DocumentCode :
1080308
Title :
Finding Consistent Gene Transmission Patterns on Large and Complex Pedigrees
Author :
Pirinen, M. ; Gasbarra, D.
Author_Institution :
Dept. of Math. & Stat., Helsinki Univ.
Volume :
3
Issue :
3
fYear :
2006
Firstpage :
252
Lastpage :
262
Abstract :
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
Keywords :
Markov processes; Monte Carlo methods; biology computing; cellular biophysics; genetics; heuristic programming; molecular biophysics; probability; APE program; Allelic Path Explorer; Markov chain Monte Carlo simulation; approximative conditional probability distribution; consistent gene transmission patterns; heuristic algorithm; large complex pedigrees; partially observed genotype data; Computer errors; Computer science; Data analysis; Genetics; Heuristic algorithms; Monte Carlo methods; Probability distribution; Sorting; Sum product algorithm; Testing; Backtracking; biology and genetics; consistent genotype configuration.; constraint satisfaction; heuristic methods; pedigree; sorting and searching; Algorithms; Biological Evolution; Chromosome Mapping; Computer Simulation; Evolution, Molecular; Genotype; Humans; Models, Genetic; Pattern Recognition, Automated; Pedigree; Recombination, Genetic;
fLanguage :
English
Journal_Title :
Computational Biology and Bioinformatics, IEEE/ACM Transactions on
Publisher :
ieee
ISSN :
1545-5963
Type :
jour
DOI :
10.1109/TCBB.2006.36
Filename :
1668024
Link To Document :
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