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Open Access Research

The analysis of disease biomarker data using a mixed hidden Markov model (Open Access publication)

Johann C Detilleux

Author Affiliations

Quantitative Genetics Group, Department of Animal Production, Faculty of Veterinary Medicine, University of Liège, Liège, Belgium

Genetics Selection Evolution 2008, 40:491-509  doi:10.1186/1297-9686-40-5-491


The electronic version of this article is the complete one and can be found online at: http://www.gsejournal.org/content/40/5/491


Received:13 September 2007
Accepted:3 March 2008
Published:15 September 2008

© 2008 INRA, EDP Sciences

Abstract

A mixed hidden Markov model (HMM) was developed for predicting breeding values of a biomarker (here, somatic cell score) and the individual probabilities of health and disease (here, mastitis) based upon the measurements of the biomarker. At a first level, the unobserved disease process (Markov model) was introduced and at a second level, the measurement process was modeled, making the link between the unobserved disease states and the observed biomarker values. This hierarchical formulation allows joint estimation of the parameters of both processes. The flexibility of this approach is illustrated on the simulated data. Firstly, lactation curves for the biomarker were generated based upon published parameters (mean, variance, and probabilities of infection) for cows with known clinical conditions (health or mastitis due to Escherichia coli or Staphylococcus aureus). Next, estimation of the parameters was performed via Gibbs sampling, assuming the health status was unknown. Results from the simulations and mathematics show that the mixed HMM is appropriate to estimate the quantities of interest although the accuracy of the estimates is moderate when the prevalence of the disease is low. The paper ends with some indications for further developments of the methodology.

Keywords:
hidden Markov model; mixed model; mastitis; somatic cell score

Research

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