1 Animal Breeding and Genomics Centre, Wageningen UR Livestock Research, PO Box 65, 8200 AB Lelystad, The Netherlands.
Animal. 2013 Nov;7(11):1759-68. doi: 10.1017/S175173111300150X. Epub 2013 Aug 6.
The genomic breeding value accuracy of scarcely recorded traits is low because of the limited number of phenotypic observations. One solution to increase the breeding value accuracy is to use predictor traits. This study investigated the impact of recording additional phenotypic observations for predictor traits on reference and evaluated animals on the genomic breeding value accuracy for a scarcely recorded trait. The scarcely recorded trait was dry matter intake (DMI, n = 869) and the predictor traits were fat-protein-corrected milk (FPCM, n = 1520) and live weight (LW, n = 1309). All phenotyped animals were genotyped and originated from research farms in Ireland, the United Kingdom and the Netherlands. Multi-trait REML was used to simultaneously estimate variance components and breeding values for DMI using available predictors. In addition, analyses using only pedigree relationships were performed. Breeding value accuracy was assessed through cross-validation (CV) and prediction error variance (PEV). CV groups (n = 7) were defined by splitting animals across genetic lines and management groups within country. With no additional traits recorded for the evaluated animals, both CV- and PEV-based accuracies for DMI were substantially higher for genomic than for pedigree analyses (CV: max. 0.26 for pedigree and 0.33 for genomic analyses; PEV: max. 0.45 and 0.52, respectively). With additional traits available, the differences between pedigree and genomic accuracies diminished. With additional recording for FPCM, pedigree accuracies increased from 0.26 to 0.47 for CV and from 0.45 to 0.48 for PEV. Genomic accuracies increased from 0.33 to 0.50 for CV and from 0.52 to 0.53 for PEV. With additional recording for LW instead of FPCM, pedigree accuracies increased to 0.54 for CV and to 0.61 for PEV. Genomic accuracies increased to 0.57 for CV and to 0.60 for PEV. With both FPCM and LW available for evaluated animals, accuracy was highest (0.62 for CV and 0.61 for PEV in pedigree, and 0.63 for CV and 0.61 for PEV in genomic analyses). Recording predictor traits for only the reference population did not increase DMI breeding value accuracy. Recording predictor traits for both reference and evaluated animals significantly increased DMI breeding value accuracy and removed the bias observed when only reference animals had records. The benefit of using genomic instead of pedigree relationships was reduced when more predictor traits were used. Using predictor traits may be an inexpensive way to significantly increase the accuracy and remove the bias of (genomic) breeding values of scarcely recorded traits such as feed intake.
由于表型观察数量有限，记录数量较少的性状的基因组育种值准确性较低。增加育种值准确性的一种解决方案是使用预测性状。本研究调查了为预测性状记录额外表型观察值对参考和评估动物在记录数量较少的性状的基因组育种值准确性上的影响。记录数量较少的性状是干物质采食量（DMI，n = 869），预测性状是脂肪-蛋白校正乳（FPCM，n = 1520）和活重（LW，n = 1309）。所有表型动物均进行了基因分型，来自爱尔兰、英国和荷兰的研究农场。使用可用的预测因子，通过多性状 REML 同时估计 DMI 的方差分量和育种值。此外，还进行了仅使用系谱关系的分析。通过交叉验证（CV）和预测误差方差（PEV）评估育种值准确性。通过在遗传系谱和国家内管理组内划分动物来定义 CV 组（n = 7）。对于评估动物没有记录额外性状，基于 CV 和 PEV 的 DMI 育种值准确性都大大高于基于系谱的分析（CV：最大 0.26 用于系谱，0.33 用于基因组分析；PEV：最大 0.45 和 0.52，分别）。有额外的性状可用时，系谱和基因组准确性之间的差异减小。对于 FPCM 的额外记录，基于 CV 的系谱准确性从 0.26 增加到 0.47，基于 PEV 的系谱准确性从 0.45 增加到 0.48。基于 CV 的基因组准确性从 0.33 增加到 0.50，基于 PEV 的基因组准确性从 0.52 增加到 0.53。对于 LW 的额外记录而不是 FPCM，基于 CV 的系谱准确性增加到 0.54，基于 PEV 的系谱准确性增加到 0.61。基于 CV 的基因组准确性增加到 0.57，基于 PEV 的基因组准确性增加到 0.60。对于评估动物，FPCM 和 LW 都可用，准确性最高（CV 为 0.62，PEV 为 0.61，基于系谱；CV 为 0.63，PEV 为 0.61，基于基因组分析）。只为参考群体记录预测性状并不能提高 DMI 育种值准确性。为参考和评估动物同时记录预测性状显著提高了 DMI 育种值准确性，并消除了仅参考动物记录时观察到的偏差。当使用更多的预测性状时，使用基因组而不是系谱关系的好处减少。使用预测性状可能是一种经济有效的方法，可以显著提高记录数量较少的性状（如采食量）的准确性并消除（基因组）育种值的偏差。
