A unifying theory for genetic epidemiological analysis of binary disease data

Area of Science:

• Livestock genetics
• Epidemiology
• Quantitative genetics

Background:

• Genetic selection for host resistance complements chemical treatments for livestock diseases.
• Current binary disease data analysis methods neglect infection dynamics.
• Genetic analyses often overlook host infectiousness, a key factor in epidemic progression.

Purpose of the Study:

• Derive a genetic-epidemiological function for binary disease data.
• Incorporate infection dynamics, host susceptibility, and infectiousness.
• Validate the function and explore its integration into genetic analyses.

Main Methods:

• Derived a novel expression for infection probability.
• Validated the expression using epidemiological theory and simulations.
• Explored integration into quantitative genetic models.

Main Results:

• The derived expression is valid across various genetic-epidemiological scenarios.
• Conventional methods can be adapted for variation in susceptibility or moderate variation in both.
• Super-spreader identification requires novel analytical methods due to biased estimates in linear models.

Conclusions:

• A new genetic-epidemiological function for binary infectious disease data has been developed.
• This function accounts for infection dynamics, host susceptibility, and infectiousness.
• Novel methods are needed to identify super-spreaders effectively.