Age-associated DNA Methylation Changes Research Articles

Unveiling the molecular landscape of cognitive aging: insights from polygenic risk scores, DNA methylation, and gene expression

BackgroundAging represents a significant risk factor for the occurrence of cerebral small vessel disease, associated with white matter (WM) lesions, and to age-related cognitive alterations, though the precise mechanisms remain largely unknown. This study aimed to investigate the impact of polygenic risk scores (PRS) for WM integrity, together with age-related DNA methylation, and gene expression alterations, on cognitive aging in a cross-sectional healthy aging cohort. The PRSs were calculated using genome-wide association study (GWAS) summary statistics for magnetic resonance imaging (MRI) markers of WM integrity, including WM hyperintensities, fractional anisotropy (FA), and mean diffusivity (MD). These scores were utilized to predict age-related cognitive changes and evaluate their correlation with structural brain changes, which distinguish individuals with higher and lower cognitive scores. To reduce the dimensionality of the data and identify age-related DNA methylation and transcriptomic alterations, Sparse Partial Least Squares-Discriminant Analysis (sPLS-DA) was used. Subsequently, a canonical correlation algorithm was used to integrate the three types of omics data (PRS, DNA methylation, and gene expression data) and identify an individual “omics” signature that distinguishes subjects with varying cognitive profiles.ResultsWe found a positive association between MD-PRS and long-term memory, as well as a correlation between MD-PRS and structural brain changes, effectively discriminating between individuals with lower and higher memory scores. Furthermore, we observed an enrichment of polygenic signals in genes related to both vascular and non-vascular factors. Age-related alterations in DNA methylation and gene expression indicated dysregulation of critical molecular features and signaling pathways involved in aging and lifespan regulation. The integration of multi-omics data underscored the involvement of synaptic dysfunction, axonal degeneration, microtubule organization, and glycosylation in the process of cognitive aging.ConclusionsThese findings provide valuable insights into the biological mechanisms underlying the association between WM coherence and cognitive aging. Additionally, they highlight how age-associated DNA methylation and gene expression changes contribute to cognitive aging.

Quantifying the stochastic component of epigenetic aging

DNA methylation clocks can accurately estimate chronological age and, to some extent, also biological age, yet the process by which age-associated DNA methylation (DNAm) changes are acquired appears to be quasi-stochastic, raising a fundamental question: how much of an epigenetic clock’s predictive accuracy could be explained by a stochastic process of DNAm change? Here, using DNAm data from sorted immune cells, we build realistic simulation models, subsequently demonstrating in over 22,770 sorted and whole-blood samples from 25 independent cohorts that approximately 66–75% of the accuracy underpinning Horvath’s clock could be driven by a stochastic process. This fraction increases to 90% for the more accurate Zhang’s clock, but is lower (63%) for the PhenoAge clock, suggesting that biological aging is reflected by nonstochastic processes. Confirming this, we demonstrate that Horvath’s age acceleration in males and PhenoAge’s age acceleration in severe coronavirus disease 2019 cases and smokers are not driven by an increased rate of stochastic change but by nonstochastic processes. These results significantly deepen our understanding and interpretation of epigenetic clocks.

Tracking single-cell evolution using clock-like chromatin accessibility loci

AbstractSingle-cell chromatin accessibility sequencing (scATAC-seq) reconstructs developmental trajectory by phenotypic similarity. However, inferring the exact developmental trajectory is challenging. Previous studies showed age-associated DNA methylation (DNAm) changes in specific genomic regions, termed clock-like differential methylation loci (ClockDML). Age-associated DNAm could either result from or result in chromatin accessibility changes at ClockDML. As cells undergo mitosis, the heterogeneity of chromatin accessibility on clock-like loci is reduced, providing a measure of mitotic age. In this study, we developed a method, called EpiTrace, that counts the fraction of opened clock-like loci from scATAC-seq data to determine cell age and perform lineage tracing in various cell lineages and animal species. It shows concordance with known developmental hierarchies, correlates well with DNAm-based clocks and is complementary with mutation-based lineage tracing, RNA velocity and stemness predictions. Applying EpiTrace to scATAC-seq data reveals biological insights with clinically relevant implications, ranging from hematopoiesis, organ development, tumor biology and immunity to cortical gyrification.

On epigenetic stochasticity, entropy and cancer risk.

Epigenetic changes are known to accrue in normal cells as a result of ageing and cumulative exposure to cancer risk factors. Increasing evidence points towards age-related epigenetic changes being acquired in a quasi-stochastic manner, and that they may play a causal role in cancer development. Here, I describe the quasi-stochastic nature of DNA methylation (DNAm) changes in ageing cells as well as in normal cells at risk of neoplastic transformation, discussing the implications of this stochasticity for developing cancer risk prediction strategies, and in particular, how it may require a conceptual paradigm shift in how we select cancer risk markers. I also describe the mounting evidence that a significant proportion of DNAm changes in ageing and cancer development are related to cell proliferation, reflecting tissue-turnover and the opportunity this offers for predicting cancer risk via the development of epigenetic mitotic-like clocks. Finally, I describe how age-associated DNAm changes may be causally implicated in cancer development via an irreversible suppression of tissue-specific transcription factors that increases epigenetic and transcriptomic entropy, promoting a more plastic yet aberrant cancer stem-cell state. This article is part of a discussion meeting issue 'Causes and consequences of stochastic processes in development and disease'.

Age-associated DNA methylation changes with hypermethylation ELOVL2 and ZYG11A in dental samples: A pilot study

Age-associated DNA methylation changes with hypermethylation ELOVL2 and ZYG11A in dental samples: A pilot study

Exponential dynamics of DNA methylation with age

The association of DNA methylation with age has been extensively studied. Previous work has investigated the trajectories of methylation with age, and developed predictive biomarkers of age. However, we still have a limited understanding of the functional form of methylation-age dynamics. To address this we present a theoretical framework to model the dynamics of DNA methylation at single sites. We show that this model leads to convergence to a steady-state methylation level at an exponential rate. By fitting the model to a dataset that measures changes in DNA methylation in the brain from birth to old age, we show that the timescales of this exponential convergence are heterogeneous across sites. To model this heterogeneity we generated a simulation of CpG Methylation changes with time and investigated the functional form of the dynamics of methylation with age under the empirical distribution of timescales estimated from the dataset. The resulting dynamics of the average methylation of the system were characterized and were found to closely follow an exponential trajectory. We conclude that DNA methylation can be modeled as a system that starts out of equilibrium at birth and approaches equilibrium with age in an exponential fashion. These insights illustrate the importance of accounting for nonlinear dynamics when utilizing age associated DNA methylation changes for constructing biomarkers of aging. Thus DNA methylation, along with the exponentially increasing risk of mortality with age, further establishes the exponential nature of aging.

EVALUATING STOCHASTIC EPIGENETIC MUTATIONS: RELIABILITY, SUBTYPES, AND AN ALTERNATIVE DETECTION APPROACH

Abstract Stochastic Epigenetic Mutations (SEMs) have emerged as promising biomarkers for aging and age-related disorders. Conceptually, a SEM is defined as an outlier in DNA methylation (DNAme) value at a specific genomic site, compared to a given population or cohort. These mutations are seldom shared among subjects, emphasizing their potential to capture the heterogeneity of age-associated DNAme changes, and their cumulative count in individuals rises with age. We hypothesized that the original statistical method for SEM detection is not well suited for DNAme data and demonstrated its reliability constraints at both the individual and aggregate levels, using technical replicates from whole blood samples. In response to these challenges, we explored the factors influencing individual SEM reliability and identified best practices for SEM detection. We also developed an alternative machine learning-based detection method. Importantly, our classifier demonstrated improvements in the proportion of shared SEM and ICC scores when tested in an independent dataset with technical replicates, maintaining the associations of higher SEM loads with age, mortality, and cardiovascular disorders in the FHS study cohort. To support the wider application of our findings and address the need for SEM analysis tools, we have introduced a novel R package, SEMdetectR. Leveraging parallel programming, this package offers rapid SEM identification using both the original and new methods, accompanied by robust downstream analysis features.

Age estimation in fishes using epigenetic clocks: Applications to fisheries management and conservation biology

The distribution of age classes is a key demographic parameter of populations and thus proper age estimation is crucial for fisheries management and for conservation biology. Age estimation in fishes has traditionally relied on the analysis of growth marks in hard structures such as otoliths. However, besides being lethal this method is time-consuming, can have low accuracy in some species and cannot be applied in others. Thus, there is a need for the development of new methods. DNA methylation is an epigenetic modification consisting in the addition of a methyl group in cytosine-guanine loci. Aging is associated with changes in DNA methylation. Among a background of global and weak genome hypomethylation, there are some loci in which age-associated DNA methylation changes are of a “clock-like” nature and thus predictable. Chronological age estimators built from DNA methylation are termed ‘epigenetic clocks’. Epigenetic clocks have been developed in the last ten years for many species, notably vertebrates, including already several fish species. Here, we review the piscine epigenetic clocks built so far and outline the major considerations to be taken into account for the development of new epigenetic clocks for additional species, which include the number of samples to be collected and tissues to be targeted. The steps on how to develop such a clock and the techniques available to do so are also discussed. Next, we focus on the features of epigenetic clocks as measuring devices, considering aspects such as accuracy, precision and reproducibility. Finally, we discuss the possibility of developing a multi-species piscine epigenetic clock and how processing automation can greatly reduce the cost per sample. One important knowledge gap is to determine how environmental changes, especially temperature and food availability, may affect the tick rate of piscine epigenetic clocks. Improved age prediction through the use of piscine epigenetic clocks can contribute to better fisheries management practices in a context of overexploited fish stocks worldwide, and in the estimation of age classes in endangered species.

Local CpG density affects the trajectory and variance of age-associated DNA methylation changes

BackgroundDNA methylation is an epigenetic mark associated with the repression of gene promoters. Its pattern in the genome is disrupted with age and these changes can be used to statistically predict age with epigenetic clocks. Altered rates of aging inferred from these clocks are observed in human disease. However, the molecular mechanisms underpinning age-associated DNA methylation changes remain unknown. Local DNA sequence can program steady-state DNA methylation levels, but how it influences age-associated methylation changes is unknown.ResultsWe analyze longitudinal human DNA methylation trajectories at 345,895 CpGs from 600 individuals aged between 67 and 80 to understand the factors responsible for age-associated epigenetic changes at individual CpGs. We show that changes in methylation with age occur at 182,760 loci largely independently of variation in cell type proportions. These changes are especially apparent at 8322 low CpG density loci. Using SNP data from the same individuals, we demonstrate that methylation trajectories are affected by local sequence polymorphisms at 1487 low CpG density loci. More generally, we find that low CpG density regions are particularly prone to change and do so variably between individuals in people aged over 65. This differs from the behavior of these regions in younger individuals where they predominantly lose methylation.ConclusionsOur results, which we reproduce in two independent groups of individuals, demonstrate that local DNA sequence influences age-associated DNA methylation changes in humans in vivo. We suggest that this occurs because interactions between CpGs reinforce maintenance of methylation patterns in CpG dense regions.

A collaborative exercise on DNA methylation-based age prediction and body fluid typing

DNA methylation has become one of the most useful biomarkers for age prediction and body fluid identification in the forensic field. Therefore, several assays have been developed to detect age-associated and body fluid-specific DNA methylation changes. Among the many methods developed, SNaPshot-based assays should be particularly useful in forensic laboratories, as they permit multiplex analysis and use the same capillary electrophoresis instrumentation as STR analysis. However, technical validation of any developed assays is crucial for their proper integration into routine forensic workflow. In the present collaborative exercise, two SNaPshot multiplex assays for age prediction and a SNaPshot multiplex for body fluid identification were tested in twelve laboratories. The experimental set-up of the exercise was designed to reflect the entire workflow of SNaPshot-based methylation analysis and involved four increasingly complex tasks designed to detect potential factors influencing methylation measurements. The results of body fluid identification from each laboratory provided sufficient information to determine appropriate age prediction methods in subsequent analysis. In age prediction, systematic measurement differences resulting from the type of genetic analyzer used were identified as the biggest cause of DNA methylation variation between laboratories. Also, the use of a buffer that ensures a high ratio of specific to non-specific primer binding resulted in changes in DNA methylation measurement, especially when using degenerate primers in the PCR reaction. In addition, high input volumes of bisulfite-converted DNA often caused PCR failure, presumably due to carry-over of PCR inhibitors from the bisulfite conversion reaction. The proficiency of the analysts and experimental conditions for efficient SNaPshot reactions were also important for consistent DNA methylation measurement. Several bisulfite conversion kits were used for this study, but differences resulting from the use of any specific kit were not clearly discerned. Even when different experimental settings were used in each laboratory, a positive outcome of the study was a mean absolute age prediction error amongst participant’s data of only 2.7 years for semen, 5.0 years for blood and 3.8 years for saliva.

Abstract 2114: Horvath clock as a predictor of cancer risk in LFS patients

Abstract Li-Fraumeni syndrome (LFS) is an autosomal dominantly inherited cancer predisposition syndrome associated with germline mutations of the TP53 tumor suppressor gene. TP53 mutation carriers are susceptible to a wide range of cancers that occur at strikingly earlier age of onset than their sporadic counterparts. The lifetime cancer risk in TP53 mutation carriers is estimated to be ~73% for males and approaching 100% in females. Although improved survival outcomes have been demonstrated for carriers undergoing intense clinical surveillance, there is continued interest in identifying new environmental, genetic, and epigenetic risk factors that could improve our ability to predict disease onset and outcome. A number of studies have demonstrated age-associated DNA methylation (DNAm) changes at specific CG dinucleotides and that these changes can be combined into epigenetic age predictors to estimate chronological age. Deviation of chronological and predicted age have been associated with age-associated illnesses such as metabolic disease and cancer. For a given chronological age, older epigenetic age is presumed to indicate poorer health. An epigenetic profile defining the DNA methylation age (DNAm age) of an individual has been suggested to be a biomarker of aging, and thus possibly providing a tool for assessment of health and mortality. Our goal was to test whether DNAm age could be a possible predictor of cancer risk in LFS patients. We applied the DNA age calculator (http://dnamage.genetics.ucla.edu/) (Horvath 2013) to DNA methylation profiles derived from lymphocytes extracted from 157 LFS patients' blood samples using the Illumina HumanMethylation450 BeadChip. While a correlation of DNAm age and actual age was observed in both ‘normal' and LFS patients, the latter showed significant deviations (differences between DNAm age and chronological age). Moreover, the extent of deviations seems selectively associated with two distinct age groups (0-5 years and 20-50 years). Remarkably, this bimodal DNAm age profile shows striking resemblance to the epidemiologically characterized age dependency of LFS cancers (Amadou, 2018). Individuals with germline mutant or germline wild-type TP53 and no cancer showed no epigenetic age acceleration whereas individuals who were carriers of mutant TP53 who developed cancer showed accelerated epigenetic aging. Our preliminary results suggest that DNAm age is a dynamic, real-time correlate of patient-specific cancer risk in LFS. Further, the age dependent deviations suggest that the cancer risk profiles derived from Horvath signatures are dynamic and reflect the changes in cancer risk throughout an individual's lifetime, and could be used as a predictor of cancer onset in TP53 mutation carriers. Citation Format: Malgorzata Pienkowska, Nardin Samuel, Sanaa Choufani, Vallijah Subasri, Nish Patel, Rosanna Weksberg, Ran Kafri, David Malkin. Horvath clock as a predictor of cancer risk in LFS patients [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 2114.

Novel epigenetic clock for fetal brain development predicts prenatal age for cellular stem cell models and derived neurons

Induced pluripotent stem cells (iPSCs) and their differentiated neurons (iPSC-neurons) are a widely used cellular model in the research of the central nervous system. However, it is unknown how well they capture age-associated processes, particularly given that pluripotent cells are only present during the earliest stages of mammalian development. Epigenetic clocks utilize coordinated age-associated changes in DNA methylation to make predictions that correlate strongly with chronological age. It has been shown that the induction of pluripotency rejuvenates predicted epigenetic age. As existing clocks are not optimized for the study of brain development, we developed the fetal brain clock (FBC), a bespoke epigenetic clock trained in human prenatal brain samples in order to investigate more precisely the epigenetic age of iPSCs and iPSC-neurons. The FBC was tested in two independent validation cohorts across a total of 194 samples, confirming that the FBC outperforms other established epigenetic clocks in fetal brain cohorts. We applied the FBC to DNA methylation data from iPSCs and embryonic stem cells and their derived neuronal precursor cells and neurons, finding that these cell types are epigenetically characterized as having an early fetal age. Furthermore, while differentiation from iPSCs to neurons significantly increases epigenetic age, iPSC-neurons are still predicted as being fetal. Together our findings reiterate the need to better understand the limitations of existing epigenetic clocks for answering biological research questions and highlight a limitation of iPSC-neurons as a cellular model of age-related diseases.

An integrative analysis of the age-associated multi-omic landscape across cancers

Age is the most important risk factor for cancer, as cancer incidence and mortality increase with age. However, how molecular alterations in tumours differ among patients of different age remains largely unexplored. Here, using data from The Cancer Genome Atlas, we comprehensively characterise genomic, transcriptomic and epigenetic alterations in relation to patients’ age across cancer types. We show that tumours from older patients present an overall increase in genomic instability, somatic copy-number alterations (SCNAs) and somatic mutations. Age-associated SCNAs and mutations are identified in several cancer-driver genes across different cancer types. The largest age-related genomic differences are found in gliomas and endometrial cancer. We identify age-related global transcriptomic changes and demonstrate that these genes are in part regulated by age-associated DNA methylation changes. This study provides a comprehensive, multi-omics view of age-associated alterations in cancer and underscores age as an important factor to consider in cancer research and clinical practice.

Age-related DNA methylation changes are sex-specific: a comprehensive assessment

The existence of a sex gap in human health and longevity has been widely documented. Autosomal DNA methylation differences between males and females have been reported, but so far few studies have investigated if DNA methylation is differently affected by aging in males and females. We performed a meta-analysis of 4 large whole blood datasets, comparing 4 aspects of epigenetic age-dependent remodeling between the two sexes: differential methylation, variability, epimutations and entropy. We reported that a large fraction (43%) of sex-associated probes undergoes age-associated DNA methylation changes, and that a limited number of probes show age-by-sex interaction. We experimentally validated 2 regions mapping in FIGN and PRR4 genes and showed sex-specific deviations of their methylation patterns in models of decelerated (centenarians) and accelerated (Down syndrome) aging. While we did not find sex differences in the age-associated increase in epimutations and entropy, we showed that the number of probes having an age-related increase in methylation variability is 15 times higher in males compared to females. Our results can offer new epigenetic tools to study the interaction between aging and sex and can pave the way to the identification of molecular triggers of sex differences in longevity and age-related diseases prevalence.

Hippocampal and cortical tissue-specific epigenetic clocks indicate an increased epigenetic age in a mouse model for Alzheimer's disease.

Epigenetic clocks are based on age-associated changes in DNA methylation of CpG-sites, which can accurately measure chronological age in different species. Recently, several studies have indicated that the difference between chronological and epigenetic age, defined as the age acceleration, could reflect biological age indicating functional decline and age-associated diseases. In humans, an epigenetic clock associated Alzheimer’s disease (AD) pathology with an acceleration of the epigenetic age. In this study, we developed and validated two mouse brain region-specific epigenetic clocks from the C57BL/6J hippocampus and cerebral cortex. Both clocks, which could successfully estimate chronological age, were further validated in a widely used mouse model for AD, the triple transgenic AD (3xTg-AD) mouse. We observed an epigenetic age acceleration indicating an increased biological age for the 3xTg-AD mice compared to non-pathological C57BL/6J mice, which was more pronounced in the cortex as compared to the hippocampus. Genomic region enrichment analysis revealed that age-dependent CpGs were enriched in genes related to developmental, aging-related, neuronal and neurodegenerative functions. Due to the limited access of human brain tissues, these epigenetic clocks specific for mouse cortex and hippocampus might be important in further unravelling the role of epigenetic mechanisms underlying AD pathology or brain aging in general.

Epigenetic changes occur at decidualisation genes as a function of reproductive ageing in mice.

Reproductive decline in older female mice can be attributed to a failure of the uterus to decidualise in response to steroid hormones. Here, we show that normal decidualisation is associated with significant epigenetic changes. Notably, we identify a cohort of differentially methylated regions (DMRs), most of which gain DNA methylation between the early and late stages of decidualisation. These DMRs are enriched at progesterone-responsive gene loci that are essential for reproductive function. In female mice nearing the end of their reproductive lifespan, DNA methylation fidelity is lost at a number of CpG islands (CGIs) resulting in CGI hypermethylation at key decidualisation genes. Importantly, this hypermethylated state correlates with the failure of the corresponding genes to become transcriptionally upregulated during the implantation window. Thus, age-associated DNA methylation changes may underlie the decidualisation defects that are a common occurrence in older females. Alterations to the epigenome of uterine cells may therefore contribute significantly to the reproductive decline associated with advanced maternal age.

Dynamic Changes of DNA Methylation and Transcriptome Expression in Porcine Ovaries during Aging.

The biological function of human ovaries declines along with aging. To identify the underlying molecular changes during ovarian aging, pigs were used as model animals. Genome-wide DNA methylation and transcriptome-wide RNA expression analyses were performed via high-throughput sequencing of ovaries from young pigs (180 days, puberty stage of first ovulation) and old pigs (eight years, reproductive exhaustion stage). The results identified 422 different methylation regions between old and young pigs; furthermore, a total of 2,243 mRNAs, 95 microRNAs, 248 long noncoding RNAs (lncRNAs), and 116 circular RNAs (circRNAs) were differentially expressed during both developmental stages. Gene ontology analysis showed that these genes related to different methylation and expression are involved in the ovarian aging cycle. Specifically, these are involved in cell apoptosis, death effector domain binding, embryonic development, reproduction and fertilization process, ovarian cumulus expansion, and the ovulation cycle. Multigroup cooperative control relationships were also assessed, and competing endogenous RNA (ceRNA) networks were constructed in the ovarian aging cycle. These data will help to clarify ovary age-associated potential molecular changes in DNA methylation and transcriptional patterns over time.

Novel age-associated DNA methylation changes and epigenetic age acceleration in middle-aged African Americans and whites

BackgroundAfrican Americans (AAs) experience premature chronic health outcomes and longevity disparities consistent with an accelerated aging phenotype. DNA methylation (DNAm) levels at specific CpG positions are hallmarks of aging evidenced by the presence of age-associated differentially methylated CpG positions (aDMPs) that are the basis for the epigenetic clock for measuring biological age acceleration. Since DNAm has not been widely studied among non-European populations, we examined the association between DNAm and chronological age in AAs and whites, and the association between race, poverty, sex, and epigenetic age acceleration.ResultsWe measured genome-wide DNA methylation (866,836 CpGs) using the Illumina MethylationEPIC BeadChip in blood DNA extracted from 487 middle-aged AA (N = 244) and white (N = 243), men (N = 248), and women (N = 239). The mean (sd) age was 48.4 (8.8) in AA and 49.0 (8.7) in whites (p = 0.48). We identified 4930 significantly associated aDMPs in AAs and 469 in whites. Of these, 75.6% and 53.1% were novel, largely driven by the increased number of measured CpGs in the EPIC array, in AA and whites, respectively. AAs had more age-associated DNAm changes than whites in genes implicated in age-related diseases and cellular pathways involved in growth and development. We assessed three epigenetic age acceleration measures (universal, intrinsic, and extrinsic). AAs had a significantly slower extrinsic aging compared to whites. Furthermore, compared to AA women, both AA and white men had faster aging in the universal age acceleration measure (+ 2.04 and + 1.24 years, respectively, p < 0.05).ConclusionsAAs have more wide-spread methylation changes than whites. Race and sex interact to underlie biological age acceleration suggesting altered DNA methylation patterns may be important in age-associated health disparities.

Age-related changes in DNA methylation affect renal histology and post-transplant fibrosis

During ageing, kidney function decreases due to renal tubular atrophy, interstitial fibrosis, glomerulosclerosis and arteriosclerosis. Recently, changes in DNA methylation were shown to contribute to various ageing processes. However, it is unknown whether such changes also contribute to age-related kidney dysfunction. To assess this, we profiled genome-wide changes in DNA methylation (over 800 000 CpG sites) in 95 renal biopsies obtained prior to kidney transplantation from donors aged 16 to 73 years. Donor age significantly associated with the methylation of 92 778 CpGs (false discovery rate under 0.05), corresponding to 10 285 differentially methylated regions. These regions were most frequently located in genes involved in the Wnt/beta-catenin signaling pathway. Using an independent cohort of 67 biopsies, we autonomously validated these findings. Interestingly, the methylation status of these 92 778 age-related CpGs was associated with glomerulosclerosis (34.4% of CpGs at a false discovery rate under 0.05) and interstitial fibrosis (0.9%) and graft function at one year after transplantation, but not with tubular atrophy and arteriosclerosis. No association was observed with any of these pathologies at the time of transplantation (0% at a false discovery rate under 0.05). Thus, age-associated changes in DNA methylation at the time of transplantation predict future injury of transplanted kidneys. Specifically, our epigenome-wide association study demonstrates that epigenetic renal ageing is implicated in progressive fibrosis in both the glomerulus and the interstitium.

DNA methylation in the human frontal cortex reveals a putative mechanism for age-by-disease interactions

A consistent gene set undergoes age-associated expression changes in the human cerebral cortex, and our Age-by-Disease Model posits that these changes contribute to psychiatric diseases by “pushing” the expression of disease-associated genes in disease-promoting directions. DNA methylation (DNAm) is an attractive candidate mechanism for age-associated gene expression changes. We used the Illumina HumanMethylation450 array to characterize genome-wide DNAm in the postmortem orbital frontal cortex from 20 younger (<42 years) and 19 older (>60 years) subjects. DNAm data were integrated with existing normal brain aging expression data and sets of psychiatric disease risk genes to test the hypothesis that age-associated DNAm changes contribute to age-associated gene expression changes and, by extension, susceptibility to psychiatric diseases. We found that age-associated differentially methylated regions (aDMRs) are common, robust, bidirectional, concentrated in CpG island shelves and sea, depleted in CpG islands, and enriched among genes undergoing age-associated expression changes (OR = 2.30, p = 1.69 × 10−27). We found the aDMRs are enriched among genetic association-based risk genes for schizophrenia, Alzheimer’s disease (AD), and major depressive disorder (MDD) (OR = 2.51, p = 0.00015; OR = 2.38, p = 0.036; and OR = 3.08, p = 0.018, respectively) as well as expression-based MDD-associated genes (OR = 1.48, p = 0.00012). Similar patterns of enrichment were found for aDMRs that correlate with local gene expression. These results were replicated in a large publically-available dataset, and confirmed by meta-analysis of the two datasets. Our findings suggest DNAm is a molecular mechanism for age-associated gene expression changes and support a role for DNAm in age-by-disease interactions through preferential targeting of disease-associated genes.