ORCID Profile
0000-0002-5848-5720
Current Organisation
The University of Edinburgh
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Publisher: Public Library of Science (PLoS)
Date: 06-07-2023
DOI: 10.1371/JOURNAL.PMED.1004247
Abstract: DNA methylation is a dynamic epigenetic mechanism that occurs at cytosine-phosphate-guanine dinucleotide (CpG) sites. Epigenome-wide association studies (EWAS) investigate the strength of association between methylation at in idual CpG sites and health outcomes. Although blood methylation may act as a peripheral marker of common disease states, previous EWAS have typically focused only on in idual conditions and have had limited power to discover disease-associated loci. This study examined the association of blood DNA methylation with the prevalence of 14 disease states and the incidence of 19 disease states in a single population of over 18,000 Scottish in iduals. DNA methylation was assayed at 752,722 CpG sites in whole-blood s les from 18,413 volunteers in the family-structured, population-based cohort study Generation Scotland (age range 18 to 99 years). EWAS tested for cross-sectional associations between baseline CpG methylation and 14 prevalent disease states, and for longitudinal associations between baseline CpG methylation and 19 incident disease states. Prevalent cases were self-reported on health questionnaires at the baseline. Incident cases were identified using linkage to Scottish primary (Read 2) and secondary (ICD-10) care records, and the censoring date was set to October 2020. The mean time-to-diagnosis ranged from 5.0 years (for chronic pain) to 11.7 years (for Coronavirus Disease 2019 (COVID-19) hospitalisation). The 19 disease states considered in this study were selected if they were present on the World Health Organisation’s 10 leading causes of death and disease burden or included in baseline self-report questionnaires. EWAS models were adjusted for age at methylation typing, sex, estimated white blood cell composition, population structure, and 5 common lifestyle risk factors. A structured literature review was also conducted to identify existing EWAS for all 19 disease states tested. The MEDLINE, Embase, Web of Science, and preprint servers were searched to retrieve relevant articles indexed as of March 27, 2023. Fifty-four of approximately 2,000 indexed articles met our inclusion criteria: assayed blood-based DNA methylation, had in iduals in each comparison group, and examined one of the 19 conditions considered. First, we assessed whether the associations identified in our study were reported in previous studies. We identified 69 associations between CpGs and the prevalence of 4 conditions, of which 58 were newly described. The conditions were breast cancer, chronic kidney disease, ischemic heart disease, and type 2 diabetes mellitus. We also uncovered 64 CpGs that associated with the incidence of 2 disease states (COPD and type 2 diabetes), of which 56 were not reported in the surveyed literature. Second, we assessed replication across existing studies, which was defined as the reporting of at least 1 common site in studies that examined the same condition. Only 6/19 disease states had evidence of such replication. The limitations of this study include the nonconsideration of medication data and a potential lack of generalizability to in iduals that are not of Scottish and European ancestry. We discovered over 100 associations between blood methylation sites and common disease states, independently of major confounding risk factors, and a need for greater standardisation among EWAS on human disease.
Publisher: Cold Spring Harbor Laboratory
Date: 12-09-2022
DOI: 10.1101/2022.09.08.507115
Abstract: Epigenetic clocks can track both chronological age (cAge) and biological age (bAge). The latter is typically defined by physiological biomarkers and risk of adverse health outcomes, including all-cause mortality. As cohort s le sizes increase, estimates of cAge and bAge become more precise. Here, we aim to refine predictors and improve understanding of the epigenomic architecture of cAge and bAge. First, we perform large-scale (N = 18,413) epigenome-wide association studies (EWAS) of chronological age and all-cause mortality. Next, to improve cAge prediction, we use methylation data from 24,673 participants from the Generation Scotland (GS) study, the Lothian Birth Cohorts (LBC) of 1921 and 1936 and 8 publicly available datasets. Through the inclusion of linear and non-linear age-CpG associations from the EWAS, feature pre-selection/dimensionality reduction in advance of elastic net regression, and a leave-one-cohort-out (LOCO) cross validation framework, we arrive at an improved cAge predictor (median absolute error = 2.3 years across 10 cohorts). In addition, we train a predictor of bAge on 1,214 all-cause mortality events in GS, based on epigenetic surrogates for 109 plasma proteins and the 8 component parts of GrimAge, the current best epigenetic predictor of all-cause mortality. We test this predictor in four external cohorts (LBC1921, LBC1936, the Framingham Heart Study and the Women’s Health Initiative study) where it outperforms GrimAge in its association to survival (HR GrimAge = 1.47 [1.40, 1.54] with p = 1.08 × 10 −52 , and HR bAge = 1.52 [1.44, 1.59] with p = 2.20 × 10 −60 ). Finally, we introduce MethylBrowsR, an online tool to visualize epigenome-wide CpG-age associations.
Publisher: Cold Spring Harbor Laboratory
Date: 11-01-2023
DOI: 10.1101/2023.01.10.23284387
Abstract: Blood DNA methylation can inform us about the biological mechanisms that underlie common disease states. Previous epigenome-wide analyses of common diseases often focus solely on the prevalence or incidence of in idual conditions and rely on small s le sizes, which may limit power to discover disease-associated loci. We conduct blood-based epigenome-wide association studies on the prevalence of 14 common disease states in Generation Scotland (n in iduals ≤18,413, n CpGs =752,722). We also utilise health record linkage to perform epigenome-wide analyses on the incidence of 19 disease states. We present a structured literature review on existing epigenome-wide analyses for all 19 disease states to assess the degree of replication within the existing literature and the novelty of the present findings. We identify 69 associations between CpGs and the prevalence of four disease states at baseline, of which 58 are novel. We also uncover 64 CpGs that associate with the incidence of two disease states (COPD and type 2 diabetes), of which 56 are novel. These associations were independent from common lifestyle risk factors. We highlight poor replication across the existing literature. Here, replication was defined by the reporting of at least one common gene in studies examining the same disease state. Existing blood-based epigenome-wide analyses showed evidence of replication for only 4/19 disease states (with up-to-15% of unique genes replicated for lung cancer). Our summary data and structured review of the literature provide an important platform to guide future studies that examine the role of blood DNA methylation in complex disease states.
Location: United Kingdom of Great Britain and Northern Ireland
No related grants have been discovered for Elena Bernabeu.