Colombi Daniele, Bonifazi Renzo, Sbarra Fiorella, Quaglia Andrea, Calus Mario P L, Lasagna Emiliano
Dipartimento di Scienze Agrarie, Alimentari e Ambientali, University of Perugia, Perugia, Italy.
Animal Breeding and Genomics, Wageningen University and Research, Wageningen, the Netherlands.
J Anim Breed Genet. 2025 Jul 15. doi: 10.1111/jbg.70004.
Marchigiana, Chianina, and Romagnola are three Italian autochthonous beef cattle breeds that have been historically selected for meat production. Recent advancements suggest that the use of genomic data and multi-breed (MB) models to combine information from different breeds may help to increase the accuracies of genomic predictions, in particular if the available data per breed is limited. This study aimed to evaluate and compare the accuracies of genomic predictions for average daily gain (ADG) in the three Italian breeds. We implemented different scenarios using phenotypes collected on 5303 young bulls in performance tests across the three breeds, 23,793 pedigree records, and 4593 genotypes, and then validated through the linear regression method. The implemented scenarios were: pedigree Best Linear Unbiased Prediction (pBLUP) and single-step Genomic BLUP (ssGBLUP) single-trait single-breed evaluations where each breed was modelled separately; pBLUP and ssGBLUP single-trait multi-breed evaluations where ADG was modelled as the same trait for all breeds, and ssGBLUP multi-trait multi-breed evaluations where ADG was considered as a different correlated trait across breeds. In addition, single- and multi-breed pBLUP and ssGBLUP evaluations were implemented including weight at 1 year of age and muscularity as correlated traits of ADG in a multi-trait approach. Results highlighted the improved accuracies (an average of 5% in ssGBLUP models compared to corresponding pBLUP ones) when incorporating genomic data in the prediction models. Moreover, single-trait multi-breed scenarios resulted in higher accuracy for breeds with lower heritabilities for ADG (an average of 4% for single-trait multi-breed models compared to single-breed ones), confirming the importance of leveraging data from populations with higher heritabilities. Lastly, adding two correlated traits next to ADG in the single- and multi-breed ssGBLUP yielded even higher accuracies than the scenarios only encompassing ADG. The observed increases in accuracy when leveraging data from more populations and/or more traits could be helpful when implementing genomic predictions for innovative traits with limited records per individual or low heritabilities, and for the genetic improvement of local populations where limited data availability represents a challenge for traditional genetic selection.
马尔凯纳牛、契安尼娜牛和罗马尼奥拉牛是意大利的三个本土肉牛品种，历史上一直被选作肉用牛进行培育。最近的研究进展表明，利用基因组数据和多品种（MB）模型来整合不同品种的信息，可能有助于提高基因组预测的准确性，特别是在每个品种可用数据有限的情况下。本研究旨在评估和比较这三个意大利品种平均日增重（ADG）的基因组预测准确性。我们利用在三个品种的性能测试中收集到的5303头青年公牛的表型数据、23793条系谱记录和4593个基因型，实施了不同的方案，然后通过线性回归方法进行验证。实施的方案包括：系谱最佳线性无偏预测（pBLUP）和单步基因组最佳线性无偏预测（ssGBLUP）单性状单品种评估，即分别对每个品种进行建模；pBLUP和ssGBLUP单性状多品种评估，即将ADG作为所有品种的同一性状进行建模；以及ssGBLUP多性状多品种评估，即将ADG视为不同品种间的不同相关性状进行建模。此外，还实施了单品种和多品种的pBLUP和ssGBLUP评估，在多性状方法中将1岁体重和肌肉发达程度作为ADG的相关性状纳入其中。结果表明，在预测模型中纳入基因组数据时，准确性有所提高（与相应的pBLUP模型相比，ssGBLUP模型平均提高了5%）。此外，单性状多品种方案对于ADG遗传力较低的品种，准确性更高（与单品种模型相比，单性状多品种模型平均提高了4%），这证实了利用高遗传力群体数据的重要性。最后，在单品种和多品种的ssGBLUP中，除了ADG之外再增加两个相关性状，比仅包含ADG的方案具有更高的准确性。当针对个体记录有限或遗传力较低的创新性状进行基因组预测，以及针对数据可用性有限对传统遗传选择构成挑战的地方品种进行遗传改良时，利用更多群体和/或更多性状的数据所观察到的准确性提高可能会有所帮助。
