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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Vydané v:	IEEE/ACM transactions on computational biology and bioinformatics Ročník 3; číslo 2; s. 174 - 185
Hlavní autori:	Sevon, P., Toivonen, H., Ollikainen, V.
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	United States IEEE 01.04.2006 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	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
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Shrnutí:	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
Bibliografia:	ObjectType-Article-2 SourceType-Scholarly Journals-1 ObjectType-Feature-1 content type line 14 content type line 23 ObjectType-Article-1 ObjectType-Feature-2
ISSN:	1545-5963 1557-9964
DOI:	10.1109/TCBB.2006.28