Dairy cattle breeds have a limited genetic pool, making them vulnerable to recessive genetic disorders. Traditional methods for identifying these defects often miss conditions related to immune or metabolic issues, as they can be mistaken for environmental diseases.
This study introduces a data mining approach designed to uncover hidden genetic defects in dairy cattle. By analyzing large-scale genomic data, researchers identified 33 new genetic markers linked to increased juvenile mortality across three dairy breeds. Functional studies confirmed specific mutations in the NOA1, RFC5, and ITGB7 genes, marking the first recorded instances of these disorders in vertebrates.
These findings highlight the impact of inbreeding on dairy cattle health and provide a foundation for better genetic management strategies. Enhancing early detection methods can support improved animal welfare and mitigate economic losses within the dairy industry.
