title: Comparison of multivariate adaptive regression splines and logistic regression in detecting SNP-SNP interactions and their application in prostate cancer.
abstract: Single nucleotide polymorphism (SNP) interaction plays a critical role for complex diseases. The primary limitation of logistic regressions (LR) in testing SNP-SNP interactions is that coefficient estimates may not be valid because of numerous terms in a model. Multivariate adaptive regression splines (MARS) have useful features to effectively reduce the number of terms in a model. To study how MARS can address these drawbacks possibly better than LR, the power of MARS and LR with SNPs using the reference-coding and additive-mode scheme was compared using simulated data of ten SNPs for 400 subjects based on 1,000 replications for five interaction models. In overall scenarios, MARS performed better than LR. In the model with a dominant two-way interaction, the power range was 76-96% for MARS and 1-8% for LR in both coding schemes. In the dominant three-way interaction model, the power was 57-85% for MARS and less than 4% for LR. In the prostate cancer example, we evaluated the association between ten SNPs and prostate cancer risk in 649 Caucasians. The best model with one two-way and one three-way interaction was selected using MARS. The findings supported that MARS may provide a useful tool for exploring SNP-SNP interactions.
authors.affiliation: Medical Statistics Section, University of Alabama at Birmingham, Birmingham, AL, USA.
authors.forename: Hui-Yi
authors.initials: HY
authors.lastname: Lin
authors.affiliation: Medical Statistics Section, University of Alabama at Birmingham, Birmingham, AL, USA.
authors.forename: Wenquan
authors.initials: W
authors.lastname: Wang
authors.affiliation: INC Research, Raleigh, NC, USA.
authors.forename: Yung-Hsin
authors.initials: YH
authors.lastname: Liu
authors.affiliation: Medical Statistics Section, University of Alabama at Birmingham, Birmingham, AL, USA.
authors.forename: Seng-Jaw
authors.initials: SJ
authors.lastname: Soong
authors.affiliation: Department of Human Genetics, Virginia Commonwealth University, Richmond, VA, USA.
authors.forename: Timothy P
authors.initials: TP
authors.lastname: York
authors.affiliation: Department of Biostatistics, Tulane University, New Orleans, LA, USA.
authors.forename: Leann
authors.initials: L
authors.lastname: Myers
authors.affiliation: Sylvester Comprehensive Cancer Center and Department of Epidemiology and Public Health, University of Miami School of Medicine, Miami, FL, USA. jhu@med.miami.edu.
authors.forename: Jennifer J
authors.initials: JJ
authors.lastname: Hu
mesh_terms: D016022:Case-Control Studies; D003198:Computer Simulation / Q000706:statistics & numerical data; D004843:Epistasis, Genetic; D005787:Gene Frequency; D005838:Genotype; D006801:Humans; D016015:Logistic Models; D008297:Male; D008957:Models, Genetic; D020641:Polymorphism, Single Nucleotide; D011471:Prostatic Neoplasms / Q000235:genetics*; D012307:Risk Factors; D044465:White People / Q000235:genetics / Q000706:statistics & numerical data; D051759:Xeroderma Pigmentosum Group D Protein / Q000235:genetics
chemical_list: D051759:Xeroderma Pigmentosum Group D Protein; C497292:ERCC2 protein, human