Journal
HUMAN HEREDITY
Volume 58, Issue 2, Pages 82-92Publisher
KARGER
DOI: 10.1159/000083029
Keywords
genotype; phenotype; multifactor dimensionality reduction; classification and regression trees; recursive partitioning; patterning; patterning and recursive partitioning; multi-locus; complex disease
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Funding
- NATIONAL CENTER FOR RESEARCH RESOURCES [M01RR000040, K23RR015532] Funding Source: NIH RePORTER
- NATIONAL HEART, LUNG, AND BLOOD INSTITUTE [R01HL073278] Funding Source: NIH RePORTER
- NCRR NIH HHS [RR-1553202, M01-RR00040] Funding Source: Medline
- NHLBI NIH HHS [R01 HL7327801] Funding Source: Medline
- NIGMS NIH HHS [5 T32 GM00821617] Funding Source: Medline
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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. Copyright (C) 2004 S. Karger AG, Basel.
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