An Analytic Approach Using Candidate Gene Selection and Logic Forest to Identify Gene by Environment Interactions (G × E) for Systemic Lupus Erythematosus in African Americans

Abstract

Development and progression of many human diseases, such as systemic lupus erythematosus (SLE), are hypothesized to result from interactions between genetic and environmental factors. Current approaches to identify and evaluate interactions are limited, most often focusing on main effects and two-way interactions. While higher order interactions associated with disease are documented, they are difficult to detect since expanding the search space to all possible interactions of p predictors means evaluating 2p − 1 terms. For example, data with 150 candidate predictors requires considering over 1045 main effects and interactions. In this study, we present an analytical approach involving selection of candidate single nucleotide polymorphisms (SNPs) and environmental and/or clinical factors and use of Logic Forest to identify predictors of disease, including higher order interactions, followed by confirmation of the association between those predictors and interactions identified with disease outcome using logistic regression. We applied this approach to a study investigating whether smoking and/or secondhand smoke exposure interacts with candidate SNPs resulting in elevated risk of SLE. The approach identified both genetic and environmental risk factors, with evidence suggesting potential interactions between exposure to secondhand smoke as a child and genetic variation in the ITGAM gene associated with increased risk of SLE.

The role of environmental exposures and gene–environment interactions in the etiology of systemic lupus erythematous

“…A case‐control study of 152 Japanese patients with SLE and corresponding controls reported an additive interaction between tumor necrosis factor receptor superfamily, member 1B ( TNFRSF1B ) and smoking [59]. Potential G–E interactions between smoking and/or second‐hand smoke have focused on SNPs located on additional SLE susceptibility genes, including those related to interleukin‐10 ( IL‐10 , which may be linked to SLE risk in currently smoking individuals) [60], interleukin‐33 ( IL‐33 ; also linked to SLE risk in current smokers) [61], integrin subunit alpha M ( ITGAM ; linked to SLE risk in the setting of childhood second‐hand smoke) [62], and estrogen receptor 1 ( ESR1 , linked to SLE risk in current smokers) [63].…”

Novel Machine Learning Approach to Identify Preoperative Risk Factors Associated With Super-Utilization of Medicare Expenditure Following Surgery

“…Unlike statistical models that require that data meet certain strong underlying assumptions, machine learning probes data to identify an underlying structure using an iterative approach to learn from the data. A specific machine learning technique, called Logic Forest, iteratively investigates the main data effects and the sample space of all interactions without any specific a priori assumptions . As such, this method represents an optimal approach to identify potentially associated predictors and combinations of associated predictors without any user input bias.…”

An Introduction to Systems Analytics and Integration of Big Omics Data

“…This special issue presented new methodologies in the context of gene-environment, tissue-specific gene expression and how external factors or host genetics impact the microbiome [8][9][10]. Wolf and colleagues developed an analytical approach for identifying the main effects and interactions between genetic and environmental factors linked to a disease outcome [8].…”

“…This special issue presented new methodologies in the context of gene-environment, tissue-specific gene expression and how external factors or host genetics impact the microbiome [8][9][10]. Wolf and colleagues developed an analytical approach for identifying the main effects and interactions between genetic and environmental factors linked to a disease outcome [8]. The method involves selection of candidate genetic and/or environmental factors, utilization of a machine learning algorithm Logic Forest to identify the salient effects and interactions in the disease, followed by confirmation of the association between interactions identified by the algorithm using logistic regression.…”

“…A case study examining the association between SNPs and cigarette smoke exposure with risk of developing systemic lupus erythematosus (SLE) is presented. This identified genetic and environmental risk factors, and potential interactions between exposure to secondhand smoke as a child and genetic variation in the Integrin alpha M (ITGAM) gene associated with increased risk of SLE [8].…”