The Development of Genomics Applied to Cattle Breeding
A special issue of Agriculture (ISSN 2077-0472). This special issue belongs to the section "Farm Animal Production".
Deadline for manuscript submissions: 30 November 2025 | Viewed by 407
Special Issue Editor
Interests: bovine genetics and breeding; sustainable livestock husbandry; forage innovation and valorization
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
The integration of genomics into livestock breeding has revolutionized cattle genetics, enabling unprecedented precision in trait selection and herd improvement. From early marker-assisted selection to cutting-edge genome-wide association studies and CRISPR-based editing, genomic tools have reshaped our understanding of complex traits like disease resistance, feed efficiency, and climate adaptability. This Special Issue aims to synthesize the historical evolution of genomic technologies in cattle breeding, explore their translational impacts on industry practices, and highlight emerging frontiers.
We seek contributions that bridge gaps between discovery and application, including innovative methods in genomic prediction, the functional validation of candidate genes, and multi-omics integration for phenotype enhancement. Topics may span population genomics, gene-edited livestock models, and bioinformatics pipelines for breeding optimization. We particularly encourage interdisciplinary studies combining genetics, bioinformatics, and agricultural economics to address global challenges such as food security and sustainable livestock production.
This issue welcomes original research, critical reviews, and technical perspectives that advance the ethical, scalable deployment of genomic solutions in cattle breeding. Together, we aim to catalyze a paradigm shift toward data-driven, precision-engineered livestock systems.
Dr. Chugang Mei
Guest Editor
Manuscript Submission Information
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Keywords
- cattle breeding
- genomics
- gene editing
- genomic selection
- functional genomics
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