In this study, we tested whether genotyping both live and dead animals (GSD) realises more genetic gain for post-weaning survival (PWS) in pigs compared to genotyping only live animals (GOS). Stochastic simulation was used to estimate the rate of genetic gain realised by GSD and GOS at a 0.01 rate of pedigree-based inbreeding in three breeding schemes, which differed in PWS (95%, 90% and 50%) and litter size (6 and 10). Pedigree-based selection was conducted as a point of reference. Variance components were estimated and then estimated breeding values (EBV) were obtained in each breeding scheme using a linear or a threshold model. Selection was for a single trait, i.e. PWS with a heritability of 0.02 on the observed scale. The trait was simulated on the underlying scale and was recorded as binary (0/1). Selection candidates were genotyped and phenotyped before selection, with only live candidates eligible for selection. Genotyping strategies differed in the proportion of live and dead animals genotyped, but the phenotypes of all animals were used for predicting EBV of the selection candidates. Based on a 0.01 rate of pedigree-based inbreeding, GSD realised 14 to 33% more genetic gain than GOS for all breeding schemes depending on PWS and litter size. GSD increased the prediction accuracy of EBV for PWS by at least 14% compared to GOS. The use of a linear versus a threshold model did not have an impact on genetic gain for PWS regardless of the genotyping strategy and the bias of the EBV did not differ significantly among genotyping strategies. Genotyping both dead and live animals was more informative than genotyping only live animals to predict the EBV for PWS of selection candidates, but with marginal increases in genetic gain when the proportion of dead animals genotyped was 60% or greater. Therefore, it would be worthwhile to use genomic information on both live and more than 20% dead animals to compute EBV for the genetic improvement of PWS under the assumption that dead animals reflect increased liability on the underlying scale.

中文翻译：

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对活体和死体动物进行基因分型，以提高育种计划中猪的断奶后存活率


在这项研究中，我们测试了与仅对活体动物进行基因分型 (GOS) 相比，对活体动物和死体动物进行基因分型 (GSD) 是否能实现猪断奶后存活率 (PWS) 更多的遗传增益。随机模拟用于估计 GSD 和 GOS 在基于系谱的近交率为 0.01 的三种育种方案中实现的遗传增益率，这三种育种方案的 PWS（95%、90% 和 50%）和窝产仔数（6 和 6 岁）有所不同。 10）。以谱系为基础的选择作为参考。估计方差分量，然后使用线性或阈值模型在每个育种方案中获得估计育种值（EBV）。选择针对单一性状，即在观察范围内遗传力为0.02的PWS。该性状在基础尺度上进行模拟，并记录为二进制 (0/1)。选择候选人在选择之前进行了基因分型和表型分析，只有活着的候选人才有资格选择。基因分型策略在活体动物和死体动物基因分型的比例上有所不同，但所有动物的表型都用于预测选择候选者的 EBV。基于 0.01 的基于谱系的近亲繁殖率，GSD 在所有育种计划中实现了比 GOS 高 14% 至 33% 的遗传增益，具体取决于 PWS 和窝产仔数。与 GOS 相比，GSD 将 PWS 的 EBV 预测精度提高了至少 14%。无论基因分型策略如何，线性模型与阈值模型的使用都不会对 PWS 的遗传增益产生影响，并且 EBV 的偏差在基因分型策略之间没有显着差异。 对死亡动物和活体动物进行基因分型比仅对活体动物进行基因分型可提供更多信息，以预测选择候选者 PWS 的 EBV，但当基因分型的死亡动物比例为 60% 或更高时，遗传增益会略有增加。因此，假设死亡动物反映了潜在规模上责任的增加，那么使用活体动物和超过 20% 死亡动物的基因组信息来计算 PWS 遗传改良的 EBV 是值得的。