Abstract
BackgroundGAW20 working group 5 brought together researchers who contributed 7 papers with the aim of evaluating methods to detect genetic by epigenetic interactions. GAW20 distributed real data from the Genetics of Lipid Lowering Drugs and Diet Network (GOLDN) study, including single-nucleotide polymorphism (SNP) markers, methylation (cytosine-phosphate-guanine [CpG]) markers, and phenotype information on up to 995 individuals. In addition, a simulated data set based on the real data was provided.ResultsThe 7 contributed papers analyzed these data sets with a number of different statistical methods, including generalized linear mixed models, mediation analysis, machine learning, W-test, and sparsity-inducing regularized regression. These methods generally appeared to perform well. Several papers confirmed a number of causative SNPs in either the large number of simulation sets or the real data on chromosome 11. Findings were also reported for different SNPs, CpG sites, and SNP–CpG site interaction pairs.ConclusionsIn the simulation (200 replications), power appeared generally good for large interaction effects, but smaller effects will require larger studies or consortium collaboration for realizing a sufficient power.
Highlights
GAW20 working group 5 brought together researchers who contributed 7 papers with the aim of evaluating methods to detect genetic by epigenetic interactions
The real data set consisted of phenotypes of high-density lipoprotein cholesterol, triglyceride (TG) levels, and metabolic syndrome diagnosis, before and after treatment with fenofibrate, genome-wide methylation pre- and posttreatment, and dense genome-wide single-nucleotide polymorphisms (SNPs) from the GOLDN study
Four models were used: model 1.a included the intercept, the main effects, CpG sites and SNPs, and their interaction effect on TG post– medication-treatment plus a vector of covariates; model 1.b included only the interaction effect on TG post–medication-treatment plus the vector of covariates; model 2.a included as the response variable the change of TG including the main effects, SNP and the methylation difference between visits 4 and 2, their interaction and covariates; model 2.b was a reduced version of model 2.a that included only interactions
Summary
GAW20 working group 5 brought together researchers who contributed 7 papers with the aim of evaluating methods to detect genetic by epigenetic interactions. Genes do not act in isolation: the expression of gene-coded proteins is affected by many factors, including other genes, environment, and epigenetics. These interactions can complicate the identification of genes related to a particular trait or disease because some genes with an important effect through interaction can have a small marginal effect. The expectation is that individuals with higher levels of methylation at a site will have a lower level of gene expression for that site Incorporating this expectation into a statistical analysis can simplify it, as de Andrade et al BMC Genetics 2018, 19(Suppl 1): compared to, for example, an analysis of an environmental factor for which the direction and mechanism of the effect are completely unknown. The papers included in this group represent a wide range of approaches facilitated by the current understanding of gene by epigenetic interaction
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