The genetic architecture of fasting plasma triglyceride response to fenofibrate treatment

Abstract

Metabolic response to the triglyceride (TG)-lowering drug, fenofibrate, is shaped by interactions between genetic and environmental factors, yet knowledge regarding the genetic determinants of this response is primarily limited to single-gene effects. Since very low-density lipoprotein (VLDL) is the central carrier of fasting TG, identifying factors that affect both total TG and VLDL-TG response to fenofibrate is critical for predicting individual fenofibrate response. As part of the Genetics of Lipid Lowering Drugs and Diet Network (GOLDN) study, 688 individuals from 161 families were genotyped for 91 single-nucleotide polymorphisms (SNPs) in 25 genes known to be involved in lipoprotein metabolism. Using generalized estimating equations to control for family structure, we performed linear modeling to investigate whether single SNPs, single covariates, SNP-SNP interactions, and/or SNP-covariate interactions had a significant association with the change in total fasting TG and fasting VLDL-TG after 3 weeks of fenofibrate treatment. A 10-iteration fourfold cross-validation procedure was used to validate significant associations and quantify their predictive abilities. More than one-third of the significant, cross-validated SNP-SNP interactions predicting each outcome involved just five SNPs, showing that these SNPs are of key importance to fenofibrate response. Multiple variable models constructed using the top-ranked SNP--covariate interactions explained 11.9% more variation in the change in TG and 7.8% more variation in the change in VLDL than baseline TG alone. These results yield insight into the complex biology of fenofibrate response, which can be used to target fenofibrate therapy to individuals who are most likely to benefit from the drug.

Figure 1

Partial F-test P-values versus CV R² for SNP–covariate and epistatic (SNP–SNP) interactions predicting ΔTG and ΔVLDL. Percent variation explained in independent test samples (CV R²) versus −log₁₀ partial F-test P-value for SNP–covariate and epistatic (SNP–SNP) interactions that were significant in ΔTG or ΔVLDL (partial F-test P-value ≤0.1). R² for the correlation between −log P-value and CV R² is 0.13. Lines are shown at −log P=0.05 and CV R²=0.5%.

Figure 2

KGraph: fasting total TG and VLDL–TG and response to fenofibrate. Using 8 regions, the KGraph shows the relationships between the SNPs, clinical covariates, and outcomes by displaying the results from tests of correlation, linkage disequilibrium, association and cross-validation. The key at the bottom of the graphic shows the test criterion for each region and the colors associated with the test result. The region number key in the lower left corner shows the location of each region, and indicates whether the results in the region were assessed using cross-validation (shaded regions). Cross-validation of a specific test result is indicated by a black bar. Results for ΔTG are displayed in the top/right half of the cell and results for ΔVLDL are displayed in the bottom/left half. Region 1 displays the association between the SNPs and the clinical covariates, region 2 displays the correlation between the clinical covariates, and region 3 displays the linkage disequilibrium between the SNPs. Region 4 displays covariate association with the outcomes (ΔTG and ΔVLDL), region 5 displays SNP association with the outcomes, region 6 displays covariate–covariate interactions predicting the outcomes, region 7 displays SNP–covariate interactions predicting the outcomes, and region 8 displays SNP–SNP (epistatic) interactions predicting the outcomes.