Investigating potential causal relationships between SNPs, DNA methylation and HDL

Lai Jiang,Karim Oualkacha,Celia M T Greenwood,Kaiqiong Zhao,Kathleen Klein,Angelo J Canty

doi:10.1186/s12919-018-0117-x

Abstract

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 (ΔHDL) using single-nucleotide polymorphisms (SNPs) as instrumental variables. Several methods were used to estimate the causal effects at CpGs of interest on ΔHDL, including a newly developed method that we call constrained instrumental variables (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 ΔHDL 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—RNMT;C18orf19 and C6orf141—as showing a causal relationship with ΔHDL.

Highlights

Individuals and families in GAW20 data participated in the National Institutes of Health (NIH)-funded Genetics of Lipid Lowering Drugs and Diet Network (GOLDN) study of the effects of lipid-lowering drugs and diet on triglycerides and other atherogenic phenotypes
Our goal was to explore the use of Mendelian randomization (MR) methods, a type of instrumental variable analysis, to try and elucidate the causal relationships between methylation and blood lipids in the GAW20 real data set
The pretreatment methylation levels and ΔHDL were adjusted for the fixed effects of the top 4PCs as well as age, sex, smoking, center, fast time, and metabolic syndrome status, and for a random effect with covariance based on the kinship matrix, to capture effects resulting from familial relationships

Summary

Introduction

Individuals and families in GAW20 data participated in the National Institutes of Health (NIH)-funded Genetics of Lipid Lowering Drugs and Diet Network (GOLDN) study of the effects of lipid-lowering drugs and diet on triglycerides and other atherogenic phenotypes. Significant associations between methylation levels and blood lipids could arise from a causal relationship, such that changes in methylation levels at a Strong SNP associations are needed for a successful MR analysis. Jiang et al BMC Proceedings 2018, 12(Suppl 9): changes in methylation levels (pre−/posttreatment) and the SNPs. here we have explored the potential causal relationships between pretreatment methylation and the HDL treatment response, that is, the changes pre−/posttreatment (ΔHDL). We can be sure that there is no reverse causation (lipid changes cannot alter pretreatment methylation) Another key assumption for MR analysis is that there is no pleiotropy, such that the SNPs are associated with the outcome (ΔHDL) only through the intermediate phenotypes (methylation). We investigate the performance of a new method that tries to account for potential pleiotropy by selecting SNPs with strong associations with the intermediate phenotype of interest, and little association with potential pleiotropic phenotypes

Objectives

Methods

Results

Conclusion