ArticleAnimal reproduction2026
Application of omics technologies to identify reproductive phenotypes in the context of assisted reproductive technologies for cattle.
Article in Animal reproduction, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Fertility is a key determinant of reproductive efficiency, economic performance, and sustainability in cattle production systems. Traditional fertility phenotypes that are still widely used today, such as conception rate, calving interval, non-return rate, and semen quality parameters, primarily describe reproductive outcomes rather than predict fertility. Consequently, important molecular and physiological differences between animals with contrasting fertility may remain undetected. Omics technologies provide new opportunities to identify reproductive phenotypes that more accurately reflect the biological mechanisms underlying fertility in cattle. Genomics enables the identification of single nucleotide polymorphisms, quantitative trait loci, and haplotypes associated with variation in fertility. Transcriptomics has revealed gene expression patterns related to spermatogenesis, sperm function, uterine receptivity, and embryo-maternal communication. Proteomics has identified protein profiles associated with oocyte developmental competence, fertilization capacity, and embryonic development, while metabolomics reflects the biochemical state most closely linked to the expressed phenotype, identifying biomarkers associated with oxidative balance and energy, lipid, and protein metabolism. This manuscript reviews selected studies that have applied omics technologies to identify measurable reproductive phenotypes that better capture the biological basis of fertility in cattle within the context of assisted reproductive technologies. While genomics remains the most widely applied omics approach due to the stability and accessibility of DNA, downstream omics may provide a more accurate representation of the dynamic biological processes defining fertility. Future research should focus on translating these findings into practical assays for rapid and accessible biomarker measurement, supporting informed decision-making at the farm level.
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