title: Classification methods for confronting heterogeneity.
abstract: Recursive partitioning/tree models are discussed as a method of dissecting the complex nature of traits with different causal mechanisms operating in different subsets of the data (e.g., different genes operating in different subsets of families). In addition to the straightforward application of classification and regression trees to define more homogeneous subsets of the data on which to conduct further analysis, developments incorporating linkage analysis into the definition of the regression trees (Shannon et al., 2000) are discussed. The pros and cons of recursive partitioning vs. the related approach of context-dependent analysis (Turner et al., 1999) are also reviewed as two promising analysis strategies that may be useful for genetic dissection of complex traits.
authors.affiliation: Division of Biostatistics, Washington University School of Medicine, St. Louis, Missouri 63110, USA.
authors.forename: M A
authors.initials: MA
authors.lastname: Province
authors.forename: W D
authors.initials: WD
authors.lastname: Shannon
authors.forename: D C
authors.initials: DC
authors.lastname: Rao
mesh_terms: D030342:Genetic Diseases, Inborn / Q000145:classification / Q000235:genetics*; D018740:Genetic Heterogeneity; D008040:Genetic Linkage; D005838:Genotype; D006801:Humans; D006973:Hypertension / Q000145:classification / Q000235:genetics; D012307:Risk Factors; D044465:White People