Fangmann A, Sharifi R A, Heinkel J, Danowski K, Schrade H, Erbe M, Simianer H
J Anim Sci. 2017 Apr;95(4):1434-1443. doi: 10.2527/jas.2016.0991.
Currently used multi-step methods to incorporate genomic information in the prediction of breeding values (BV) implicitly involve many assumptions which, if violated, may result in loss of information, inaccuracies and bias. To overcome this, single-step genomic best linear unbiased prediction (ssGBLUP) was proposed combining pedigree, phenotype and genotype of all individuals for genetic evaluation. Our objective was to implement ssGBLUP for genomic predictions in pigs and to compare the accuracy of ssGBLUP with that of multi-step methods with empirical data of moderately sized pig breeding populations. Different predictions were performed: conventional parent average (PA), direct genomic value (DGV) calculated with genomic BLUP (GBLUP), a GEBV obtained by blending the DGV with PA, and ssGBLUP. Data comprised individuals from a German Landrace (LR) and Large White (LW) population. The trait 'number of piglets born alive' (NBA) was available for 182,054 litters of 41,090 LR sows and 15,750 litters from 4534 LW sows. The pedigree contained 174,021 animals, of which 147,461 (26,560) animals were LR (LW) animals. In total, 526 LR and 455 LW animals were genotyped with the Illumina PorcineSNP60 BeadChip. After quality control and imputation, 495 LR (424 LW) animals with 44,368 (43,678) SNP on 18 autosomes remained for the analysis. Predictive abilities, i.e., correlations between de-regressed proofs and genomic BV, were calculated with a five-fold cross validation and with a forward prediction for young genotyped validation animals born after 2011. Generally, predictive abilities for LR were rather small (0.08 for GBLUP, 0.19 for GEBV and 0.18 for ssGBLUP). For LW, ssGBLUP had the greatest predictive ability (0.45). For both breeds, assessment of reliabilities for young genotyped animals indicated that genomic prediction outperforms PA with ssGBLUP providing greater reliabilities (0.40 for LR and 0.32 for LW) than GEBV (0.35 for LR and 0.29 for LW). Grouping of animals according to information sources revealed that genomic prediction had the highest potential benefit for genotyped animals without their own phenotype. Although, ssGBLUP did not generally outperform GBLUP or GEBV, the results suggest that ssGBLUP can be a useful and conceptually convincing approach for practical genomic prediction of NBA in moderately sized LR and LW populations.
目前用于在育种值（BV）预测中纳入基因组信息的多步方法隐含地涉及许多假设，若这些假设不成立，可能会导致信息丢失、不准确和偏差。为克服这一问题，提出了单步基因组最佳线性无偏预测（ssGBLUP），它结合了所有个体的系谱、表型和基因型进行遗传评估。我们的目标是在猪的基因组预测中实施ssGBLUP，并将ssGBLUP的准确性与多步方法的准确性进行比较，使用中等规模猪育种群体的经验数据。进行了不同的预测：传统的亲本均值（PA）、用基因组最佳线性无偏预测（GBLUP）计算的直接基因组值（DGV）、通过将DGV与PA混合获得的基因组估计育种值（GEBV）以及ssGBLUP。数据包括来自德国长白猪（LR）和大白猪（LW）群体的个体。“活产仔猪数”（NBA）这一性状可用于41,090头LR母猪的182,054窝以及4534头LW母猪的15,750窝。系谱包含174,021只动物，其中147,461（26,560）只为LR（LW）动物。总共对526头LR和455头LW动物使用Illumina PorcineSNP60 BeadChip进行了基因分型。经过质量控制和填充后，18条常染色体上具有44,368（43,678）个单核苷酸多态性（SNP）的495头LR（424头LW）动物被保留用于分析。预测能力，即去回归证明与基因组BV之间的相关性，通过五重交叉验证以及对2011年后出生的年轻基因分型验证动物的向前预测来计算。一般来说，LR的预测能力相当小（GBLUP为0.08，GEBV为0.19，ssGBLUP为0.18）。对于LW，ssGBLUP具有最大的预测能力（0.45）。对于两个品种，对年轻基因分型动物的可靠性评估表明，基因组预测优于PA，ssGBLUP提供的可靠性（LR为0.40，LW为0.32）高于GEBV（LR为0.35，LW为0.29）。根据信息来源对动物进行分组表明，基因组预测对没有自身表型的基因分型动物具有最高的潜在益处。虽然，ssGBLUP一般并不优于GBLUP或GEBV，但结果表明，ssGBLUP对于中等规模的LR和LW群体中NBA的实际基因组预测可能是一种有用且在概念上令人信服的方法。
