Abstract
Privileged by rapid increase in available epigenomic data, epigenome‐wide association studies (EWAS) are to make a profound contribution to understand the molecular mechanism of DNA methylation in cognitive aging. Current statistical methods used in EWAS are dominated by models based on multiple assumptions, for example, linear relationship between molecular profiles and phenotype, normal distribution for the methylation data and phenotype. In this study, we applied an assumption‐free method, the generalized correlation coefficient (GCC), and compare it to linear models, namely the linear mixed model and kinship model. We use DNA methylation associated with a cognitive score in 400 and 206 twins as discovery and replication samples respectively. DNA methylation associated with cognitive function using GCC, linear mixed model, and kinship model, identified 65 CpGs (p < 1e‐04) from discovery sample displaying both nonlinear and linear correlations. Replication analysis successfully replicated 9 of these top CpGs. When combining results of GCC and linear models to cover diverse patterns of relationships, we identified genes like KLHDC4, PAPSS2, and MRPS18B as well as pathways including focal adhesion, axon guidance, and some neurological signaling. Genomic region‐based analysis found 15 methylated regions harboring 11 genes, with three verified in gene expression analysis, also the 11 genes were related to top functional clusters including neurohypophyseal hormone and maternal aggressive behaviors. The GCC approach detects valuable methylation sites missed by traditional linear models. A combination of methylation markers from GCC and linear models enriched biological pathways sensible in neurological function that could implicate cognitive performance and cognitive aging.
Highlights
Cognitive impairment refers to when a person has trouble remembering, learning new things, concentrating, or making decisions that can affect one's everyday life
This was done by the linear mixed-effect (LME) (Bates et al, 2014) model with cognitive function as outcome and age, sex, cell counts as fixed effects, and twin pairing as a random factor
This study of DNA methylation data in twin samples indicates the strength of generalized correlation coefficient (GCC) in comparison with the traditional methods characterized by (1) ability to capture nonlinear correlation patterns missed by the linear models, (2) robustness in handling epigenome-wide association studies (EWAS) data on related samples such as twins, and (3) biologically meaningful annotations of identified markers
Summary
Cognitive impairment refers to when a person has trouble remembering, learning new things, concentrating, or making decisions that can affect one's everyday life. The level of cognitive functioning is shown to decrease with age accompanied by increased variability in twin pairs (McCartney et al, 1990). Epigenetic modification such as DNA methylation is a promising marker in understanding many age-related phenotypes (Bakulski & Fallin, 2014). Despite the wide-spread performance of epigenetic association studies, limited markers have been detected. The limitation in marker detection might have different reasons such as the distribution of the phenotype of interest which is not always normal and the complex patterns of relationship between epigenetic markers and cognition, possibly involving any non-random patterns not limited to linearity. The multiple assumptions in the conventional statistical models could be responsible for the low replication and limited explanation in the phenotype variation by the identified markers
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