 (RFI) 的基因组估计育种值 (GEBV) 的准确性。 RFI 的 GEBV 是使用具有 50k 或高密度单核苷酸多态性 (SNP) 的基因组最佳线性无偏预测 (GBLUP) 来估计的，该预测来自单变量到三变量分析中的 3797 名个体训练群体，其中三个性状是使用 584 个澳大利亚人计算的 RFI 表型泌乳牛 (AUSc)、824 头生长小母牛 (AUSh) 和 2526 头国际泌乳牛 (OVE)。 AUSc 中 GEBV 的准确性通过出生年份队列或四重随机交叉验证进行评估。 AUSc 的 GEBV 也仅使用 AUS 训练群体进行预测，该矩阵具有加权基因组关系矩阵，该矩阵由来自 50k 阵列的 SNP 和从仅包含国际数据集的元 GWAS 中选择的序列变体构建。使用 AUSc、OVE 和 AUSh 数据集估计的基因组遗传力中等，范围为 0.20 至 0.36。小母牛和奶牛之间性状的遗传相关性 (rg) 范围为 0.30 至 0.95，但与较大的标准误差相关。澳大利亚奶牛 GEBV 的平均准确度高达 0。32，当训练群体中包括海外奶牛或海外奶牛和澳大利亚小母牛时，几乎翻了一番。当选定的序列变体与 50k SNP 组合时，它们也会增加，但相对增加较小。当使用国际数据或将选定的序列变体与 50k SNP 阵列数据相结合时，RFI GEBV 的准确性会提高。这表明，如果直接共享数据不可行，则对 GWAS 统计汇总进行荟萃分析可以为定制面板提供选定的 SNP，以用于基因组选择程序。然而，由于这一发现是基于小型交叉验证研究，因此建议通过更大规模的研究进行确认。