Biomarker Detection in Association Studies: Modeling SNPs Simultaneously via Logistic ANOVA

Yoonsuh Jung, Jianhua Z. Huang, Jianhua Hu

Research output: Contribution to journalArticlepeer-review

8 Scopus citations

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 the L 1-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.
Original languageEnglish (US)
Pages (from-to)1355-1367
Number of pages13
JournalJournal of the American Statistical Association
Volume109
Issue number508
DOIs
StatePublished - Dec 22 2014
Externally publishedYes

Bibliographical note

KAUST Repository Item: Exported on 2020-10-01
Acknowledged KAUST grant number(s): KUS-CI-016-04, GRP-CF-2011-19-P-Gao-Huang
Acknowledgements: Hu's work was partially supported by the National Institute of Health Grants R21CA129671, R01GM080503, R01CA158113, and CGSG P30 CA016672. Huang's work was partially supported by grants from NSF (DMS-0907170, DMS-1007618, DMS-1208952), and Award Number KUS-CI-016-04 and GRP-CF-2011-19-P-Gao-Huang, made by King Abdullah University of Science and Technology (KAUST). The authors thank the editor, the associate editor, and reviewers for many constructive comments.
This publication acknowledges KAUST support, but has no KAUST affiliated authors.

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