Université Paris-Saclay, UVSQ, INRAE, BREED, Jouy-en-Josas, France.
Ecole Nationale Vétérinaire d'Alfort, BREED, Maisons-Alfort, France.
PLoS One. 2024 Feb 23;19(2):e0298623. doi: 10.1371/journal.pone.0298623. eCollection 2024.
Bull fertility is an important economic trait, and the use of subfertile semen for artificial insemination decreases the global efficiency of the breeding sector. Although the analysis of semen functional parameters can help to identify infertile bulls, no tools are currently available to enable precise predictions and prevent the commercialization of subfertile semen. Because male fertility is a multifactorial phenotype that is dependent on genetic, epigenetic, physiological and environmental factors, we hypothesized that an integrative analysis might help to refine our knowledge and understanding of bull fertility. We combined -omics data (genotypes, sperm DNA methylation at CpGs and sperm small non-coding RNAs) and semen parameters measured on a large cohort of 98 Montbéliarde bulls with contrasting fertility levels. Multiple Factor Analysis was conducted to study the links between the datasets and fertility. Four methodologies were then considered to identify the features linked to bull fertility variation: Logistic Lasso, Random Forest, Gradient Boosting and Neural Networks. Finally, the features selected by these methods were annotated in terms of genes, to conduct functional enrichment analyses. The less relevant features in -omics data were filtered out, and MFA was run on the remaining 12,006 features, including the 11 semen parameters and a balanced proportion of each type of-omics data. The results showed that unlike the semen parameters studied the-omics datasets were related to fertility. Biomarkers related to bull fertility were selected using the four methodologies mentioned above. The most contributory CpGs, SNPs and miRNAs targeted genes were all found to be involved in development. Interestingly, fragments derived from ribosomal RNAs were overrepresented among the selected features, suggesting roles in male fertility. These markers could be used in the future to identify subfertile bulls in order to increase the global efficiency of the breeding sector.
公牛的繁殖力是一个重要的经济性状，使用生育力低下的精液进行人工授精会降低全球畜牧业的效率。尽管对精液功能参数的分析有助于识别不育公牛，但目前尚无工具可用于准确预测和防止生育力低下的精液商业化。由于雄性生育力是一种依赖于遗传、表观遗传、生理和环境因素的多因素表型，我们假设综合分析可能有助于深化我们对公牛生育力的认识和理解。我们将 -omics 数据（基因型、CpG 位点的精子 DNA 甲基化和精子小非编码 RNA）与来自 98 头具有不同生育力水平的蒙贝利亚尔公牛的大型队列的精液参数相结合进行分析。多元因子分析用于研究数据集与生育力之间的联系。然后考虑了四种方法来识别与公牛生育力变化相关的特征：逻辑套索、随机森林、梯度提升和神经网络。最后，这些方法选择的特征根据基因进行注释，以进行功能富集分析。-omics 数据中不太相关的特征被过滤掉，MFA 应用于剩余的 12006 个特征，包括 11 个精液参数和每种类型的 -omics 数据的平衡比例。结果表明，与研究的精液参数不同，-omics 数据集与生育力有关。使用上述四种方法选择与公牛生育力相关的生物标志物。选定的最有贡献的 CpG、SNP 和 miRNA 靶向基因都被发现参与了发育。有趣的是，所选特征中核糖体 RNA 衍生片段的含量过高，表明它们在雄性生育力中发挥作用。这些标记物可用于未来识别生育力低下的公牛，以提高全球畜牧业的效率。
