Selection signature analysis of whole-genome sequences to identify genome differences between selected and unselected Holstein cattle

Authors: CAI, JIARUI - University Of Maryland; YANG, LIU - University Of Maryland; GAO, YAHUI - University Of Maryland; Liu, Ge; DA, YANG - University Of Minnesota; MA, LI - University Of Maryland

Submitted to: Animals
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 7/29/2025
Publication Date: 7/31/2025
Citation: Cai, J., Yang, L., Gao, Y., Liu, G., Da, Y., Ma, L. 2025. Selection signature analysis of whole-genome sequences to identify genome differences between selected and unselected Holstein cattle. Animals. https://doi.org/10.3390/ani15152247.
DOI: https://doi.org/10.3390/ani15152247

Interpretive Summary: Understanding how long-term selection shapes the cattle genome is key to improving traits like milk production, fertility, and health. Using a rare line of unselected Holstein cattle maintained since 1964, we compared their genomes to modern, selected Holsteins. This revealed widespread genomic changes driven by decades of artificial selection, affecting many regions across the genome rather than just a few major genes. Many of these regions are linked to traits important for productivity and animal well-being. These findings provide potential markers to breed more efficient cattle in the future. This research benefits scientists and others working to improve animal health and productivity.

Technical Abstract: A unique line of Holstein cattle was maintained without selection in Minnesota since 1964. Comparison between this line of unselected Holstein and animals under selection provides useful insights connecting selection and complex traits in cattle. Utilizing these unique resources and sequence data, we seek to identify genome changes due to selection. We sequenced 30 unselected and 54 selected Holstein cattle and compared their sequence variants to identify selection signatures. After many years of selection, the two populations showed completely different patterns in the genome-level population structure and linkage disequilibrium. By integrating signals from five different detection methods, we detected consensus selection signatures from at least four methods covering 14,533 SNPs and 155 protein coding genes. An integrated analysis of selection signatures with gene annotation, pathway, and cattle QTL database demonstrated that the genomic regions under selection are related to milk productivity, health, and reproductive efficiency. The polygenic nature of these complex traits is evident from hundreds of selection signatures and candidate genes, suggesting that long-term artificial selection has acted on the whole genome rather than a few major genes. In summary, our study identified candidate selection signatures underlying phenotypic differences between unselected and selected Holstein cows and revealed insights of the genetic basis of complex traits in cattle.