Dopaminergic gene methylation is associated with cognitive performance in a childhood monozygotic twin study

Abstract

Individual differences in cognitive function are due to a combination of heritable and non-heritable factors. A large body of evidence from clinical, cognitive, and pharmacological neuroscience implicates dopaminergic gene variants as modulators of cognitive functions. Neuroepigenetic studies demonstrate environmental factors also influence complex phenotypes by affecting gene expression regulation. To evaluate the mechanism of environmental influence on cognitive abilities, we examined if epigenetic regulation of dopaminergic genes plays a role in cognition. Using a DNA methylation profiling microarray, we used a monozygotic (MZ) twin difference design to evaluate if co-twin differences in methylation of CpG sites near six dopaminergic genes predicted differences in response inhibition and memory performance. Studying MZ twins allows us to assess if environmentally driven differences in methylation affect differences in phenotype while controlling for the influence of genotype and shared family environment. Response inhibition was assessed with the flanker task and short-term and working memory were assessed with digit span recall. We found MZ co-twin differences in DRD4 gene methylation predicted differences in short-term memory. MZ differences in COMT, DBH, DAT1, DRD1, and DRD2 gene methylation predicted differences in response inhibition. Taken together, findings suggest methylation status of dopaminergic genes may influence cognitive functions in a dissociable manner. Our results highlight the importance of the epigenome and environment, over and above the influence of genotype, in supporting complex cognitive functions.

Keywords: DNA methylation; childhood; cognition; dopamine; epigenetics; memory; response inhibition; twin.

Figure 2.

The Infinium MethylationEPIC β-values representing percent methylation were plotted against DBH CpG sites. Data points represent individuals. CpG location in relation to the gene is indicated by the data point color. The two heat map rows represent the first and second component loadings from PCA analysis.

Figure 3.

The Infinium MethylationEPIC β-values representing percent methylation were plotted against DAT1 CpG sites. Data points represent individuals. CpG location in relation to the gene is indicated by the data point colors. The two heat map rows represent the first and second component loadings from PCA analysis.

Figure 4.

The Infinium MethylationEPIC β-values representing percent methylation were plotted against DRD1 CpG sites. Data points represent individuals. CpG location in relation to the gene is indicated by the data point colors. The two heat map rows represent the first and second component loadings from PCA analysis.

Figure 5.

The Infinium MethylationEPIC β-values representing percent methylation were plotted against DRD2 CpG sites. Data points represent individuals. CpG location in relation to the gene is indicated by the data point colors. The two heat map rows represent the first and second component loadings from PCA analysis.

Figure 1.

The Infinium MethylationEPIC β-values representing percent methylation were plotted against COMT CpG sites. Data points represent individuals. CpG location in relation to the gene is indicated by the data point color. The two heat map rows represent the first and second component loadings from PCA analysis.

Figure 6.

The Infinium MethylationEPIC β-values representing percent methylation were plotted against DRD4 CpG sites. Data points represent individuals. CpG location in relation to the gene is indicated by the data point colors. The heat map row represents the component loadings from PCA analysis.