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Generalized-Linear-Mixed Models (GLMMs) accurately estimate genetic parameters for disease susceptibility using longitudinal infection data. This approach enhances genetic selection for disease resistance in populations.

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Area of Science:

  • Quantitative genetics
  • Epidemiology
  • Animal breeding

Background:

  • Infectious disease control can be enhanced by genetic selection, leveraging indirect genetic effects during transmission.
  • Classical quantitative genetics underestimates the potential of genetic selection due to complex transmission dynamics.
  • Estimating genetic parameters and breeding values requires specialized statistical methods for disease transmission.

Purpose of the Study:

  • To evaluate Generalized-Linear-Mixed Models (GLMMs) for estimating genetic parameters and breeding values for host susceptibility to infection.
  • To assess the impact of data characteristics on the accuracy of GLMMs in simulated epidemic data.
  • To determine the feasibility of using GLMMs with longitudinal infection state data in animal breeding.

Main Methods:

  • Simulated epidemic data with longitudinal records of individual infection states were used.
  • Generalized-Linear-Mixed Models (GLMMs) were implemented using standard animal breeding software.
  • The influence of sampling interval, population structure, infection dynamics, and model formulation was assessed.

Main Results:

  • GLMMs accurately and unbiasedly estimated genetic variance for susceptibility.
  • GLMMs achieved good prediction accuracies for breeding values related to disease susceptibility.
  • The time interval between observations significantly impacted estimation accuracy, more so than group size.

Conclusions:

  • GLMMs are accurate and easily implementable for estimating genetic parameters and breeding values for disease susceptibility.
  • Longitudinal records of individual infection status are crucial for effective GLMM application.
  • The optimal observation interval for GLMMs is dependent on disease-specific infection and recovery rates.