The relative contribution of DNA methylation and genetic variants on protein biomarkers for human diseases

Abstract

Associations between epigenetic alterations and disease status have been identified for many diseases. However, there is no strong evidence that epigenetic alterations are directly causal for disease pathogenesis. In this study, we combined SNP and DNA methylation data with measurements of protein biomarkers for cancer, inflammation or cardiovascular disease, to investigate the relative contribution of genetic and epigenetic variation on biomarker levels. A total of 121 protein biomarkers were measured and analyzed in relation to DNA methylation at 470,000 genomic positions and to over 10 million SNPs. We performed epigenome-wide association study (EWAS) and genome-wide association study (GWAS) analyses, and integrated biomarker, DNA methylation and SNP data using between 698 and 1033 samples depending on data availability for the different analyses. We identified 124 and 45 loci (Bonferroni adjusted P < 0.05) with effect sizes up to 0.22 standard units’ change per 1% change in DNA methylation levels and up to four standard units’ change per copy of the effective allele in the EWAS and GWAS respectively. Most GWAS loci were cis-regulatory whereas most EWAS loci were located in trans. Eleven EWAS loci were associated with multiple biomarkers, including one in NLRC5 associated with CXCL11, CXCL9, IL-12, and IL-18 levels. All EWAS signals that overlapped with a GWAS locus were driven by underlying genetic variants and three EWAS signals were confounded by smoking. While some cis-regulatory SNPs for biomarkers appeared to have an effect also on DNA methylation levels, cis-regulatory SNPs for DNA methylation were not observed to affect biomarker levels. We present associations between protein biomarker and DNA methylation levels at numerous loci in the genome. The associations are likely to reflect the underlying pattern of genetic variants, specific environmental exposures, or represent secondary effects to the pathogenesis of disease.

The role of genomic vs. epigenomic variation in shaping patterns of convergent transcriptomic variation across continents in a young species complex

“…Similar qualitative and quantitative observations of a lower contribution of DNA methylation compared to genetic variation were made in previous studies. For example, Ahsan et al investigated the relative contribution of DNA methylation and genetic variants on biomarkers of human diseases, and found that for 36% (44/121) of the studied proteins the abundance variation relied on DNA methylation bases while 52% (23/44) of these were associated to genetic variants (i.e., interaction between DNA methylation and genetic) [76]. Moreover, studies focusing on the bases of adaptive traits in Arabidopsis thaliana and combining both DNA methylation and genomic, highlighted that DNA methylation variation was less likely to contribute to the adaptive traits than standing genetic variation [64,77].…”

Systematic evaluation of the causal relationship between DNA methylation and C-reactive protein

“…Contrary to this report, Ahsan et al (17) investigated the interplay between DNA methylation and 121 protein biomarkers for inflammation, cancer and cardiovascular disease (although not CRP). They found that for 22% (i.e., n=27) of the studied proteins, biomarker levels appeared to influence DNA methylation, rather than the reserve.…”

Integrative omics approach to identify the molecular architecture of inflammatory protein levels in healthy older adults

“…We also found altered methylation at the NLRC5 locus (NOD-like receptor family CARD domain containing 5) is associated with circulating CXCL9 levels. NRLC5 acts as a potent regulator of the inflammasome (40,41). Zaghlool et al showed that altered methylation at the NLRC5 locus associates with several inflammatory markers, including CXCL10 and CXCL11, with pathway analyses linking it to disease states in which NLRC5 dysfunction is implicated such as cancer and cardiovascular disease (9).…”

“…Zaghlool et al performed an epigenome-wide association study (EWAS) of 1123 proteins, which pointed towards networks of chronic low-grade inflammatory biomarkers (n = 944 individuals) (9). In an integrative approach, Ahsan et al identified epigenetic, as well as genetic, markers associated with protein biomarkers including inflammatory mediators (n ≤ 1,033 individuals) (10). Notably, most studies examining the molecular architecture of human traits have relied on linear model-based methods which examine marker or probe effects marginally (11,12).…”