背景:在基因组选择开始时,一些中国公司用不同的单核苷酸多态性(SNP)阵列对猪进行基因分型。然后将获得的基因组数据组合起来,已经制定了几种归责策略。通常,在遗传评估中只考虑加性遗传效应。然而,对某些性状可能很重要的优势效应可以在混合线性模型中拟合为“经典”或“基因型”优势效应。它们对基因组评估的影响很少被研究。因此,这项研究的目的是使用加拿大约克郡猪的数据集(1)比较不同的策略,以组合来自两个SNP阵列(Affymetrix55K和Illumina42K)的数据,并确定最合适的基因组评估策略和(2)评估优势效应(经典和基因型)和近交抑郁效应对平均日增重(ADG)的基因组预测能力的影响,背脂肪厚度(BF),腰肌肉深度(LMD),天至100公斤(AGE100),以及初产期出生的仔猪总数(TNB)。
结果:使用加性基因组模型获得的可靠性表明,用于组合来自两个SNP阵列的数据的策略对基因组评估影响很小。具有经典或基因型优势效应的模型对所有性状均显示出相似的预测能力。对于ADG,BF,LMD,和AGE100,优势效应占总遗传变异的一小部分(2%至11%),而对于TNB来说,优势效应占11%至20%。对于所有特征,当将基因组近交抑郁效应纳入模型时,模型的预测能力显著提高.然而,纳入优势效应不会改变除TNB外的任何性状的预测能力.
结论:我们的研究表明,将来自不同SNP阵列的数据进行基因组评估是可行的,并且所有组合方法都会导致相似的准确性。无论优势效应如何在基因组模型中拟合,对遗传评估没有影响。包括近亲繁殖抑郁症效应的模型优于仅具有加性效应的模型,即使该性状不受显性基因的强烈影响。
BACKGROUND: At the beginning of genomic selection, some Chinese companies genotyped pigs with different single nucleotide polymorphism (SNP) arrays. The obtained genomic data are then combined and to do this, several imputation strategies have been developed. Usually, only additive genetic effects are considered in genetic evaluations. However, dominance effects that may be important for some traits can be fitted in a mixed linear model as either \'classical\' or \'genotypic\' dominance effects. Their influence on genomic evaluation has rarely been studied. Thus, the objectives of this study were to use a dataset from Canadian Yorkshire pigs to (1) compare different strategies to combine data from two SNP arrays (Affymetrix 55K and Illumina 42K) and identify the most appropriate strategy for genomic evaluation and (2) evaluate the impact of dominance effects (classical\' and \'genotypic\') and inbreeding depression effects on genomic predictive abilities for average daily gain (ADG), backfat thickness (BF), loin muscle depth (LMD), days to 100 kg (AGE100), and the total number of piglets born (TNB) at first parity.
RESULTS: The reliabilities obtained with the additive genomic models showed that the strategy used to combine data from two SNP arrays had little impact on genomic evaluations. Models with classical or genotypic dominance effect showed similar predictive abilities for all traits. For ADG, BF, LMD, and AGE100, dominance effects accounted for a small proportion (2 to 11%) of the total genetic variance, whereas for TNB, dominance effects accounted for 11 to 20%. For all traits, the predictive abilities of the models increased significantly when genomic inbreeding depression effects were included in the model. However, the inclusion of dominance effects did not change the predictive ability for any trait except for TNB.
CONCLUSIONS: Our study shows that it is feasible to combine data from different SNP arrays for genomic evaluation, and that all combination methods result in similar accuracies. Regardless of how dominance effects are fitted in the genomic model, there is no impact on genetic evaluation. Models including inbreeding depression effects outperform a model with only additive effects, even if the trait is not strongly affected by dominant genes.