title: MDR and PRP: a comparison of methods for high-order genotype-phenotype associations.
abstract: Complex diseases such as cardiovascular disease are likely due to the effects of high-order interactions among multiple genes and demographic factors. Therefore, in order to understand their underlying biological mechanisms, we need to consider simultaneously the effects of genotypes across multiple loci. Statistical methods such as multifactor dimensionality reduction (MDR), the combinatorial partitioning method (CPM), recursive partitioning (RP), and patterning and recursive partitioning (PRP) are designed to uncover complex relationships without relying on a specific model for the interaction, and are therefore well-suited to this data setting. However, the theoretical overlap among these methods and their relative merits have not been well characterized. In this paper we demonstrate mathematically that MDR is a special case of RP in which (1) patterns are used as predictors (PRP), (2) tree growth is restricted to a single split, and (3) misclassification error is used as the measure of impurity. Both approaches are applied to a case-control study assessing the effect of eleven single nucleotide polymorphisms on coronary artery calcification in people at risk for cardiovascular disease.
authors.affiliation: Division of Biostatistics, University of Pennsylvania School of Medicine, Philadelphia, PA 19104, USA. lbastone@cceb.penn.edu
authors.forename: L
authors.initials: L
authors.lastname: Bastone
authors.forename: M
authors.initials: M
authors.lastname: Reilly
authors.forename: D J
authors.initials: DJ
authors.lastname: Rader
authors.forename: A S
authors.initials: AS
authors.lastname: Foulkes
mesh_terms: D002318:Cardiovascular Diseases / Q000235:genetics; D003627:Data Interpretation, Statistical; D020022:Genetic Predisposition to Disease; D005838:Genotype; D006801:Humans; D020412:Multifactorial Inheritance; D010641:Phenotype