On these GEITP pages, often we have chatted about what contributes to a genetic trait (phenotype). The trait can be a relatively simple Mendelian recessive or dominant disorder, or the phenoypte can be a multifactorial trait –– such as height, weight, or complex disease such as schizophrenia, type-2 diabetes, obesity, drug toxicity or efficacy, or cancer. Factors that contribute to these traits include genetics (DNA sequence), epigenetic factors, or environmental effects. Epigenetic factors include DNA methylation, RNA-interference, histone modifications, and chromatin remodeling. The present paper [attached] focuses on DNA methylation.
For more than a decade, genome-wide association studies (GWAS) have identified thousands of DNA genetic variants that are associated with human traits and diseases.
However, these identified DNA variants explain only a small portion of the trait’s variance. Epigenome-wide association studies (EWAS) are therefore being increasingly carried out in order to identify associations between epigenetic modifications and complex traits. Differential epigenetic patters have been identified for such multifactorial traits as asthma, obesity and myocardial infarction. However, epigenetic variability can be influenced by genetic variation, binding of transcription factors, or by external (environmental) causes –– such as smoking or medications.
In this study [attached], authors combined single-nucleotide variant (SNV) 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 >10 million SNVs. Authors compared EWAS and GWAS analyses, and integrated biomarker, DNA methylation, and SNV data –– using between 698 and 1033 samples. They identified 124 and 45 loci, respectively (with effect-sizes up to 0.22 standard units of change per 1% change in DNA-methylation levels, and up to four standard units of change per copy of the effective allele) in the EWAS and GWAS, respectively. Most GWAS loci were cis-regulatory (i.e. close to the gene of interest), whereas most EWAS loci were trans-regulatory (i.e. distant to the gene of interest). All EWAS signals that overlapped with a GWAS locus were driven by underlying genetic variants, and three EWAS signals were confounded by smoking.
Whereas some cis-regulatory SNVs for biomarkers appeared to have an effect also on DNA-methylation levels, cis-regulatory SNVs for DNA methylation were not observed to affect biomarker levels. Authors found associations between protein biomarker and DNA-methylation levels at numerous loci in the genome; these associations are likely to reflect underlying patterns of genetic variants, specific environmental exposures, and/or represent effects secondary to pathogenesis of disease.
PloS Genet Sept 2o17; 13: e1007005