PubMed · 8809489
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C Rovee-Collier. 1996. Testing the limits: a model of human dysfunction.. https://pubmed.ncbi.nlm.nih.gov/8809489/
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Single-locus disorders in domesticated animals were among the first Mendelian traits to be documented after the rediscovery of Mendelism, and to be included in early linkage maps. The use of linkage maps and (increasingly) comparative genomics has been central to the identification of the causative gene for single-locus disorders of considerable practical importance. The 'score-card' in domestic animals is now more than 100 disorders for which the molecular lesion has been identified and hence for which a DNA test is available. Because of the limited lifespan of any such test, a cost-effective and hence popular means of protecting the intellectual property inherent in a DNA test is not to publish the discovery. While understandable, this practice creates a disconcerting precedent. For multifactorial disorders that are scored on an all-or-none basis or into many classes, the effectiveness of control schemes could be greatly enhanced by selection on estimated breeding values for liability. Genetic variation for resistance to pathogens and parasites is ubiquitous. Selection for resistance can therefore be successful. Because of the technical and welfare challenges inherent in the requirement to expose animals to pathogens or parasites in order to be able to select for resistance, there is a very active search for DNA markers for resistance. The first practical fruits of this research were seen in 2002, with the launch of a national scrapie control programme in the UK.
Quantitative risk assessments are now required to support many regulatory decisions involving infectious diseases of animals. Current methods, however, do not consider the relative values of historical and recent data. A Markov-chain model can use specific disease characteristics to estimate the present value of disease information collected in the past. Uncertainty about the disease characteristics and variability among animals and herds can be accounted for with Monte Carlo simulation modeling. This results in a transparent method of valuing historical testing information for use in risk assessments. We constructed such a model to value historical testing information in a more-transparent and -reproducible manner. Applications for this method include trade, food safety, and domestic animal-health regulations.