Disease resistance modelled as first-passage times of genetically dependent stochastic processes

Authors: S. Sæbø; T. Almøy; A. H. Aastveit

Source: Journal of the Royal Statistical Society: Series C (Applied Statistics), Volume 54, Number 1, January 2005 , pp. 273-285(13)

Publisher: Wiley-Blackwell

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Abstract:

Summary.

Mastitis resistance data on dairy cattle are modelled as first-passage times of stochastic processes. Population heterogeneity is included by expressing process parameters as functions of shared random variables. We show how dependences between individuals, e.g. genetic relationships, can be exploited in the analyses. The method can be extended to handle situations with multiple hidden causes of failure. Markov chain Monte Carlo methods are used for estimation.

Keywords: Bayesian analysis; Genetic information; Inverse Gaussian distribution; Markov chain Monte Carlo methods; Mastitis; Survival analysis; Wiener process

Document Type: Research article

DOI: http://dx.doi.org/10.1111/j.1467-9876.2005.00483.x

Affiliations: 1: Agricultural University of Norway, Ås, Norway

Publication date: 2005-01-01