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A Bayesian threshold-normal mixture model for analysis of a continuous mastitis-related trait
J Ødegård1, P Madsen, D Gianola
1Department of Animal and Aquacultural Sciences, Norwegian University of Life Sciences, N-1432 As, Norway. jorgen.odegard@umb.no
Journal of Dairy Science
|June 16, 2005
Summary
This study introduces a new Bayesian mixture model for mastitis detection in dairy cows. This model improves genetic selection by better accounting for cow health status and somatic cell scores (SCS).
Area of Science:
- Animal Genetics
- Dairy Science
- Veterinary Epidemiology
Background:
- Mastitis in dairy cows is linked to elevated milk somatic cell count (SCS).
- Current genetic selection for mastitis focuses on low SCS, but this may not fully capture disease status.
- SCS observations can represent a mixture influenced by the cow's unknown health status.
Purpose of the Study:
- To develop a novel hierarchical 2-component mixture model for analyzing SCS in dairy cows.
- To infer disease status and genetic parameters for SCS and mastitis liability.
- To provide improved genetic selection criteria beyond simply selecting for lower SCS.
Main Methods:
- Developed a hierarchical 2-component mixture model.
- Incorporated an underlying liability variable to define health status affecting SCS.
- Utilized a Bayesian approach with fixed and random effects for parameter estimation.
- Validated the model using simulation studies.
Main Results:
- The Bayesian model accurately estimated parameters close to true values in simulations.
- The model allows for inferences on both SCS and liability to mastitis.
- It accounts for variations in prior mastitis probability across subgroups (herds, families).
Conclusions:
- The proposed mixture model offers a more robust approach to genetic analysis of mastitis than traditional methods.
- It provides more appealing selection criteria for improving dairy cow health.
- The model is adaptable for various genetic analyses of mixture traits.