Biocomputing 2015 2014
DOI: 10.1142/9789814644730_0019
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A Screening-Testing Approach for Detecting Gene-Environment Interactions Using Sequential Penalized and Unpenalized Multiple Logistic Regression

Abstract: Gene-environment (G×E) interactions are biologically important for a wide range of environmental exposures and clinical outcomes. Because of the large number of potential interactions in genomewide association data, the standard approach fits one model per G×E interaction with multiple hypothesis correction (MHC) used to control the type I error rate. Although sometimes effective, using one model per candidate G×E interaction test has two important limitations: low power due to MHC and omitted variable bias. T… Show more

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Cited by 5 publications
(20 citation statements)
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“…For the bladder cancer example presented in Frost et al. (), f=2 (for the omnibus tests associated with the test stage models populated using the marginal association filter and gene‐environment correlation filter) and d=4 for the four SNP‐smoking interactions that had significant Wald tests at a level‐specific FDR of q=0.1. Therefore, if hierarchical FDR been employed in this case with q=0.1, approximate FDR control for the interaction‐specific Wald test results would have been achieved at a level of 0.1(2+4)/(4+1)=0.12.…”
Section: Methodsmentioning
confidence: 99%
See 3 more Smart Citations
“…For the bladder cancer example presented in Frost et al. (), f=2 (for the omnibus tests associated with the test stage models populated using the marginal association filter and gene‐environment correlation filter) and d=4 for the four SNP‐smoking interactions that had significant Wald tests at a level‐specific FDR of q=0.1. Therefore, if hierarchical FDR been employed in this case with q=0.1, approximate FDR control for the interaction‐specific Wald test results would have been achieved at a level of 0.1(2+4)/(4+1)=0.12.…”
Section: Methodsmentioning
confidence: 99%
“…To support comparative evaluation of the extended SPUR method, the same alternative methods used in Frost et al. () were also employed to identify G× E interactions for the simulated and real data sets. For each evaluated data set, G× E interaction detection was performed using three benchmark methods: …”
Section: Methodsmentioning
confidence: 99%
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“…Although widely applied for gene-environment interaction detection [2023], p -value weighting can have a significant impact on gene set testing power given the significant growth in the size of common gene set collections, e.g., even the very selective Molecular Signatures Database (MSigDB) [8] now includes over 10,000 sets. For such large gene set collections, MHC can lower statistical power so substantially that it becomes impossible to identify true associations for many genomic data sets [24].…”
Section: Introductionmentioning
confidence: 99%