GenPhySE, INRA, Université de Toulouse, 31326, Castanet-Tolosan, France.
IFIP Institut du Porc, BP35104, 35651, Le Rheu, France.
Genet Sel Evol. 2019 Sep 27;51(1):55. doi: 10.1186/s12711-019-0498-y.
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.
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.
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.
考虑非加性遗传效应的配种策略可用于最大限度地提高未来后代的整体遗传优势。在基因组背景下,与经典的基于系谱的背景相比,更容易在遗传评估中考虑显性效应,因为已知基因座上等位基因的组合。我们研究的目的有两个。首先,估计了 100 公斤龄(AGE)、140 天背膘厚(BD)和窝产平均初生重(APWL)的显性方差分量。其次,探索了考虑显性和近交衰退来最大化未来后代整体遗传优势的配种策略的效率。
使用包含有和没有非加性遗传效应(显性)的近交衰退的基因组模型估计遗传方差分量。包含显性效应的模型并不比基因组加性模型更适合数据。以加性遗传方差的百分比表示的显性方差估计值分别为 AGE、BD 和 APWL 的 20%、11%和 12%。从遗传方差分量估计中提取了加性和显性单核苷酸多态性效应的估计值,并用于根据总遗传和育种值预测交配的结果。在交配中最大化总遗传值而不是育种值,使后代平均优势分别为 AGE、BD 和 APWL 的-0.79 天、-0.04 毫米和 11.3 克,但略微降低了预期的加性遗传增益,例如 AGE 的增益降低了 1.8%。
考虑非加性遗传效应的基因组配种策略是一种可行且有潜力的策略,可以在不显著降低加性遗传增益的情况下提高后代的性能。