Gene-methylation interactions: Discovering region-wise DNA methylation levels that modify SNP-associated disease risk

Abstract

The genetic code is tightly linked to epigenetic instructions as to what genes to express, and when and where to express them. The most studied epigenetic mark is DNA methylation at CpG dinucleotides. Today's technology enables a rapid assessment of DNA sequence and methylation levels at a single-site resolution for hundreds of thousands of sites in the human genome, in thousands of individuals at a time. Recent years have seen a rapid increase in epigenome-wide association studies (EWAS) searching for the causes of risk for genetic diseases that previous genome-wide association studies (GWAS) could not pinpoint. However, those single-omics data analyses led to even more questions and it has become clear that only by integrating data one can get closer to answers. Here, we propose two new methods within genetic association analyses that treat the level of DNA methylation at a given CpG site as environmental exposure. Our analyses search for statistical interactions between a given allele and DNA methylation (G×M e), and between a parent-of-origin effect and DNA methylation (PoO×Me). The new methods were implemented in the R package Haplin and were tested on a dataset comprising genotype data from mother-father-child triadsm with DNA methylation data from the children only. The phenotype here was orofacial clefts (OFC), a relatively common birth defect in humans, which is known to have a genetic origin and an environmental component possibly mediated by DNA methylation. We found no significant PoO×Me interactions and a few significant G×Me interactions. Our results show that the significance of these interaction effects depends on the genomic region in which the CpGs reside and on the number of strata of methylation level. We demonstrate that, by including the methylation level around the SNP in the analyses, the estimated relative risk of OFC can change significantly. We also discuss the importance of including control data in such analyses. The new methods will be of value for all the researchers who want to explore genome-and epigenome-wide datasets in an integrative manner. Moreover, thanks to the implementation in a popular R package, the methods are easily accessible and enable fast scans of the genome-and epigenome-wide datasets.

THI Modulation of Genetic and Non-genetic Variance Components for Carcass Traits in Hanwoo Cattle

“…Residual variance heterogeneity may be due to outliers, scale effects or non-normality of data, which can be remedied by standard quality control including normalization (e.g., rank-based inverse normal or log transformation) ( Box and Hill, 1974 ; Downs and Rocke, 1979 ; Atkinson et al, 2016 ). However, residual can include some effects, which are not captured in the model, but have biological functions, such as the effects of gene-expression, methylation or unrecorded environmental factors ( Soto-Ramírez et al, 2013 ; Romanowska et al, 2019 ). Such residual effects can be modulated by THI, which is referred to as residual-by-environment (R×E) interaction in this study.…”

Susceptibility to hypertension based on MTHFR rs1801133 single nucleotide polymorphism and MTHFR promoter methylation

“…As an integrated functional genomics approach, epigenetics could delineate some molecular mechanisms behind human diseases (63). However, knowledge about genetic and epigenetic events involved in disease susceptibility is scarce (36). In the current study, the risk of hypertension was significantly higher among individuals with the TT genotype compared to the CC genotype.…”

“…Since DNA methylation also affects hypertension (2), gene-environment interactions could account for the remaining heritability (3). Moreover, since the genome and epigenome interwind (36), combining the genetic and epigenetic biomarkers could improve risk stratification and identify potential targets for pharmacological and lifestyle interventions (32,37,38).…”

Interaction between a diabetes-related methylation site (TXNIP cg19693031) and variant (GLUT1 rs841853) on fasting blood glucose levels among non-diabetics

“…Hence, the identification of variants and biomarkers specific to certain diseases could be helpful in targeted therapy [ 53 ]. The genome intertwines with the epigenome [ 54 ] and there is a high probability that genomic variations cause diseases by affecting DNA methylation [ 55 ]. Therefore, the integration of genetic and methylation data could expand our understanding of disease etiology and prognosis.…”

“…Therefore, the integration of genetic and methylation data could expand our understanding of disease etiology and prognosis. However, this area of research is lagging [ 54 ]. To our knowledge, no study has investigated the joint effect of genetic and epigenetic factors on diabetes and or FBG.…”