Abstract

The analysis of genetic data on general pedigrees can pose enormous computational problems for exact methods of probability and likelihood calculation such as the peeling algorithms (Elston and Stewart, 1971; Cannings et al., 1978) when the pedigree or the genetic model under consideration is overly complex. The obvious representation of a pedigree as a graph leads naturally to an exploitation of graphical models (Lauritzen, 1996), which have their origins in the development of a probabilistic approach to dealing with uncertainty in expert systems (Pearl, 1988). The idea behind such models is to reduce a complex problem into small manageable subcomponents, thus facilitating understanding of the computational issues involved and informing the development of more efficient algorithms for handling such problems. The advantage to be gained by formally setting complex genetics applications into a more general computational framework is in the development of a flexible modelling environment whereby different problems can be tackled by essentially the same software. 

N. A Sheehan, B. Guldbrandtsen, D. A Sorensen

Proceedings of the World Congress on Genetics Applied to Livestock Production, Volume 2002. Session 16, , 16.03, 2002
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