JSM 2011 Online Program

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Abstract Details

Activity Number: 357
Type: Contributed
Date/Time: Tuesday, August 2, 2011 : 10:30 AM to 12:20 PM
Sponsor: Section on Statistics in Epidemiology
Abstract - #302680
Title: Network-Guided, Sparse Regression Modeling for Detection of Gene-Gene Interactions
Author(s): Chen Lu*+ and Josée Dupuis and Eric Kolaczyk
Companies: Boston University and Boston University and Boston University
Address: Boston University School of Public Health , Boston, MA, 02118,
Keywords: gene-gene interaction ; sparse regression ; network ; pathway ; penalized regression
Abstract:

Unlike Mendelian diseases, in which disease phenotypes are largely driven by mutation in a single gene locus, complex diseases are believed to be caused by a number of factors of both genetic and environmental nature, as well as lifestyle. Progress in this area must therefore come from unraveling the interplay of genes with each other, as well as with environment, through the system of biological pathways and related networks. We present a novel, network-guided statistical methodology to facilitate the discovery of gene-gene interactions associated with complex quantitative traits in human disease. Our method uses sparse regression principles to fit regression models (i.e., of phenotype on genetic markers) with second-order interactions, based on a penalized least-squares criterion, where the penalty incorporates known network biology in the form of pathways and gene function. We discuss the derivation of our methodology, as well as its implementation, and present results from simulation studies and various applications.


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