Alzheimer’s disease (AD) is a chronic neurodegenerative disorder characterized by progressive neuropathology and cognitive decline. We describe a cross-tissue analysis of methylomic variation in AD using samples from three independent human post-mortem brain cohorts. We identify a differentially methylated region in the ankyrin 1 (ANK1) gene that is associated with neuropathology in the entorhinal cortex, a primary site of AD manifestation. This region was confirmed as significantly hypermethylated in two other cortical regions (superior temporal gyrus and prefrontal cortex) but not in the cerebellum, a region largely protected from neurodegeneration in AD, nor whole blood obtained pre-mortem, from the same individuals. Neuropathology-associated ANK1 hypermethylation was subsequently confirmed in cortical samples from three independent brain cohorts. This study represents the first epigenome-wide association study (EWAS) of AD employing a sequential replication design across multiple tissues, and highlights the power of this approach for identifying methylomic variation associated with complex disease.
Epigenetic processes play a key role in orchestrating transcriptional regulation during development. The importance of DNA methylation in fetal brain development is highlighted by the dynamic expression of de novo DNA methyltransferases during the perinatal period and neurodevelopmental deficits associated with mutations in the methyl-CpG binding protein 2 (MECP2) gene. However, our knowledge about the temporal changes to the epigenome during fetal brain development has, to date, been limited. We quantified genome-wide patterns of DNA methylation at~400,000 sites in 179 human fetal brain samples (100 male, 79 female) spanning 23 to 184 d post-conception. We identified highly significant changes in DNA methylation across fetal brain development at >7% of sites, with an enrichment of loci becoming hypomethylated with fetal age. Sites associated with developmental changes in DNA methylation during fetal brain development were significantly underrepresented in promoter regulatory regions but significantly overrepresented in regions flanking CpG islands (shores and shelves) and gene bodies. Highly significant differences in DNA methylation were observed between males and females at a number of autosomal sites, with a small number of regions showing sex-specific DNA methylation trajectories across brain development. Weighted gene comethylation network analysis (WGCNA) revealed discrete modules of comethylated loci associated with fetal age that are significantly enriched for genes involved in neurodevelopmental processes. This is, to our knowledge, the most extensive study of DNA methylation across human fetal brain development to date, confirming the prenatal period as a time of considerable epigenomic plasticity.
DNA methylation is assumed to be complementary on both alleles across the genome, although there are exceptions, notably in regions subject to genomic imprinting. We present a genome-wide survey of the degree of allelic skewing of DNA methylation with the aim of identifying previously unreported differentially methylated regions (DMRs) associated primarily with genomic imprinting or DNA sequence variation acting in cis. We used SNP microarrays to quantitatively assess allele-specific DNA methylation (ASM) in amplicons covering 7.6% of the human genome following cleavage with a cocktail of methylation-sensitive restriction enzymes (MSREs). Selected findings were verified using bisulfite-mapping and gene-expression analyses, subsequently tested in a second tissue from the same individuals, and replicated in DNA obtained from 30 parent-child trios. Our approach detected clear examples of ASM in the vicinity of known imprinted loci, highlighting the validity of the method. In total, 2,704 (1.5%) of our 183,605 informative and stringently filtered SNPs demonstrate an average relative allele score (RAS) change > or =0.10 following MSRE digestion. In agreement with previous reports, the majority of ASM ( approximately 90%) appears to be cis in nature, and several examples of tissue-specific ASM were identified. Our data show that ASM is a widespread phenomenon, with >35,000 such sites potentially occurring across the genome, and that a spectrum of ASM is likely, with heterogeneity between individuals and across tissues. These findings impact our understanding about the origin of individual phenotypic differences and have implications for genetic studies of complex disease.
Youth with high callous-unemotional traits (CU) are at risk for early-onset and persistent conduct problems. Research suggests that there may be different developmental pathways to CU (genetic/constitutional vs environmental), and that the absence or presence of co-occurring internalizing problems is a key marker. However, it is unclear whether such a distinction is valid. Intermediate phenotypes such as DNA methylation, an epigenetic modification regulating gene expression, may help to clarify aetiological pathways. This is the first study to examine prospective inter-relationships between environmental risk (prenatal/postnatal) and DNA methylation (birth, age 7, age 9) in the prediction of CU (age 13), for youth low vs. high in internalizing problems. We focused on DNA methylation in the vicinity of the oxytocin receptor (OXTR) gene as it has been previously implicated in CU. Participants were 84 youth with early-onset and persistent conduct problems drawn from the Avon Longitudinal Study of Parents and Children. For youth with low internalizing problems (46%), we found that: (i) OXTR methylation at birth associated with higher CU (age 13) as well as decreased experience of victimization during childhood (birth – age 9), (ii) higher prenatal parental risks (maternal psychopathology, criminal behaviors, substance use) associated with higher OXTR methylation at birth, and (iii) OXTR methylation levels were more stable across time (birth – age 9). In contrast, for youth with high internalizing problems, CU was associated with prenatal risks of an interpersonal nature (i.e., intimate partner violence, family conflict) but not OXTR methylation. Findings support the existence of distinct developmental pathways to CU.
Human DNA methylation data have been used to develop biomarkers of ageing, referred to as 'epigenetic clocks', which have been widely used to identify differences between chronological age and biological age in health and disease including neurodegeneration, dementia and other brain phenotypes. Existing DNA methylation clocks have been shown to be highly accurate in blood but are less precise when used in older samples or in tissue types not included in training the model, including brain. We aimed to develop a novel epigenetic clock that performs optimally in human cortex tissue and has the potential to identify phenotypes associated with biological ageing in the brain. We generated an extensive dataset of human cortex DNA methylation data spanning the life course (n = 1397, ages = 1 to 108 years). This dataset was split into 'training' and 'testing' samples (training: n = 1047; testing: n = 350). DNA methylation age estimators were derived using a transformed version of chronological age on DNA methylation at specific sites using elastic net regression, a supervised machine learning method. The cortical clock was subsequently validated in a novel independent human cortex dataset (n = 1221, ages = 41 to 104 years) and tested for specificity in a large whole blood dataset (n = 1175, ages = 28 to 98 years). We identified a set of 347 DNA methylation sites that, in combination, optimally predict age in the human cortex. The sum of DNA methylation levels at these sites weighted by their regression coefficients provide the cortical DNA methylation clock age estimate. The novel clock dramatically outperformed previously reported clocks in additional cortical datasets. Our findings suggest that previous associations between predicted DNA methylation age and neurodegenerative phenotypes might represent false positives resulting from clocks not robustly calibrated to the tissue being tested and for phenotypes that become manifest in older ages. The age distribution and tissue type of samples included in training datasets need to be considered when building and applying epigenetic clock algorithms to human epidemiological or disease cohorts.
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