title: Using data mining to address heterogeneity in the Southampton data.
abstract: When analyzing complex traits such as asthma, heterogeneity needs to be assumed. With this in mind, to identify a more homogeneous group of asthmatic patients, we analyzed the Southampton data using the data mining technique known as the regression tree method and the two most inheritable quantitative phenotypes (LnIgE and RAST) as the target variables. Two-point and multipoint nonparametric linkage analyses were carried out using one of the subgroups as affected. In addition, we performed quantitative trait loci nonparametric linkage analysis using each phenotype as the outcome. The results from the affected-sibpairs method and quantitative linkage analysis were compared.
authors.affiliation: Department of Clinical Research, National Taiwan University Hospital, Taipei, Taiwan.
authors.forename: C J
authors.initials: CJ
authors.lastname: Chang
authors.forename: C S
authors.initials: CS
authors.lastname: Fann
mesh_terms: D000328:Adult; D001249:Asthma / Q000453:epidemiology / Q000235:genetics*; D002648:Child; D004739:England; D005260:Female; D005787:Gene Frequency; D018740:Genetic Heterogeneity; D005828:Genetics, Population; D006801:Humans; D007073:Immunoglobulin E / Q000097:blood; D008297:Male; D008432:Mathematical Computing; D010641:Phenotype; D019655:Quantitative Trait, Heritable; D011852:Radioallergosorbent Test; D012044:Regression Analysis; D006255:Rhinitis, Allergic, Seasonal / Q000453:epidemiology / Q000235:genetics; D012984:Software; D018709:Statistics, Nonparametric
chemical_list: D007073:Immunoglobulin E