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ARS Home » Northeast Area » Beltsville, Maryland (BARC) » Beltsville Agricultural Research Center » Animal Genomics and Improvement Laboratory » Research » Publications at this Location » Publication #390592
Research Project: Improving Dairy Animals by Increasing Accuracy of Genomic Prediction, Evaluating New Traits, and Redefining Selection Goals

Location: Animal Genomics and Improvement Laboratory

Title: Mastering mastitis: how genetics can help & where we go from here

Author
item Miles, Asha
item PARKER GADDIS, KRISTEN - Council On Dairy Cattle Breeding

Submitted to: Mastitis Council Meeting Proceedings
Publication Type: Proceedings
Publication Acceptance Date: 12/15/2021
Publication Date: 2/1/2022
Citation: Miles, A.M., Parker Gaddis, K.L. 2022. Mastering mastitis: how genetics can help & where we go from here. National Mastitis Council Meeting Proceedings. p. 33-46.

Interpretive Summary: Combining optimized management with genetic improvement strategies has a proven record of increasing mastitis resiliency among US dairy cattle. In this paper we describe how the system of U.S. evaluations work, the process of developing evaluations for mastitis, and discuss the challenges and opportunities facing us as we move into the era of big data and -omics discovery.

Technical Abstract: Genetic and genomic evaluations are delivered thanks to the cooperation of key industry partners through a complex system of data sharing and management. Standardizing definitions of mastitis across heterogeneous data sources and accurately describing the underlying biology of the trait are key challenges. Including genomic data has exponentially increased the rate of genetic gain compared to conventional genetic evaluations. Evaluations for SCS have been available since 1994 and for clinical mastitis from 2018, the latter of which required developing many data quality control measures. Using a selection index like NM$ is an effective way to select for overall high performance and control for the unfavorable correlation of mastitis and milk yield. Examining the downstream functional effects of marker variants may help prioritize SNPs for inclusion in evaluations, but complex gene networks muddy the waters. ssGBLUP can mitigate biases in evaluations that arise from animals “pre-selected” on the basis of their genetic merit; faster computer algorithms are a must. Milk microbiome work should focus on collective molecular signatures for new insight into the mechanistics of mastitis resistance and milk quality. Among the challenges of sensor-based milk data are quality assurance within and across systems and potential legal controversy regarding data ownership.