CpG-set association assessment of lipid concentration changes and DNA methylation

Investigating potential causal relationships between SNPs, DNA methylation and HDL

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.

Application of novel and existing methods to identify genes with evidence of epigenetic association: results from GAW20

BACKGROUND

The rise in popularity and accessibility of DNA methylation data to evaluate epigenetic associations with disease has led to numerous methodological questions. As part of GAW20, our working group of 8 research groups focused on gene searching methods.

RESULTS

Although the methods were varied, we identified 3 main themes within our group. First, many groups tackled the question of how best to use pedigree information in downstream analyses, finding that (a) the use of kinship matrices is common practice, (b) ascertainment corrections may be necessary, and (c) pedigree information may be useful for identifying parent-of-origin effects. Second, many groups also considered multimarker versus single-marker tests. Multimarker tests had modestly improved power versus single-marker methods on simulated data, and on real data identified additional associations that were not identified with single-marker methods, including identification of a gene with a strong biological interpretation. Finally, some of the groups explored methods to combine single-nucleotide polymorphism (SNP) and DNA methylation into a single association analysis.

CONCLUSIONS

A causal inference method showed promise at discovering new mechanisms of SNP activity; gene-based methods of summarizing SNP and DNA methylation data also showed promise. Even though numerous questions still remain in the analysis of DNA methylation data, our discussions at GAW20 suggest some emerging best practices.