Triglyceride (TG) concentrations decrease in response to fenofibrate treatment, and also are associated with DNA methylation. But how interactions between fenofibrate response and DNA methylation ...affect TGs remains unclear.
In the present study, we identified and compared differential methylation sites associated with TG concentrations in individuals before and after fenofibrate treatment. We then estimated interactions between fenofibrate treatment and methylation to identify differential methylation effects associated with fenofibrate treatment on TG concentrations using the entire longitudinal family sample. To account for within-family and within-individual corrections, the generalized estimating equations approach was used to estimate main and interaction effects between methylation sites and fenofibrate treatment, adjusting for potential confounders. Analyses were also performed with and without adjusting for high-density lipoprotein (HDL) concentrations.
Prior to fenofibrate treatment, 23 cytosine-phosphate-guanine (CpG) sites were significantly associated with TG concentrations, while only 13 CpG sites were identified posttreatment, adjusting for HDL. Without adjusting for HDL, pretreatment, 20 CpG sites were significantly associated with TG concentrations, while only 12 CpG sites were identified posttreatment. Among these sites, only one differential site (cg19003390 in the
gene) overlapped from pre- and posttreatment measurements regardless of HDL adjustment. Furthermore, 11 methylation sites showed substantial interaction effects (
< 1.43 × 10
with Bonferroni correction) with or without HDL adjustment when using the whole longitudinal data.
Our analyses suggest that DNA methylation likely modified the effect of fenofibrate on TG concentrations. Differential fenofibrate-associated methylation sites on TGs differed with and without adjusting for HDL concentrations, suggesting that these HDLs and TGs might share some common epigenetic processes.
Using the real data set from GAW20, we examined changes in the distribution of DNA methylation before and after treatment. Paired analysis of differences in both mean and variance had grossly ...inflated type 1 error, suggesting either a very large number of changes across the entire epigenome or major non-biological issues, such as batch effects. Separate analysis of Infinium I and II probes indicated differences in the paired
-test statistics between these two types of probes. Examination of combined principal components showed that the first and fourth principal components discriminate between the before and after treatment measurements, further evidencing the presence of batch effects that make any conclusions about treatment effect suspect.
Bayesian networks have been proposed as a way to identify possible causal relationships between measured variables based on their conditional dependencies and independencies. We explored the use of ...Bayesian network analyses applied to the GAW20 data to identify possible causal relationships between differential methylation of cytosine-phosphate-guanine dinucleotides (CpGs), single-nucleotide polymorphisms (SNPs), and blood lipid trait (triglycerides TGs).
After initial exploratory linear regression analyses, 2 Bayesian networks analyses were performed. First, we used the real data and modeled the effects of 4 CpGs previously found to be associated with TGs in the Genetics of Lipid Lowering Drugs and Diet Network Study (GOLDN). Second, we used the simulated data and modeled the effect of a fictional lipid modifying drug with 5 known causal SNPs and 5 corresponding CpGs.
In the real data we show that relationships are present between the CpGs, TGs, and other variables-age, sex, and center. In the simulated data, we show, using linear regression, that no CpGs and only 1 SNP were associated with a change in TG levels, and, using Bayesian network analysis, that relationships are present between the change in TG levels and most SNPs, but not with CpGs.
Even when the causal relationships between variables are known, as with the simulated data, if the relationships are not strong then it is challenging to reproduce them in a Bayesian network.
Obesity is a risk factor for heart disease, stroke, diabetes, high blood pressure, and other chronic diseases. Some drugs, including fenofibrate, are used to treat obesity or excessive weight by ...lowering the level of specific triglycerides. However, different groups have different drug sensitivities and, consequently, there are differences in drug effects. In this study, we assessed both genetic and nongenetic factors that influence drug responses and stratified patients into groups based on differential drug effect and sensitivity. Our methodology of investigating genetic factors and nongenetic factors is applicable to studying differential effects of other drugs, such as statins, and provides an approach to the development of personalized medicine.
As part of GAW20, we analyzed the familiality and variability of methylation to identify cytosine-phosphate-guanine (CpG) sites responsive to treatment with fenofibrate. Methylation was measured at ...approximately 450,000 sites in pedigree members, prior to and after 3 weeks of treatment. Initially, we aimed to identify responsive sites by analyzing the pre- and posttreatment methylation changes within individuals, but these data exhibited a confounding treatment/batch effect. We applied an alternative indirect approach by searching for CpG sites whose methylation levels exhibit a genetic response to the drug. We reasoned that these sites would exhibit highly familial and variable methylation levels posttreatment, but not pretreatment. Using a 0.1% threshold, posttreatment sibling correlation (scor) and standard deviation (SD) distributions share 16 outliers, while the corresponding pretreatment distributions share none. Comparing the pre- and posttreatment CpG outliers, 36 (8%) of SD distributions, and 449/450 (nearly 100%) of scor distributions differ. Combined, these results identify methylation sites within the
and
genes. Each gene also has a highly significant methylation quantitative trait locus (meQTL) (
< 1e-200;
< 3e-248), indicating that methylation levels at these CpG sites are driven by meQTL and fenofibrate.
Using data on 680 patients from the GAW20 real data set, we conducted Mendelian randomization (MR) studies to explore the causal relationships between methylation levels at selected probes ...(cytosine-phosphate-guanine sites CpGs) and high-density lipoprotein (HDL) changes (Δ
) using single-nucleotide polymorphisms (SNPs) as instrumental variables. Several methods were used to estimate the causal effects at CpGs of interest on Δ
, including a newly developed method that we call
(CIV). CIV performs automatic SNP selection while providing estimates of causal effects adjusted for possible pleiotropy, when the potentially-pleiotropic phenotypes are measured. For CpGs in or near the 10 genes identified as associated with Δ
using a family-based VC-score test, we compared CIV to Egger regression and the two-stage least squares (TSLS) method. All 3 approaches selected at least 1CpG in 2 genes-
and
-as showing a causal relationship with Δ
.
GAW20 provided a platform for developing and evaluating statistical methods to analyze human lipid-related phenotypes, DNA methylation, and single-nucleotide markers in a study involving a ...pharmaceutical intervention. In this article, we present an overview of the data sets and the contributions analyzing these data. The data, donated by the Genetics of Lipid Lowering Drugs and Diet Network (GOLDN) investigators, included data from 188 families (
= 1105) which included genome-wide DNA methylation data before and after a 3-week treatment with fenofibrate, single-nucleotide polymorphisms, metabolic syndrome components before and after treatment, and a variety of covariates. The contributions from individual research groups were extensively discussed prior, during, and after the Workshop in groups based on discussion themes, before being submitted for publication.
To examine whether single-nucleotide polymorphism (SNP) by methylation interactions can be detected, we analyzed GAW20 simulated triglycerides at visits 3 and 4 against baseline (visits 1 and 2) ...under 4 general linear models and 2 tree-based models in 200 replications of a sample of 680 individuals. Effects for SNPs, methylation cytosine-phosphate-guanine (CpG) effects, and interactions for SNP/CpG pairs were included. Causative SNPs/CpG pairs distributed on autosomal chromosomes 1 to 20 were tested to examine sensitivity. We also tested noncausative SNP/CpG pairs on chromosomes 21 and 22 to estimate the empirical null. We found reasonable power to detect the main causative loci, with the exact power depending on sample size and strength of effects at the SNP and CpG sites.
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