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ARS Home » Plains Area » Clay Center, Nebraska » U.S. Meat Animal Research Center » Genetics and Animal Breeding » Research » Publications at this Location » Publication #207372

Title: Using Epidemiological Models and Genetic Selection to Identify Theoretical Opportunities to Reduce Disease Impact

item Snowder, Gary

Submitted to: American Society of Animal Science Annual Meeting
Publication Type: Abstract Only
Publication Acceptance Date: 3/9/2007
Publication Date: 7/9/2007
Citation: Snowder, G.D. 2007. Using epidemiological models and genetic selection to identify theoretical opportunities to reduce disease impact [abstract]. Journal of Animal Science. 85 (Supplement 1):373. Abstract #409.

Interpretive Summary:

Technical Abstract: Selection for disease resistance is a contemporary topic with developing approaches for genetic improvement. Merging the sciences of genetic selection and epidemiology is essential to identify selection schemes to enhance disease resistance. Epidemiological models can identify theoretical opportunities for genetic selection to reduce the impact of a disease. Potential selection venues may be more appropriately comprehended by compartmentalization of disease components using epidemiological models. This study considers the standard Susceptible, Infected, Recovered (SIR) epidemiological model and five other common epidemiological models (MSIR, SEIR, SIS, Carrier State, and SIR Vector) with genetic selection alternatives. Theoretical modeling of genetic selection effects on epidemiological models were used to: predict the economic effect of selection, estimate the optimal number of resistant animals to prevent an epidemic, and determine genetic selection alternatives. Selection alternatives to genetic disease resistance include lowering the probability of being infected, tolerance for the pathogen, longer latency period, less severe clinical expression, faster recovery rate, and compensatory rebound. These selection alternatives can result in favorable changes to the differential equations for susceptibility, infected, and recovery rates. Potentially undesirable consequences due to selection can be predicted, such as an increase in the size of sub clinical populations harboring and shedding pathogens. When applied to actual data for bovine respiratory disease, this approach identifies the complexity of genetic resistance to this disease while detecting potential opportunities for genetic selection. When a disease such as bovine respiratory disease is caused by different pathogens (bacterial, viral, mycoplasmal, etc.) with different pathways of infection, the probability of reducing the disease prevalence with genetic selection is diminished.