Quantitative trait loci, GxE and GxG for glycemic traits: response to metformin and placebo in the Diabetes Prevention Program (DPP)

The complex genetic architecture of type-2-diabetes (T2D) includes gene-by-environment (GxE) and gene-by-gene (GxG) interactions. To identify GxE and GxG, we screened markers for patterns indicative of interactions (relationship loci [rQTL] and variance heterogeneity loci [vQTL]). rQTL exist when the correlation between multiple traits varies by genotype and vQTL occur when the variance of a trait differs by genotype (potentially flagging GxG and GxE). In the metformin and placebo arms of the DPP (n = 1762) we screened 280,965 exomic and intergenic SNPs, for rQTL and vQTL patterns in association with year one changes from baseline in glycemia and related traits (insulinogenic index [IGI], insulin sensitivity index [ISI], fasting glucose and fasting insulin). Significant (p < 1.8 x 10(-7)) rQTL and vQTL generated a priori hypotheses of individual GxE tests for a SNP x metformin treatment interaction and secondarily for GxG screens. Several rQTL and vQTL identified led to 6 nominally significant (p < 0.05) metformin treatment x SNP interactions (4 for IGI, one insulin, and one glucose) and 12GxG interactions (all IGI) that exceeded experiment-wide significance (p < 4.1 x 10(-9)). Some loci are directly associated with incident diabetes, and others are rQTL and modify a trait's relationship with diabetes (2 diabetes/glucose, 2 diabetes/insulin, 1 diabetes/IGI). rs3197999, an ISI/insulin rQTL, is a possible gene damaging missense mutation in MST1, a gene affecting beta-cell apoptosis and insulin secretion. This rQTL may link MST1 with insulin sensitivity where ISI and insulin responses differentially vary by genotype. This study demonstrates evidence for context-dependent effects (GxG & GxE) and the complexity of these T2D-related traits.

Automated summary

The complex genetic architecture of type-2 diabetes includes gene-by-environment and gene-by-gene interactions. By screening SNPs, we identified patterns associated with diabetes and explored the interactions with metformin treatment and gene variations, particularly related to IGI.