The single-step genomic best linear unbiased prediction (SSGBLUP) method is a popular approach for genetic evaluation with high-density genotype data. To solve the problem that pedigree and genomic relationship matrices refer to different base populations, a single-step genomic method with metafounders (MF-SSGBLUP) was put forward. The aim of this study was to compare the predictive ability and bias of genomic evaluations obtained with MF-SSGBLUP and standard SSGBLUP. We examined feed conversion ratio (FCR) and average daily gain (ADG) in DanBred Landrace (LL) and Yorkshire (YY) pigs using both univariate and bivariate models, as well as the optimal weighting factors (ω), which represent the proportions of the genetic variance not captured by markers, for ADG and FCR in SSGBLUP and MF-SSGBLUP. In general, SSGBLUP and MF-SSGBLUP showed similar predictive abilities and bias of genomic estimated breeding values (GEBV). In the LL population, the predictive ability for ADG reached 0.36 using uni- or bi-variate SSGBLUP or MF-SSGBLUP, while the predictive ability for FCR was highest (0.20) for the bivariate model using MF-SSGBLUP, but differences between analyses were very small. In the YY population, predictive ability for ADG was similar for the four analyses (up to 0.35), while the predictive ability for FCR was highest (0.36) for the uni- and bi-variate MF-SSGBLUP analyses. SSGBLUP and MF-SSGBLUP exhibited nearly the same bias. In general, the bivariate models had lower bias than the univariate models. In the LL population, the optimal ω for ADG was ~ 0.2 in the univariate or bivariate models using SSGBLUP or MF-SSGBLUP, and the optimal ω for FCR was 0.70 and 0.55 for SSGBLUP and MF-SSGBLUP, respectively. In the YY population, the optimal ω ranged from 0.25 to 0. 35 for ADG across the four analyses and from 0.10 to 0.30 for FCR. Our results indicate that MF-SSGBLUP performed slightly better than SSGBLUP for genomic evaluation. There was little difference in the optimal weighting factors (ω) between SSGBLUP and MF-SSGBLUP. Overall, the bivariate model using MF-SSGBLUP is recommended for single-step genomic evaluation of ADG and FCR in DanBred Landrace and Yorkshire pigs.

中文翻译：

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使用元创始人对丹麦长白猪和约克夏猪的饲料转化率和平均日增重进行单步基因组评估

单步基因组最佳线性无偏预测 (SSGBLUP) 方法是一种使用高密度基因型数据进行遗传评估的流行方法。为了解决谱系和基因组关系矩阵涉及不同基群的问题，提出了一种带有元创始人的单步基因组方法（MF-SSGBLUP）。本研究的目的是比较使用 MF-SSGBLUP 和标准 SSGBLUP 获得的基因组评估的预测能力和偏差。我们使用单变量和双变量模型检查了丹育长白 (LL) 和约克夏 (YY) 猪的饲料转化率 (FCR) 和平均日增重 (ADG)，以及代表标记未捕获的遗传变异，用于 SSGBLUP 和 MF-SSGBLUP 中的 ADG 和 FCR。一般来说，SSGBLUP 和 MF-SSGBLUP 显示出相似的预测能力和基因组估计育种值 (GEBV) 的偏差。在 LL 人群中，使用单变量或双变量 SSGBLUP 或 MF-SSGBLUP 对 ADG 的预测能力达到 0.36，而使用 MF-SSGBLUP 的双变量模型对 FCR 的预测能力最高（0.20），但分析之间的差异是很小。在 YY 人群中，四项分析的 ADG 预测能力相似（高达 0.35），而单变量和双变量 MF-SSGBLUP 分析的 FCR 预测能力最高（0.36）。SSGBLUP 和 MF-SSGBLUP 表现出几乎相同的偏差。一般来说，双变量模型比单变量模型具有更低的偏差。在 LL 群体中，在使用 SSGBLUP 或 MF-SSGBLUP 的单变量或双变量模型中，ADG 的最佳 ω 为 ~ 0.2，对于 SSGBLUP 和 MF-SSGBLUP，FCR 的最佳 ω 分别为 0.70 和 0.55。在 YY 群体中，四个分析中 ADG 的最佳 ω 范围为 0.25 至 0. 35，FCR 的最佳 ω 范围为 0.10 至 0.30。我们的结果表明 MF-SSGBLUP 在基因组评估方面的表现略好于 SSGBLUP。SSGBLUP 和 MF-SSGBLUP 之间的最佳加权因子 (ω) 几乎没有差异。总体而言，建议使用 MF-SSGBLUP 双变量模型对丹育长白猪和约克夏猪的 ADG 和 FCR 进行单步基因组评估。SSGBLUP 和 MF-SSGBLUP 之间的最佳加权因子 (ω) 几乎没有差异。总体而言，建议使用 MF-SSGBLUP 双变量模型对丹育长白猪和约克夏猪的 ADG 和 FCR 进行单步基因组评估。SSGBLUP 和 MF-SSGBLUP 之间的最佳加权因子 (ω) 几乎没有差异。总体而言，建议使用 MF-SSGBLUP 双变量模型对丹育长白猪和约克夏猪的 ADG 和 FCR 进行单步基因组评估。