title: Biomarker Detection in Association Studies: Modeling SNPs Simultaneously via Logistic ANOVA.
abstract: In genome-wide association studies, the primary task is to detect biomarkers in the form of Single Nucleotide Polymorphisms (SNPs) that have nontrivial associations with a disease phenotype and some other important clinical/environmental factors. However, the extremely large number of SNPs comparing to the sample size inhibits application of classical methods such as the multiple logistic regression. Currently the most commonly used approach is still to analyze one SNP at a time. In this paper, we propose to consider the genotypes of the SNPs simultaneously via a logistic analysis of variance (ANOVA) model, which expresses the logit transformed mean of SNP genotypes as the summation of the SNP effects, effects of the disease phenotype and/or other clinical variables, and the interaction effects. We use a reduced-rank representation of the interaction-effect matrix for dimensionality reduction, and employ theL1-penalty in a penalized likelihood framework to filter out the SNPs that have no associations. We develop a Majorization-Minimization algorithm for computational implementation. In addition, we propose a modified BIC criterion to select the penalty parameters and determine the rank number. The proposed method is applied to a Multiple Sclerosis data set and simulated data sets and shows promise in biomarker detection.
authors: Department of Statistics, Univerisity of Waikato, Private Bag 3105, Hamilton 3240, New Zealand.
authors: Yoonsuh
authors: Y
authors: Jung
authors: Department of Statistics, Texas A&M University, College Station, TX, USA, and Special Term Professor at ISEM, Captial University of Economics and Business, Beijing, China.
authors: Jianhua Z
authors: JZ
authors: Huang
authors: Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
authors: Jianhua
authors: J
authors: Hu
keywords: BIC; GWAS; L1-penalty; MM Algorithm; Penalized Bernoulli Likelihood; Simultaneous Modeling of SNPs
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