title: Data mining applied to linkage disequilibrium mapping.
abstract: We introduce a new method for linkage disequilibrium mapping: haplotype pattern mining (HPM). The method, inspired by data mining methods, is based on discovery of recurrent patterns. We define a class of useful haplotype patterns in genetic case-control data and use the algorithm for finding disease-associated haplotypes. The haplotypes are ordered by their strength of association with the phenotype, and all haplotypes exceeding a given threshold level are used for prediction of disease susceptibility-gene location. The method is model-free, in the sense that it does not require (and is unable to utilize) any assumptions about the inheritance model of the disease. The statistical model is nonparametric. The haplotypes are allowed to contain gaps, which improves the method's robustness to mutations and to missing and erroneous data. Experimental studies with simulated microsatellite and SNP data show that the method has good localization power in data sets with large degrees of phenocopies and with lots of missing and erroneous data. The power of HPM is roughly identical for marker maps at a density of 3 single-nucleotide polymorphisms/cM or 1 microsatellite/cM. The capacity to handle high proportions of phenocopies makes the method promising for complex disease mapping. An example of correct disease susceptibility-gene localization with HPM is given with real marker data from families from the United Kingdom affected by type 1 diabetes. The method is extendable to include environmental covariates or phenotype measurements or to find several genes simultaneously.
authors.affiliation: Nokia Research Center and Rolf Nevanlinna Institute, University of Helsinki, Finland.
authors.forename: H T
authors.initials: HT
authors.lastname: Toivonen
authors.forename: P
authors.initials: P
authors.lastname: Onkamo
authors.forename: K
authors.initials: K
authors.lastname: Vasko
authors.forename: V
authors.initials: V
authors.lastname: Ollikainen
authors.forename: P
authors.initials: P
authors.lastname: Sevon
authors.forename: H
authors.initials: H
authors.lastname: Mannila
authors.forename: M
authors.initials: M
authors.lastname: Herr
authors.forename: J
authors.initials: J
authors.lastname: Kere
mesh_terms: D000293:Adolescent; D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D000483:Alleles; D002648:Child; D002675:Child, Preschool; D002874:Chromosome Mapping / Q000379:methods* / Q000706:statistics & numerical data; D003198:Computer Simulation; D003922:Diabetes Mellitus, Type 1 / Q000235:genetics; D005260:Female; D018703:Founder Effect; D005799:Genes, Dominant / Q000235:genetics; D020022:Genetic Predisposition to Disease / Q000235:genetics; D006680:HLA Antigens / Q000235:genetics; D006239:Haplotypes / Q000235:genetics*; D006801:Humans; D007223:Infant; D015810:Linkage Disequilibrium / Q000235:genetics*; D008297:Male; D018895:Microsatellite Repeats / Q000235:genetics; D008875:Middle Aged; D008957:Models, Genetic; D009154:Mutation / Q000235:genetics; D010641:Phenotype; D020641:Polymorphism, Single Nucleotide / Q000235:genetics; D018709:Statistics, Nonparametric; D006113:United Kingdom
chemical_list: D006680:HLA Antigens