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Dominance and epistatic genetic variances for litter size in pigs using genomic models

机译:利用基因组模型对猪产仔数的优势和上位遗传变异

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Epistatic genomic relationship matrices for interactions of any-order can be constructed using the Hadamard products of orthogonal additive and dominance genomic relationship matrices and standardization based on the trace of the resulting matrices. Variance components for litter size in pigs were estimated by Bayesian methods for five nested models with additive, dominance, and pairwise epistatic effects in a pig dataset, and including genomic inbreeding as a covariate. Estimates of additive and non-additive (dominance and epistatic) variance components were obtained for litter size. The variance component estimates were empirically orthogonal, i.e. they did not change when fitting increasingly complex models. Most of the genetic variance was captured by non-epistatic effects, as expected. In the full model, estimates of dominance and total epistatic variances (additive-by-additive plus additive-by-dominance plus dominance-by-dominance), expressed as a proportion of the total phenotypic variance, were equal to 0.02 and 0.04, respectively. The estimate of broad-sense heritability for litter size (0.15) was almost twice that of the narrow-sense heritability (0.09). Ignoring inbreeding depression yielded upward biased estimates of dominance variance, while estimates of epistatic variances were only slightly affected. Epistatic variance components can be easily computed using genomic relationship matrices. Correct orthogonal definition of the relationship matrices resulted in orthogonal partition of genetic variance into additive, dominance, and epistatic components, but obtaining accurate variance component estimates remains an issue. Genomic models that include non-additive effects must also consider inbreeding depression in order to avoid upward bias of estimates of dominance variance. Inclusion of epistasis did not improve the accuracy of prediction of breeding values.
机译:可以使用正交加性和优势基因组关系矩阵的Hadamard乘积,并基于所得矩阵的踪迹进行标准化,来构建用于任意顺序相互作用的上位基因组关系矩阵。通过贝叶斯方法,对猪数据集中具有加性,优势和成对上位效应的五个嵌套模型,通过贝叶斯方法估算了猪产仔数的方差分量,其中包括基因组近交作为协变量。对加料和不加料(显性和上位性)方差分量的估计值用于垫料大小。方差分量估计值在经验上是正交的,即,当拟合日益复杂的模型时,它们不会发生变化。如预期的那样,大多数遗传变异是通过非止痛作用捕获的。在完整模型中,占总表型方差的比例表示的优势度和总上位方差(由加性,加性,加性和主导性)的估计分别等于0.02和0.04。 。产仔数的广义遗传力(0.15)估计值几乎是狭义遗传力(0.09)的两倍。忽略近亲抑郁会产生对优势方差的向上估计,而对上位方差的估计仅受到轻微影响。上位方差分量可以使用基因组关系矩阵轻松计算。正确的关系矩阵正交定义导致将遗传方差正交划分为加性,支配性和上位性成分,但是获得准确的方差成分估计仍然是一个问题。包括非累加效应的基因组模型还必须考虑近交衰退,以避免优势变异估计的向上偏差。包含上位性并不能提高繁殖值预测的准确性。

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