A Novel Statistical Model to Estimate Host Genetic Effects Affecting Disease Transmission

Area of Science:

• Quantitative genetics
• Epidemiology
• Disease ecology

Background:

• Genetic diversity impacts disease spread in plants, livestock, and humans.
• Existing epidemiological models struggle to account for individual heterogeneity in disease traits like susceptibility and infectivity.
• Current quantitative genetic models do not fully capture heritable variation in infectivity due to nonlinear infection dynamics.

Purpose of the Study:

• To develop a novel statistical model and inference method for estimating genetic parameters of host susceptibility and infectivity.
• To address limitations in current models regarding nonlinear infection dynamics and heritable variation in infectivity.
• To provide a tool for better understanding and controlling infectious disease spread.

Main Methods:

• Combined quantitative genetic models of social interactions with stochastic processes.
• Modeled nonlinear and dynamic infection processes.
• Employed adaptive Bayesian computational techniques for parameter estimation.

Main Results:

• The model accurately estimates heritabilities and genetic risks for both susceptibility and infectivity.
• Demonstrated the ability to capture heritable variation in infectivity, a trait often overlooked.
• Validated using simulated epidemic data.

Conclusions:

• The novel methodology can accurately estimate genetic parameters for susceptibility and infectivity.
• This approach can significantly influence understanding and management of infectious disease spread.
• Potential applications include selective breeding for livestock disease control and predicting/controlling human disease outbreaks.