State Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-sen University, North Third Road, Guangzhou Higher Education Mega Center, Guangzhou, Guangdong 510006, P. R. China.
Guangdong Provincial Key Laboratory of Waterfowl Healthy Breeding, College of Animal Science & Technology, Zhongkai University of Agriculture and Engineering, Guangzhou, Guangdong, 510225, P. R. China.
Animal. 2019 Sep;13(9):1804-1810. doi: 10.1017/S1751731118003567. Epub 2019 Jan 8.
Single-step genomic BLUP (ssGBLUP) has been widely used in genomic evaluation due to relatively higher prediction accuracy and simplicity of use. The prediction accuracy from ssGBLUP depends on the amount of information available concerning both genotype and phenotype. This study investigated how information on genotype and phenotype that had been acquired from previous generations influences the prediction accuracy of ssGBLUP, and thus we sought an optimal balance about genotypic and phenotypic information to achieve a cost-effective and computationally efficient genomic evaluation. We generated two genetically correlated traits (h2 = 0.35 for trait A, h2 = 0.10 for trait B and genetic correlation 0.20) as well as two distinct populations mimicking purebred swine. Phenotypic and genotypic information in different numbers of previous generations and different genotyping rates for each litter were set to generate different datasets. Prediction accuracy was evaluated by correlating genomic estimated breeding values with true breeding values for genotyped animals in the last generation. The results revealed a negligible impact of previous generations that lacked genotyped animals on the prediction accuracy. Phenotypic and genotypic data, including the most recent three to four generations with a genotyping rate of 40% or 50% for each litter, could lead to asymptotic maximum prediction accuracy for genotyped animals in the last generation. Single-step genomic best linear unbiased prediction yielded an optimal balance about genotypic and phenotypic information to ensure a cost-effective and computationally efficient genomic evaluation of populations of polytocous animals such as purebred pigs.
单步基因组最佳线性无偏预测（ssGBLUP）由于具有较高的预测准确性和使用简便性，已被广泛应用于基因组评估。ssGBLUP 的预测准确性取决于基因型和表型相关信息的数量。本研究探讨了从先前世代获得的基因型和表型信息如何影响 ssGBLUP 的预测准确性，从而寻求最佳的基因型和表型信息平衡，以实现具有成本效益和计算效率的基因组评估。我们生成了两个遗传相关性状（性状 A 的遗传力为 0.35，性状 B 的遗传力为 0.10，遗传相关系数为 0.20）以及两个模拟纯种猪的不同群体。在不同数量的前几代中设置了表型和基因型信息以及每个窝的不同基因型率，以生成不同的数据集。通过将基因组估计育种值与最后一代已鉴定动物的真实育种值相关联来评估预测准确性。结果表明，缺乏已鉴定动物的前几代对预测准确性的影响可以忽略不计。表型和基因型数据，包括最近的三到四代，每窝的基因型率为 40%或 50%，可以导致最后一代已鉴定动物的预测准确性达到渐近最大值。单步基因组最佳线性无偏预测在基因型和表型信息之间达到了最佳平衡，以确保多胎动物（如纯种猪）群体的具有成本效益和计算效率的基因组评估。
