Disentangling associations between DNA methylation and blood lipids: a Mendelian randomization approach.

BACKGROUND: DNA methylation is an epigenetic mechanism that has been proposed as a possible link between genetic and environmental determinants of disease. Prior studies reported robust associations between the methylation of specific cytosine-phosphate-guanine (CpG) sites and plasma lipids, namely triglycerides (TGs) and high-density lipoprotein cholesterol (HDL-C). However, the causality of the observed association remains elusive, hampered by weak instrumental variables for methylation status. AIM: We present a novel application of the elastic net approach to implement a bidirectional Mendelian randomization approach to inferring causal relationships between candidate CpGs and plasma lipids in GAW20 data. METHODS: We used DNA methylation, TGs, and HDL-C measured during the visit 2. Based on prior findings, we selected 5 methylation markers (cg00574958, cg07504977, cg06690548, cg19693031, and cg03717755) related to TGs, 2 markers (cg09572125 and cg02650017) related to HDL-C, and 2 markers (cg06500161 and cg11024682) related to both traits. We implemented an elastic net approach to improve the selection of the genetic instrument for the methylation markers, followed by bidirectional Mendelian randomization 2-stage least-squares regression. RESULTS: We observed causal effects of blood fasting TGs on the methylation levels of cg00574958 (CPT1A) and cg06690548 (SLC7A11). For cg00574958, our findings were also consistent with the reverse direction of association, that is, from CPT1A methylation to TGs. CONCLUSIONS: Current evidence does not rule out either direction of association between the methylation of the cg00574958 CPT1A locus and plasma TGs, highlighting the complexity of lipid homeostasis. We also demonstrated a novel approach to improve instrument selection in DNA methylation studies.

Causal modeling in a multi-omic setting: insights from GAW20.

BACKGROUND: Increasingly available multilayered omics data on large populations has opened exciting analytic opportunities and posed unique challenges to robust estimation of causal effects in the setting of complex disease phenotypes. The GAW20 Causal Modeling Working Group has applied complementary approaches (eg, Mendelian randomization, structural equations modeling, Bayesian networks) to discover novel causal effects of genomic and epigenomic variation on lipid phenotypes, as well as to validate prior findings from observational studies. RESULTS: Two Mendelian randomization studies have applied novel approaches to instrumental variable selection in methylation data, identifying bidirectional causal effects of CPT1A and triglycerides, as well as of RNMT and C6orf42, on high-density lipoprotein cholesterol response to fenofibrate. The CPT1A finding also emerged in a Bayesian network study. The Mendelian randomization studies have implemented both existing and novel steps to account for pleiotropic effects, which were independently detected in the GAW20 data via a structural equation modeling approach. Two studies estimated indirect effects of genomic variation (via DNA methylation and/or correlated phenotypes) on lipid outcomes of interest. Finally, a novel weighted R2 measure was proposed to complement other causal inference efforts by controlling for the influence of outlying observations. CONCLUSIONS: The GAW20 contributions illustrate the diversity of possible approaches to causal inference in the multi-omic context, highlighting the promises and assumptions of each method and the benefits of integrating both across methods and across omics layers for the most robust and comprehensive insights into disease processes.

An epigenome-wide association study of inflammatory response to fenofibrate in the Genetics of Lipid Lowering Drugs and Diet Network.

AIM: Fenofibrate, a PPAR-α inhibitor used for treating dyslipidemia, has well-documented anti-inflammatory effects that vary between individuals. While DNA sequence variation explains some of the observed variability in response, epigenetic patterns present another promising avenue of inquiry due to the biological links between the PPAR-α pathway, homocysteine and S-adenosylmethionine - a source of methyl groups for the DNA methylation reaction. HYPOTHESIS: DNA methylation variation at baseline is associated with the inflammatory response to a short-term fenofibrate treatment. METHODS: We have conducted the first epigenome-wide study of inflammatory response to daily treatment with 160 mg of micronized fenofibrate over a 3-week period in the Genetics of Lipid Lowering Drugs and Diet Network (GOLDN, n = 750). Epigenome-wide DNA methylation was quantified on CD4+ T cells using the Illumina Infinium HumanMethylation450 array. RESULTS: We identified multiple CpG sites significantly associated with the changes in plasma concentrations of inflammatory cytokines such as high sensitivity CRP (hsCRP, 7 CpG sites), IL-2 soluble receptor (IL-2sR, one CpG site), and IL-6 (4 CpG sites). Top CpG sites mapped to KIAA1324L (p = 2.63E-10), SMPD3 (p = 2.14E-08), SYNPO2 (p = 5.00E-08), ILF3 (p = 1.04E-07), PRR3, GNL1 (p = 6.80E-09), FAM50B (p = 3.19E-08), RPTOR (p = 9.79e-07) and several intergenic regions (p < 1.03E-07). We also derived two inflammatory patterns using principal component analysis and uncovered additional epigenetic hits for each pattern before and after fenofibrate treatment. CONCLUSION: Our study provides preliminary evidence of a relationship between DNA methylation and inflammatory response to fenofibrate treatment.

Epigenetics of Lipid Phenotypes.

Dyslipidemia is a well-established risk factor for cardiovascular disease, the main cause of death worldwide. Blood lipid profiles are patterned by both genetic and environmental factors. In recent years, epigenetics has emerged as a paradigm that unifies these influences. In this review, we have summarized the latest evidence implicating epigenetic mechanisms-DNA methylation, histone modification, and regulation by RNAs-in lipid homeostasis. Key findings have emerged in a number of novel epigenetic loci located in biologically plausible genes (e.g. CPT1A, ABCG1, SREBF1, and others), as well as microRNA-33a/b. Evidence from animal and cell culture models suggests a complex interplay between different classes of epigenetic processes in the lipid-related genomic regions. While epigenetic findings hold the potential to explain the interindividual variability in lipid profiles as well as the underlying mechanisms, they have yet to be translated into effective therapies for dyslipidemia.