BACKGROUND Mate allocation strategies that account for non-additive genetic effects can be used to maximize the overall genetic merit of future offspring. Accounting for dominance effects in genetic evaluations is easier in a genomic context, than in a classical pedigree-based context because the combinations of alleles at loci are known. The objective of our study was two-fold. First, dominance variance components were estimated for age at 100 kg (AGE), backfat depth (BD) at 140 days, and for average piglet weight at birth within litter (APWL). Second, the efficiency of mate allocation strategies that account for dominance and inbreeding depression to maximize the overall genetic merit of future offspring was explored. RESULTS Genetic variance components were estimated using genomic models that included inbreeding depression with and without non-additive genetic effects (dominance). Models that included dominance effects did not fit the data better than the genomic additive model. Estimates of dominance variances, expressed as a percentage of additive genetic variance, were 20, 11, and 12% for AGE, BD, and APWL, respectively. Estimates of additive and dominance single nucleotide polymorphism effects were retrieved from the genetic variance component estimates and used to predict the outcome of matings in terms of total genetic and breeding values. Maximizing total genetic values instead of breeding values in matings gave the progeny an average advantage of - 0.79 days, - 0.04 mm, and 11.3 g for AGE, BD and APWL, respectively, but slightly reduced the expected additive genetic gain, e.g. by 1.8% for AGE. CONCLUSIONS Genomic mate allocation accounting for non-additive genetic effects is a feasible and potential strategy to improve the performance of the offspring without dramatically compromising additive genetic gain.

中文翻译：

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基于SNP的配偶分配策略可最大程度地提高猪的总遗传价值。

背景技术考虑非累加遗传效应的伴侣分配策略可用于最大化未来后代的总体遗传价值。与基于谱系的经典背景相比，在基因组背景下考虑基因评估中的优势效应要容易得多，因为已知基因座处的等位基因组合。我们研究的目的是双重的。首先，估计了年龄在100公斤（AGE），后脂肪深度（BD）在140天和产仔时平均仔猪体重（APWL）的优势变异成分。其次，研究了占优势和近亲抑郁的配偶分配策略以最大化未来后代的整体遗传优势的效率。结果遗传方差成分是使用基因组模型估算的，该模型包括具有和不具有非累加遗传效应（显性）的近交抑郁症。包含优势效应的模型并不比基因组加性模型更好地拟合数据。年龄，BD和APWL的优势差异估计值以累加遗传差异百分比表示，分别为20％，11％和12％。从遗传方差分量估计中检索出加性和优势单核苷酸多态性影响的估计，并根据总遗传和育种值来预测交配的结果。最大化总遗传值而不是交配中的育种值，后代的平均优势分别为AGE，BD和APWL：-0.79天，-0.04 mm和11.3 g，但稍微降低了预期的附加遗传增益，例如AGE降低了1.8％。结论解决非累加遗传效应的基因组配偶分配是一种可行且潜在的策略，可在不显着损害累加遗传增益的情况下提高后代的性能。