Department of Animal Science, Universidade Federal de Viçosa, Viçosa, Brazil.
Topigs Norsvin, Curitiba, Brazil.
J Anim Breed Genet. 2021 Jul;138(4):442-453. doi: 10.1111/jbg.12528. Epub 2020 Dec 7.
Biological information regarding markers and gene association may be used to attribute different weights for single nucleotide polymorphism (SNP) in genome-wide selection. Therefore, we aimed to evaluate the predictive ability and the bias of genomic prediction using models that allow SNP weighting in the genomic relationship matrix (G) building, with and without incorporating biological information to obtain the weights. Firstly, we performed a genome-wide association studies (GWAS) in data set containing single- (SL) or a multi-line (ML) pig population for androstenone, skatole and indole levels. Secondly, 1%, 2%, 5%, 10%, 30% and 50% of the markers explaining the highest proportions of the genetic variance for each trait were selected to build gene networks through the association weight matrix (AWM) approach. The number of edges in the network was computed and used to derive weights for G (AWM-WssGBLUP). The single-step GBLUP (ssGBLUP) and weighted ssGBLUP (WssGBLUP) were used as standard scenarios. All scenarios presented predictive abilities different from zero; however, the great overlap in their confidences interval suggests no differences among scenarios. Most of scenarios of based on AWM provide overestimations for skatole in both SL and ML populations. On the other hand, the skatole and indole prediction were no biased in the ssGBLUP (S1) in both SL and ML populations. Most of scenarios based on AWM provide no biased predictions for indole in both SL and ML populations. In summary, using biological information through AWM matrix and gene networks to derive weights for genomic prediction resulted in no increase in predictive ability for boar taint compounds. In addition, this approach increased the number of analyses steps. Thus, we can conclude that ssGBLUP is most appropriate for the analysis of boar taint compounds in comparison with the weighted strategies used in the present work.
生物信息学关于标记物和基因关联的信息可用于为全基因组选择中的单核苷酸多态性 (SNP) 分配不同的权重。因此，我们旨在评估使用允许在基因组关系矩阵 (G) 构建中为 SNP 加权的模型进行基因组预测的预测能力和偏差，包括是否结合生物信息来获得权重。首先，我们在包含单系（SL）或多系（ML）猪群体的数据集上进行了全基因组关联研究（GWAS），以研究雄烯酮、粪臭素和吲哚水平。其次，选择解释每个性状遗传方差最高比例的 1%、2%、5%、10%、30%和 50%的标记，通过关联权重矩阵（AWM）方法构建基因网络。计算网络中的边数，并将其用于推导 G 的权重（AWM-WssGBLUP）。单步 GBLUP（ssGBLUP）和加权 ssGBLUP（WssGBLUP）被用作标准场景。所有场景的预测能力均与零不同；然而，置信区间的高度重叠表明场景之间没有差异。大多数基于 AWM 的场景都高估了 SL 和 ML 群体中的粪臭素。另一方面，在 SL 和 ML 群体中的 ssGBLUP（S1）中，粪臭素和吲哚的预测没有偏差。大多数基于 AWM 的场景在 SL 和 ML 群体中对吲哚的预测均无偏差。总之，通过 AWM 矩阵和基因网络使用生物信息来推导基因组预测的权重并没有提高猪体味化合物的预测能力。此外，这种方法增加了分析步骤的数量。因此，我们可以得出结论，与本研究中使用的加权策略相比，ssGBLUP 最适合分析猪体味化合物。
