TreeDT: tree pattern mining for gene mapping

We describe TreeDT, a novel association-based gene mapping method. Given a set of disease-associated haplotypes and a set of control haplotypes, TreeDT predicts likely locations of a disease susceptibility gene. TreeDT extracts, essentially in the form of haplotype trees, information about historica...

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Bibliographic Details
Published in:IEEE/ACM transactions on computational biology and bioinformatics Vol. 3; no. 2; pp. 174 - 185
Main Authors: Sevon, P., Toivonen, H., Ollikainen, V.
Format: Journal Article
Language:English
Published: United States IEEE 01.04.2006
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Algorithm design and analysis
Algorithms
Biological cells
Biology and genetics
Chromosome mapping
Chromosome Mapping - methods
Chromosomes
Computational Biology - methods
Computer Simulation
Data mining
Diabetes Mellitus, Type 1 - genetics
Diseases
Genetic Predisposition to Disease - genetics
Genetics
Haplotypes
Haplotypes - genetics
History
Humans
Linkage Disequilibrium - genetics
nonnumerical algorithms and problems
nonparametric statistics
Pedigree
Performance analysis
Performance evaluation
Recombination, Genetic - genetics
Statistics, Nonparametric
Studies
Testing
Trees
ISSN:1545-5963, 1557-9964
Online Access:Get full text
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Summary:We describe TreeDT, a novel association-based gene mapping method. Given a set of disease-associated haplotypes and a set of control haplotypes, TreeDT predicts likely locations of a disease susceptibility gene. TreeDT extracts, essentially in the form of haplotype trees, information about historical recombinations in the population: A haplotype tree constructed at a given chromosomal location is an estimate of the genealogy of the haplotypes. TreeDT constructs these trees for all locations on the given haplotypes and performs a novel disequilibrium test on each tree: Is there a small set of subtrees with relatively high proportions of disease-associated chromosomes, suggesting shared genetic history for those and a likely disease gene location? We give a detailed description of TreeDT and the tree disequilibrium tests, we analyze the algorithm formally, and we evaluate its performance experimentally on both simulated and real data sets. Experimental results demonstrate that TreeDT has high accuracy on difficult mapping tasks and comparisons to other methods (EATDT, HPM, TDT) show that TreeDT is very competitive
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ISSN:1545-5963
1557-9964
DOI:10.1109/TCBB.2006.28