Ben Zaabza Hafedh, Ferdosi Mohammad H, Strandén Ismo, C D Cuyabano Beatriz, Neupane Mahesh, Misztal Ignacy, Lourenco Daniela, Gondro Cedric
Department of Animal Science, Michigan State University, 474 S Shaw Ln, East Lansing, MI 48824, USA.
Animal Genetics and Breeding Unit (AGBU), a joint venture between the NSW Department of Primary Industries and Regional Development and the University of New England, Armidale, NSW 2351, Australia.
J Anim Sci. 2025 Aug 27. doi: 10.1093/jas/skaf292.
Genomic selection has been used in animal breeding for c. 15 years and continues to be an important tool in predicting genetic merit in livestock populations. The dairy cattle industry was the first to adopt genomic selection, initially based on some 50K SNP arrays for thousands of animals. Later advances in genome-scanning technologies have enabled inexpensive genotyping and sequencing, leading to wider adoption, and constantly increasing amounts of genomic data, both as to the number of genotyped animals and variants genotyped per animal. Full sequence data are expected to supersede SNP chips in the coming years. We review the methods and computational approaches used with sequence data and the impact of the methods and model assumptions on genomic prediction accuracy. The modeling, development, and applicability of these methods to sequence data are discussed as well as the computational resources required. Sequence data should in principle provide full information of genetic variability, which should lead to higher prediction accuracy. In practice there is limited evidence of additional benefit from using sequence data over medium or high-density SNP panels. This is particularly true for small effective population sizes (Ne) such as cattle populations, where animals within a breed have many common ancestors and thus longer chromosome segments with high linkage disequilibrium (LD) accurately trackable with a relatively small number of markers. A population with a small Ne has long haplotype blocks, from 1 to 5 Mb, making it hard to identify casual variants within blocks. However, in major cattle breeds a medium-density SNP panel is sufficient to tag the blocks themselves, and prediction with large datasets is highly accurate. Clearly, sequence data should not be used directly for genomic prediction, but for identifying putative causal variants to improve the accuracy and stability of subsequent predictions. We show that the best strategy to deal with any large data with high SNP densities is to use only a subset of (important) markers and determine the most appropriate model for exploiting the preselected variants in the genomic evaluation. Novel prediction methods that subset trait-specific informative markers could offer the advantage of using sequence data by potentially linking individuals through underlying functional variants rather than simply through shared haplotype blocks inherited from ancestors. Further research is required to clarify this aspect.
基因组选择已在动物育种中应用约15年，并且仍然是预测家畜群体遗传价值的重要工具。奶牛行业率先采用基因组选择，最初是基于约50K SNP芯片对数千头动物进行检测。基因组扫描技术的后续进展使得基因分型和测序成本降低，从而得到更广泛的应用，并且基因组数据量不断增加，无论是基因分型动物的数量还是每头动物基因分型的变异数量。预计在未来几年全序列数据将取代SNP芯片。我们综述了用于序列数据的方法和计算方法，以及这些方法和模型假设对基因组预测准确性的影响。讨论了这些方法对序列数据的建模、开发和适用性以及所需的计算资源。序列数据原则上应提供遗传变异的完整信息，这应能提高预测准确性。但实际上，与使用中密度或高密度SNP面板相比，使用序列数据带来额外益处的证据有限。对于有效群体规模较小（Ne）的群体，如牛群，情况尤其如此，因为同一品种内的动物有许多共同祖先，因此具有高连锁不平衡（LD）的较长染色体片段可以用相对较少的标记准确追踪。有效群体规模较小的群体具有长度为1至5 Mb的长单倍型块，这使得难以识别块内的偶然变异。然而，在主要的牛品种中，中密度SNP面板足以标记这些块本身，并且使用大型数据集进行预测的准确性很高。显然，序列数据不应直接用于基因组预测，而应用于识别假定的因果变异，以提高后续预测的准确性和稳定性。我们表明，处理任何具有高SNP密度的大数据的最佳策略是仅使用（重要）标记的一个子集，并确定在基因组评估中利用预选变异的最合适模型。通过潜在地通过潜在的功能变异而非简单地通过从祖先遗传的共享单倍型块将个体联系起来，子集性状特异性信息标记的新型预测方法可能具有使用序列数据的优势。需要进一步研究来阐明这一方面。
