No abstract

DNA methylation regulates many cellular processes, including embryonic development, transcription, chromatin structure, X-chromosome inactivation, genomic imprinting and chromosome stability. DNA methyltransferases establish and maintain the presence of 5-methylcytosine (5mC), and ten-eleven translocation cytosine dioxygenases (TETs) oxidise 5mC to 5-hydroxymethylcytosine (5hmC), 5-formylcytosine (5fC) and 5-carboxylcytosine (5caC), which can be removed by base excision repair (BER) proteins. Multiple forms of DNA methylation are recognised by methyl-CpG binding proteins (MeCPs), which play vital roles in chromatin-based transcriptional regulation, DNA repair and replication. Accordingly, defects in DNA methylation and its mediators may cause silencing of tumour suppressor genes and misregulation of multiple cell cycles, DNA repair and chromosome stability genes, and hence contribute to genome instability in various human diseases, including cancer. Thus, understanding functional genetic mutations and aberrant expression of these DNA methylation mediators is critical to deciphering the crosstalk between concurrent genetic and epigenetic alterations in specific cancer types and to the development of new therapeutic strategies.

Recent research has revealed loci that display variance heterogeneity through various means such as biological disruption, linkage disequilibrium (LD), gene-by-gene (GxG), or gene-by-environment (GxE) interaction. We propose a versatile likelihood ratio test that allows joint testing for mean and variance heterogeneity (LRTMV) or either effect alone (LRTM or LRTV) in the presence of covariates. Using extensive simulations for our method and others we found that all parametric tests were sensitive to non-normality regardless of any trait transformations. Coupling our test with the parametric bootstrap solves this issue. Using simulations and empirical data from a known mean-only functional variant we demonstrate how linkage disequilibrium (LD) can produce variance-heterogeneity loci (vQTL) in a predictable fashion based on differential allele frequencies, high D’ and relatively low r2 values. We propose that a joint test for mean and variance heterogeneity is more powerful than a variance only test for detecting vQTL. This takes advantage of loci that also have mean effects without sacrificing much power to detect variance only effects. We discuss using vQTL as an approach to detect gene-by-gene interactions and also how vQTL are related to relationship loci (rQTL) and how both can create prior hypothesis for each other and reveal the relationships between traits and possibly between components of a composite trait.