On the use of multifactor dimensionality reduction (MDR) and classification and regression tree (CART) to identify haplotype-haplotype interactions in genetic studies.
Haplotype-based approaches may have greater power than single-locus analyses when the SNPs are in strong linkage disequilibrium with the risk locus. To overcome potential complexities owing to large numbers of haplotypes in genetic studies, we evaluated two data mining approaches, multifactor dimensionality reduction (MDR) and classification and regression tree (CART), with the concept of haplotypes considering their haplotype uncertainty to detect haplotype-haplotype (HH) interactions. In evaluation of performance for detecting HH interactions, MDR had higher power than CART, but MDR gave a slightly higher type I error. Additionally, we performed an HH interaction analysis with a publicly available dataset of Parkinson's disease and confirmed previous findings that the RET proto-oncogene is associated with the disease. In this study, we showed that using HH interaction analysis is possible to assist researchers in gaining more insight into identifying genetic risk factors for complex diseases.
Institute of Public Health, Yang-Ming University, Taipei, Taiwan.
Ai-Ru
AR
Hsieh
Ching-Lin
CL
Hsiao
Su-Wei
SW
Chang
Hui-Min
HM
Wang
Cathy S J
CS
Fann
D000465:Algorithms; D003198:Computer Simulation; D057225:Data Mining; D006239:Haplotypes / Q000235:genetics*; D006801:Humans; D015810:Linkage Disequilibrium; D058011:Multifactor Dimensionality Reduction / Q000379:methods*; D010300:Parkinson Disease / Q000235:genetics; D020641:Polymorphism, Single Nucleotide; D000090063:Proto-Oncogene Mas; D051096:Proto-Oncogene Proteins c-ret / Q000235:genetics; D012044:Regression Analysis
C000715470:MAS1 protein, human; D000090063:Proto-Oncogene Mas; D051096:Proto-Oncogene Proteins c-ret; C099282:RET protein, human