Background: Large numbers of autosomal sites are found differentially methylated in the aging genome. Due to analytical difficulties in dealing with sex differences in X-chromosome content and X-inactivation (XCI) in females, this has not been explored for the X chromosome. Methods: Using data from middle age to elderly individuals (age 55+ years) from two Danish cohorts of monozygotic twins and the Scottish Lothian Birth Cohort 1921, we conducted an X-chromosome-wide analysis of age-associated DNA methylation patterns with consideration of stably inferred XCI status. Results: Through analysing and comparing sex-specific X-linked DNA methylation changes over age late in life, we identified 123, 293 and 55 CpG sites significant (FDR < 0.05) only in males, only in females and in both sexes of Danish twins. All findings were significantly replicated in the two Danish twin cohorts. CpG sites escaping XCI are predominantly de-methylated with increasing age across cohorts. In contrast, CpGs highly methylated in both sexes are methylated even further with increasing age. Among the replicated sites in Danish samples, 16 (13%), 24 (8.2%) and 3 (5.5%) CpGs were further validated in LBC1921 (FDR < 0.05). Conclusions: The X-chromosome of whole blood leukocytes displays age- and sex-dependent DNA methylation patterns in relation to XCI across cohorts.

Background: Gastric cancer (GC) has been considered as the 5th most common type of cancer and the third leading cause of cancer-associated death worldwide. The aim of this historical cohort study was to evaluate the survival predictors for all patients with GC using the Cox proportional hazards, extended Cox, and gamma-frailty models. Methods: This historical cohort study was performed according to documents of 1695 individuals having GC referred to three medical centers in Iran from 2001 to 2018. First, most significant prognostic risk factors on survival were selected, Cox proportional hazards, extended Cox, gamma-frailty models were applied to evaluate the effects of the risk factors, and then these models were compared with the Akaike information criterion. Results: The age of patients, body mass index (BMI), tumor size, type of treatment and grade of the tumor increased the hazard rate (HR) of GC patients in both the Cox and frailty models (P < 0.05). Also, the size of the tumor and BMI were considered as time-varying variables in the extended Cox model. Moreover, the frailty model showed that there is at least an unknown factor, genetic or environmental factors, in the model that is not measured (P < 0.05). Conclusions: Some prognostic factors, including age, tumor size, the grade of the tumor, type of treatment and BMI, were regarded as indispensable predictors in patients of GC. Frailty model revealed that there are unknown or latent factors, genetic and environmental factors, resulting in the biased estimates of the regression coefficients.

Background: Epigenetic inactivation of O⁶-methylguanine DNA-methyltransferase (MGMT) is associated with increased sensitivity to alkylating chemotherapeutic agents in glioblastoma patients. The genetic background underlying MGMT gene methylation may explain individual differences in treatment response and provide a clue to a personalized treatment strategy. Making use of the longitudinal twin design, we aimed, for the first time, to estimate the genetic contributions to MGMT methylation in a Danish twin cohort. Methods: DNA-methylation from whole blood (18 monozygotic (MZ) and 25 dizygotic (DZ) twin pairs) repeated 10 years apart from the Longitudinal Study of Aging Danish Twins (LSADT) were used to search for genetic and environmental contributions to DNA-methylation at 170 CpG sites of across the MGMT gene. Both univariate and bivariate twin models were applied. The intraclass correlations, performed on cross-sectional data (246 MZ twin pairs) from an independent study population, the Middle-Aged Danish Twins (MADT), were used to assess the genetic influence at each CpG site of MGMT for replication. Results: Univariate twin model revealed twelve CpG sites showing significantly high heritability at intake (wave 1, h² > 0.43), and seven CpG sites with significant heritability estimates at end of follow-up (wave 2, h² > 0.5). There were six significant CpG sites, located at the gene body region, that overlapped among the two waves (h² > 0.5), of which five remained significant in the bivariate twin model, which was applied to both waves. Within MZ pair correlation in these six CpGs from MADT demarks top level of genetic influence. There were 11 CpGs constantly have substantial common environmental component over the 10 years. Conclusions: We have identified 6 CpG sites linked to the MGMT gene with strong and persistent genetic control based on their DNA methylation levels. The genetic basis of MGMT gene methylation could help to explain individual differences in glioblastoma treatment response and most importantly, provide references for mapping the methylation Quantitative Trait Loci (meQTL) underlying the genetic regulation.

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.

Aim: Many efforts have been deployed to identify genetic variants associated with BMI. Alternatively, we explore epigenetic contribution to BMI variation by focusing on long noncoding RNAs (lncRNAs...