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A heuristic algorithm for finding gene transmission patterns on large and complex pedigrees with partially observed genotype data is proposed. The method can be used to generate an initial point for a Markov chain Monte Carlo simulation or to check that the given pedigree and the genotype data are consistent. In small pedigrees, the algorithm is exact by exhaustively enumerating all possibilities, but, in large pedigrees, with a considerable amount of unknown data, only a subset of promising configurations can actually be checked. For that purpose, the configurations are ordered by combining the approximative conditional probability distribution of the unknown genotypes with the information on the relationships between individuals. We also introduce a way to divide the task into subparts, which has been shown to be useful in large pedigrees. The algorithm has been implemented in a program called APE (Allelic Path Explorer) and tested in three different settings with good results.
Backtracking, heuristic methods, constraint satisfaction, sorting and searching, biology and genetics, pedigree, consistent genotype configuration.
Dario Gasbarra, Matti Pirinen, "Finding Consistent Gene Transmission Patterns on Large and Complex Pedigrees", IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 3, no. , pp. 252-262, July-September 2006, doi:10.1109/TCBB.2006.36
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