J Anim Sci. 2017 Dec;95(12):5197-5207. doi: 10.2527/jas2017.1705.
In pig breeding, the final product is a crossbred (CB) animal, while selection is performed at the purebred (PB) level using mainly PB data. However, incorporating CB data in genetic evaluations is expected to result in greater genetic progress at the CB level. Currently, there is no optimal way to include CB genotypes into the genomic relationship matrix. This is because, in single-step genomic BLUP, which is the most commonly used method, genomic and pedigree relationships must refer to the same base. This may not be the case when several breeds and CB are included. An alternative to overcome this issue may be to use a genomic relationship matrix (G matrix) that accounts for both linkage disequilibrium (LD) and linkage analysis (LA), called G. The objectives of this study were to further develop the G matrix approach to utilize both PB and CB genotypes simultaneously, to investigate its performance, and the general added value of including CB genotypes in genomic evaluations. Data were available on Dutch Landrace, Large White, and the F1 cross of those breeds. In total, 7 different G matrix compositions (PB alone, PB together, each PB with the CB, all genotypes across breeds, and G) were tested on 3 maternal traits: total number born (TNB), live born (LB), and gestation length (GL). Results show that G gave the greatest prediction accuracy of all the relationship matrices tested for PB prediction, but not for CB prediction. Including CB genotypes in general increased prediction accuracy for all breeds. However, in some cases, these increases in prediction accuracy were not significant (at < 0.05). To conclude, CB genotypes increased prediction accuracy for some of the traits and breeds, but not for all. The G matrix had significantly greater prediction accuracy in PB than the other G matrix with both PB and CB genotypes, except in one case. While for CB, the G matrix with genotypes across all breeds gave the greatest accuracy, though this was not significantly different from G. Computation time was high for G, and research will be needed to reduce its computational costs to make it feasible for use in routine evaluations. The main conclusion is that inclusion of CB genotypes is beneficial for both PB and CB animals.
在猪的育种中，最终产物是杂交（CB）动物，而选择是在纯种（PB）水平上进行的，主要使用 PB 数据。然而，在遗传评估中纳入 CB 数据有望在 CB 水平上取得更大的遗传进展。目前，还没有将 CB 基因型纳入基因组关系矩阵的最佳方法。这是因为，在单步基因组 BLUP 中，这是最常用的方法，基因组和系谱关系必须参考相同的基础。当包含几个品种和 CB 时，情况可能并非如此。克服这个问题的一种替代方法可能是使用同时考虑连锁不平衡（LD）和连锁分析（LA）的基因组关系矩阵（G 矩阵），称为 G 矩阵。本研究的目的是进一步开发 G 矩阵方法，以同时利用 PB 和 CB 基因型，研究其性能以及在基因组评估中纳入 CB 基因型的一般附加值。数据可用于荷兰长白猪、大白猪和它们的 F1 杂交品种。总共测试了 7 种不同的 G 矩阵组成（仅 PB、PB 一起、每个 PB 与 CB、所有品种的基因型和 G）在 3 个母性性状上：总产仔数（TNB）、活产仔数（LB）和妊娠期（GL）。结果表明，对于 PB 预测，G 矩阵在所有测试的关系矩阵中给出了最大的预测准确性，但对于 CB 预测则不是。一般来说，纳入 CB 基因型会增加所有品种的预测准确性。然而，在某些情况下，这些预测准确性的提高并不显著（<0.05）。总之，CB 基因型增加了一些性状和品种的预测准确性，但并非所有性状和品种。对于 PB，G 矩阵在 PB 基因型和 CB 基因型方面的预测准确性显著高于其他 G 矩阵，除了一种情况。对于 CB，在所有品种的基因型的 G 矩阵给出了最大的准确性，尽管这与 G 矩阵没有显著差异。G 矩阵的计算时间很高，需要进行研究以降低其计算成本，使其在常规评估中可行。主要结论是，纳入 CB 基因型对 PB 和 CB 动物都有益。
