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. Author summary Every human being is unique, each with an individual risk of developing disease. Variation between humans can be attributed to genetic variation or the environment to which we are exposed. However, phenotypic differences can also be due to the epigenetic pattern of our genes, which refers to chemical modification that affects the activity of our genes. Epigenetic factors have recently gained a lot of interest, and aberrant epigenetic patterns, have been linked to many diseases. In the present study, we have identified genetic and epigenetic factors that influence the activity of our genes, by way of comparing to the level of different proteins that our genes encode, proteins that have previously been associated with human diseases such as cardiovascular diseases or cancer. We could clearly see that the epigenetic pattern was associated with the expression of specific genes but that such associations were mainly caused by genetic variants, or environmental factors influencing both epigenetic and expression patterns. However, it appeared that some disease-associated proteins could have altering effects on the epigenetic pattern, then suggesting that some epigenetic alterations represent secondary effects to the pathogenesis of disease.
【초록키워드】 Inflammation, Biomarker, Biomarkers, Diseases, Pathogenesis, Cancer, Human, Variation, Genome, Genetic, SNPs, risk, cardiovascular disease, cardiovascular diseases, smoking, Protein, Genetic variation, Genome-wide association study, SNP, CXCL9, genetic variants, Genetic variant, DNA methylation, GWAS, disease, expression, Epigenetic, association, Evidence, disease pathogenesis, Factor, effect sizes, locus, human disease, human diseases, environmental factors, alteration, disease status, copy, trans, allele, effect size, loci, IL-12, phenotypic, expression patterns, Modification, CXCL11, DNA methylation levels, NLRC5, while, MOST, IL-18, Effect, Affect, Genes, environmental factor, effective, analyzed, performed, caused, adjusted, unique, analyses, driven by, were measured, disease-associated, DNA methylation level, genomic position, overlapped, 【제목키워드】 Biomarker, Protein, Genetic variant, human disease,