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SNP-based mate allocation strategies to maximize total genetic value in pigs

机译:基于SNP的伴侣分配策略,以最大限度地提高猪的总遗传价值

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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.
机译:用于非添加性遗传效应的伴侣分配策略可用于最大化未来后代的整体遗传优点。在基因组上下文中占遗传评估中的主导效应的核算,而不是在基于古典的基础上的环境中,因为基因座的等位基因组合是已知的。我们研究的目标是两倍。首先,在140天内以100kg(年龄),背部饮料深度(BD)的年龄估计优势差异组分,并且在垃圾(APWL)中出生时的平均仔猪重量。其次,探讨了伴随着占主导地位和近亲繁殖抑郁症来最大限度地提高未来后代整体遗传优势的伙伴分配战略的效率。利用基因组模型估计遗传方差组分,其包括近贫化抑郁症,无血压抑郁症(占优势遗传效应(优势)。包括优势效应的模型与基因组添加剂模型更好地符合数据。优势差的估计值,表达为增殖遗传方差的百分比,分别为20,11和12%,分别为年龄,BD和APWL。从遗传方差组分估计中检索添加剂和占优势单核苷酸多态性作用的估计,并用于预测总遗传和育种值的聚合物的结果。最大化总遗传值,而不是在聚合物中的繁殖值,其平均优势分别为50.79天,0.79天,0.04mm和11.3g,同龄,BD和APWL,但略微降低了预期的添加剂遗传增益,例如预期的添加剂遗传增益。年龄的1.8%。非加性遗传效应的基因组伴侣分配核算是一种可行和潜在的策略,以改善后代的性能而不显着损害添加剂遗传增益。

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