Comparison of 2 models for gene–environment interactions: an example of simulated gene–medication interactions on systolic blood pressure in family-based data. Issue 7 (October 2016)

• Record Type: Journal Article
• Authors: Fernández-Rhodes, Lindsay; Hodonsky, Chani; Graff, Mariaelisa; Love, Shelly-Ann; Howard, Annie; Seyerle, Amanda; Avery, Christy; Chittoor, Geetha; Franceschini, Nora; Voruganti, V.; Young, Kristin; O'Connell, Jeffrey; North, Kari; Justice, Anne
• Is Part Of: BMC proceedings. Volume 10:Issue 7(2016)
• Page Start: 371
• Page End: 377
• Publication Date: 2016-10
• Subjects: Medicine -- Congresses; Biology -- Congresses; 610.5
• DOI: 10.1186/s12919-016-0058-1
• Languages: English
• ISSNs: 1753-6561

Abstract

Abstract Background Nearly half of adults in the United States who are diagnosed with hypertension use blood-pressure-lowering medications. Yet there is a large interindividual variability in the response to these medications. Two complementary gene–environment interaction methods have been published and incorporated into publicly available software packages to examine interaction effects, including whether genetic variants modify the association between medication use and blood pressure. The first approach uses a gene–environment interaction term to measure the change in outcome when both the genetic marker and medication are present (the "interaction model"). The second approach tests for effect-size differences between strata of an environmental exposure (the "med-diff" approach). However, no studies have quantitatively compared how these methods perform with respect to 1 or 2 degree of freedom (DF) tests or in family-based data sets. We evaluated these 2 approaches using simulated genotype–medication response interactions at 3 single nucleotide polymorphisms (SNPs) across a range of minor allele frequencies (MAFs 0.1–5.4 %) using the Genetic Analysis Workshop 19 family sample. Results The estimated interaction effect sizes were on average larger in the interaction model approach compared to the med-diff approach. The true positive proportion was higher for the med-diff approach for SNPs less than 1 % MAF, but higher for the interaction model when common variants were evaluated (MAF >5 %). The interaction model produced lower false-positive proportions than expected (5 %) across a range of MAFs for both the 1DF and 2DF tests. In contrast, the med-diff approach produced higher but stable false-positive proportions around 5 % across MAFs for both tests. Conclusions Although the 1DF tests both performed similarly for common variants, the interaction model estimated true interaction effects with less bias and higher true positive proportions than the med-diff approach. However, if rare variation (MAF <5 %) is of interest, our findings suggest that when convergence is achieved, the med-diff approach may estimate true interaction effects more conservatively and with less variability.