Publications by authors named "Riccardo E Marioni"

146 Publications

Genomic and phenotypic insights from an atlas of genetic effects on DNA methylation.

Nat Genet 2021 Sep 6;53(9):1311-1321. Epub 2021 Sep 6.

Estonian Genome Center, Institute of Genomics, University of Tartu, Tartu, Estonia.

Characterizing genetic influences on DNA methylation (DNAm) provides an opportunity to understand mechanisms underpinning gene regulation and disease. In the present study, we describe results of DNAm quantitative trait locus (mQTL) analyses on 32,851 participants, identifying genetic variants associated with DNAm at 420,509 DNAm sites in blood. We present a database of >270,000 independent mQTLs, of which 8.5% comprise long-range (trans) associations. Identified mQTL associations explain 15-17% of the additive genetic variance of DNAm. We show that the genetic architecture of DNAm levels is highly polygenic. Using shared genetic control between distal DNAm sites, we constructed networks, identifying 405 discrete genomic communities enriched for genomic annotations and complex traits. Shared genetic variants are associated with both DNAm levels and complex diseases, but only in a minority of cases do these associations reflect causal relationships from DNAm to trait or vice versa, indicating a more complex genotype-phenotype map than previously anticipated.
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http://dx.doi.org/10.1038/s41588-021-00923-xDOI Listing
September 2021

Increase in anticholinergic burden from 1990 to 2015: age-period-cohort analysis in UK Biobank.

Br J Clin Pharmacol 2021 Aug 18. Epub 2021 Aug 18.

Alzheimer Scotland Dementia Research Centre, University of Edinburgh, Edinburgh, UK.

Background: The use of prescription drugs with anticholinergic properties has been associated with multiple negative health outcomes in older people. Moreover, recent evidence suggests that associated adverse effects may occur even decades after stopping anticholinergic use. Despite the implicated importance of examining longitudinal patterns of anticholinergic prescribing for different age groups, few such data are available.

Methods: We performed an age-period-cohort (APC) analysis to study trends in an aggregate measure of anticholinergic burden between the years 1990 and 2015, utilising data from >220 000 UK Biobank participants with linked prescription data from primary care.

Results: Anticholinergic burden in the sample increased up to 9-fold over 25 years and was observed for both period- and age-effects across most classes of drugs. The greatest increase was seen in the prescribing of antidepressants. Female sex, lower education, and greater deprivation were associated with greater anticholinergic burden.

Conclusions: The increase in anticholinergic prescribing is mostly due to an increase in polypharmacy and is attributable to both ageing of participants, as well as period-related changes in prescribing practices. Research is needed to clarify the implications of rising anticholinergic use for public health and to contextualise this rise in light of other relevant prescribing practices.
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http://dx.doi.org/10.1111/bcp.15045DOI Listing
August 2021

Genome-wide association studies identify 137 genetic loci for DNA methylation biomarkers of aging.

Genome Biol 2021 06 29;22(1):194. Epub 2021 Jun 29.

Brown Foundation Institute of Molecular Medicine, McGovern Medical School, University of Texas Health Science Center at Houston, Houston, TX, USA.

Background: Biological aging estimators derived from DNA methylation data are heritable and correlate with morbidity and mortality. Consequently, identification of genetic and environmental contributors to the variation in these measures in populations has become a major goal in the field.

Results: Leveraging DNA methylation and SNP data from more than 40,000 individuals, we identify 137 genome-wide significant loci, of which 113 are novel, from genome-wide association study (GWAS) meta-analyses of four epigenetic clocks and epigenetic surrogate markers for granulocyte proportions and plasminogen activator inhibitor 1 levels, respectively. We find evidence for shared genetic loci associated with the Horvath clock and expression of transcripts encoding genes linked to lipid metabolism and immune function. Notably, these loci are independent of those reported to regulate DNA methylation levels at constituent clock CpGs. A polygenic score for GrimAge acceleration showed strong associations with adiposity-related traits, educational attainment, parental longevity, and C-reactive protein levels.

Conclusion: This study illuminates the genetic architecture underlying epigenetic aging and its shared genetic contributions with lifestyle factors and longevity.
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http://dx.doi.org/10.1186/s13059-021-02398-9DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8243879PMC
June 2021

A time-resolved proteomic and prognostic map of COVID-19.

Cell Syst 2021 08 14;12(8):780-794.e7. Epub 2021 Jun 14.

Charité Universitätsmedizin Berlin, Department of Infectious Diseases and Respiratory Medicine, 10117 Berlin, Germany; Berlin Institute of Health, 10178 Berlin, Germany.

COVID-19 is highly variable in its clinical presentation, ranging from asymptomatic infection to severe organ damage and death. We characterized the time-dependent progression of the disease in 139 COVID-19 inpatients by measuring 86 accredited diagnostic parameters, such as blood cell counts and enzyme activities, as well as untargeted plasma proteomes at 687 sampling points. We report an initial spike in a systemic inflammatory response, which is gradually alleviated and followed by a protein signature indicative of tissue repair, metabolic reconstitution, and immunomodulation. We identify prognostic marker signatures for devising risk-adapted treatment strategies and use machine learning to classify therapeutic needs. We show that the machine learning models based on the proteome are transferable to an independent cohort. Our study presents a map linking routinely used clinical diagnostic parameters to plasma proteomes and their dynamics in an infectious disease.
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http://dx.doi.org/10.1016/j.cels.2021.05.005DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8201874PMC
August 2021

Epigenetic predictors of lifestyle traits applied to the blood and brain.

Brain Commun 2021 19;3(2):fcab082. Epub 2021 Apr 19.

Centre for Genomic and Experimental Medicine, Institute of Genetics and Cancer, University of Edinburgh 2XU, UK.

Modifiable lifestyle factors influence the risk of developing many neurological diseases. These factors have been extensively linked with blood-based genome-wide DNA methylation, but it is unclear if the signatures from blood translate to the target tissue of interest-the brain. To investigate this, we apply blood-derived epigenetic predictors of four lifestyle traits to genome-wide DNA methylation from five post-mortem brain regions and the last blood sample prior to death in 14 individuals in the Lothian Birth Cohort 1936. Using these matched samples, we found that correlations between blood and brain DNA methylation scores for smoking, high-density lipoprotein cholesterol, alcohol and body mass index were highly variable across brain regions. Smoking scores in the dorsolateral prefrontal cortex had the strongest correlations with smoking scores in blood (=0.5,  = 14, =0.07) and smoking behaviour (=0.56,  = 9, =0.12). This was also the brain region which exhibited the largest correlations for DNA methylation at site cg05575921 - the single strongest correlate of smoking in blood-in relation to blood (=0.61,  = 14, =0.02) and smoking behaviour ( = -0.65,  = 9, =0.06). This suggested a particular vulnerability to smoking-related differential methylation in this region. Our work contributes to understanding how lifestyle factors affect the brain and suggest that lifestyle-related DNA methylation is likely to be both brain region dependent and in many cases poorly proxied for by blood. Though these pilot data provide a rarely-available opportunity for the comparison of methylation patterns across multiple brain regions and the blood, due to the limited sample size available our results must be considered as preliminary and should therefore be used as a basis for further investigation.
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http://dx.doi.org/10.1093/braincomms/fcab082DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8134833PMC
April 2021

MethylDetectR: a software for methylation-based health profiling.

Wellcome Open Res 2020 13;5:283. Epub 2021 Apr 13.

Centre for Genomic and Experimental Medicine, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, Midlothian, EH4 2XU, UK.

DNA methylation is an important biological process that involves the reversible addition of chemical tags called methyl groups to DNA and affects whether genes are active or inactive. Individual methylation profiles are determined by both genetic and environmental influences. Inter-individual variation in DNA methylation profiles can be exploited to estimate or predict a wide variety of human characteristics and disease risk profiles. Indeed, a number of methylation-based predictors of human traits have been developed and linked to important health outcomes. However, there is an unmet need to communicate the applicability and limitations of state-of-the-art methylation-based predictors to the wider community. To address this need, we have created a secure, web-based interactive platform called 'MethylDetectR' which automates the calculation of estimated values or scores for a variety of human traits using blood methylation data. These traits include age, lifestyle traits and high-density lipoprotein cholesterol. Methylation-based predictors often return scores on arbitrary scales. To provide meaning to these scores, users can interactively view how estimated trait scores for a given individual compare against other individuals in the sample. Users can optionally upload binary phenotypes and investigate how estimated traits vary according to case vs. control status for these phenotypes. Users can also view how different methylation-based predictors correlate with one another, and with phenotypic values for corresponding traits in a large reference sample (n = 4,450; Generation Scotland). The 'MethylDetectR' platform allows for the fast and secure calculation of DNA methylation-derived estimates for several human traits. This platform also helps to show the correlations between methylation-based scores and corresponding traits at the level of a sample, report estimated health profiles at an individual level, demonstrate how scores relate to important binary outcomes of interest and highlight the current limitations of molecular health predictors.
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http://dx.doi.org/10.12688/wellcomeopenres.16458.2DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8080939PMC
April 2021

Epigenome-wide association study of kidney function identifies trans-ethnic and ethnic-specific loci.

Genome Med 2021 Apr 30;13(1):74. Epub 2021 Apr 30.

Department of Epidemiology, University of Alabama at Birmingham, Birmingham, AL, USA.

Background: DNA methylation (DNAm) is associated with gene regulation and estimated glomerular filtration rate (eGFR), a measure of kidney function. Decreased eGFR is more common among US Hispanics and African Americans. The causes for this are poorly understood. We aimed to identify trans-ethnic and ethnic-specific differentially methylated positions (DMPs) associated with eGFR using an agnostic, genome-wide approach.

Methods: The study included up to 5428 participants from multi-ethnic studies for discovery and 8109 participants for replication. We tested the associations between whole blood DNAm and eGFR using beta values from Illumina 450K or EPIC arrays. Ethnicity-stratified analyses were performed using linear mixed models adjusting for age, sex, smoking, and study-specific and technical variables. Summary results were meta-analyzed within and across ethnicities. Findings were assessed using integrative epigenomics methods and pathway analyses.

Results: We identified 93 DMPs associated with eGFR at an FDR of 0.05 and replicated 13 and 1 DMPs across independent samples in trans-ethnic and African American meta-analyses, respectively. The study also validated 6 previously published DMPs. Identified DMPs showed significant overlap enrichment with DNase I hypersensitive sites in kidney tissue, sites associated with the expression of proximal genes, and transcription factor motifs and pathways associated with kidney tissue and kidney development.

Conclusions: We uncovered trans-ethnic and ethnic-specific DMPs associated with eGFR, including DMPs enriched in regulatory elements in kidney tissue and pathways related to kidney development. These findings shed light on epigenetic mechanisms associated with kidney function, bridging the gap between population-specific eGFR-associated DNAm and tissue-specific regulatory context.
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http://dx.doi.org/10.1186/s13073-021-00877-zDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8088054PMC
April 2021

Meta-analysis of genome-wide DNA methylation identifies shared associations across neurodegenerative disorders.

Genome Biol 2021 03 26;22(1):90. Epub 2021 Mar 26.

Centre for Clinical Research, The University of Queensland, Brisbane, QLD, 4019, Australia.

Background: People with neurodegenerative disorders show diverse clinical syndromes, genetic heterogeneity, and distinct brain pathological changes, but studies report overlap between these features. DNA methylation (DNAm) provides a way to explore this overlap and heterogeneity as it is determined by the combined effects of genetic variation and the environment. In this study, we aim to identify shared blood DNAm differences between controls and people with Alzheimer's disease, amyotrophic lateral sclerosis, and Parkinson's disease.

Results: We use a mixed-linear model method (MOMENT) that accounts for the effect of (un)known confounders, to test for the association of each DNAm site with each disorder. While only three probes are found to be genome-wide significant in each MOMENT association analysis of amyotrophic lateral sclerosis and Parkinson's disease (and none with Alzheimer's disease), a fixed-effects meta-analysis of the three disorders results in 12 genome-wide significant differentially methylated positions. Predicted immune cell-type proportions are disrupted across all neurodegenerative disorders. Protein inflammatory markers are correlated with profile sum-scores derived from disease-associated immune cell-type proportions in a healthy aging cohort. In contrast, they are not correlated with MOMENT DNAm-derived profile sum-scores, calculated using effect sizes of the 12 differentially methylated positions as weights.

Conclusions: We identify shared differentially methylated positions in whole blood between neurodegenerative disorders that point to shared pathogenic mechanisms. These shared differentially methylated positions may reflect causes or consequences of disease, but they are unlikely to reflect cell-type proportion differences.
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http://dx.doi.org/10.1186/s13059-021-02275-5DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8004462PMC
March 2021

Variation in VKORC1 Is Associated with Vascular Dementia.

J Alzheimers Dis 2021 ;80(3):1329-1337

Centre for Genomic and Experimental Medicine, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, UK.

Background: The genetic variant rs9923231 (VKORC1) is associated with differences in the coagulation of blood and consequentially with sensitivity to the drug warfarin. Variation in VKORC1 has been linked in a gene-based test to dementia/Alzheimer's disease in the parents of participants, with suggestive evidence for an association for rs9923231 (p = 1.8×10-7), which was included in the genome-wide significant KAT8 locus.

Objective: Our study aimed to investigate whether the relationship between rs9923231 and dementia persists only for certain dementia sub-types, and if those taking warfarin are at greater risk.

Methods: We used logistic regression and data from 238,195 participants from UK Biobank to examine the relationship between VKORC1, risk of dementia, and the interplay with warfarin use.

Results: Parental history of dementia, APOE variant, atrial fibrillation, diabetes, hypertension, and hypercholesterolemia all had strong associations with vascular dementia (p < 4.6×10-6). The T-allele in rs9923231 was linked to a lower warfarin dose (βperT - allele = -0.29, p < 2×10-16) and risk of vascular dementia (OR = 1.17, p = 0.010), but not other dementia sub-types. However, the risk of vascular dementia was not affected by warfarin use in carriers of the T-allele.

Conclusion: Our study reports for the first time an association between rs9923231 and vascular dementia, but further research is warranted to explore potential mechanisms and specify the relationship between rs9923231 and features of vascular dementia.
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http://dx.doi.org/10.3233/JAD-201256DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8150662PMC
September 2021

DNA methylation of blood cells is associated with prevalent type 2 diabetes in a meta-analysis of four European cohorts.

Clin Epigenetics 2021 02 23;13(1):40. Epub 2021 Feb 23.

MRC Integrative Epidemiology, Bristol Medical School, Bristol, BS8 2BN, UK.

Background: Type 2 diabetes (T2D) is a heterogeneous disease with well-known genetic and environmental risk factors contributing to its prevalence. Epigenetic mechanisms related to changes in DNA methylation (DNAm), may also contribute to T2D risk, but larger studies are required to discover novel markers, and to confirm existing ones.

Results: We performed a large meta-analysis of individual epigenome-wide association studies (EWAS) of prevalent T2D conducted in four European studies using peripheral blood DNAm. Analysis of differentially methylated regions (DMR) was also undertaken, based on the meta-analysis results. We found three novel CpGs associated with prevalent T2D in Europeans at cg00144180 (HDAC4), cg16765088 (near SYNM) and cg24704287 (near MIR23A) and confirmed three CpGs previously identified (mapping to TXNIP, ABCG1 and CPT1A). We also identified 77 T2D associated DMRs, most of them hypomethylated in T2D cases versus controls. In adjusted regressions among diabetic-free participants in ALSPAC, we found that all six CpGs identified in the meta-EWAS were associated with white cell-types. We estimated that these six CpGs captured 11% of the variation in T2D, which was similar to the variation explained by the model including only the common risk factors of BMI, sex, age and smoking (R = 10.6%).

Conclusions: This study identifies novel loci associated with T2D in Europeans. We also demonstrate associations of the same loci with other traits. Future studies should investigate if our findings are generalizable in non-European populations, and potential roles of these epigenetic markers in T2D etiology or in determining long term consequences of T2D.
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http://dx.doi.org/10.1186/s13148-021-01027-3DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7903628PMC
February 2021

Creating and validating a DNA methylation-based proxy for interleukin-6.

J Gerontol A Biol Sci Med Sci 2021 Feb 17. Epub 2021 Feb 17.

Centre for Genomic and Experimental Medicine, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, UK.

Background: Studies evaluating the relationship between chronic inflammation and cognitive functioning have produced heterogeneous results. A potential reason for this is the variability of inflammatory mediators which could lead to misclassifications of individuals' persisting levels of inflammation. DNA methylation has shown utility in indexing environmental exposures and could be leveraged to provide proxy signatures of chronic inflammation.

Methods: We conducted an elastic net regression of interleukin-6 (IL-6) in a cohort of 875 older adults (Lothian Birth Cohort 1936; mean age: 70 years) to develop a DNA methylation-based predictor. The predictor was tested in an independent cohort (Generation Scotland; n=7,028 [417 with measured IL-6], mean age: 51 years).

Results: A weighted score from 35 CpG sites optimally predicted IL-6 in the independent test set (Generation Scotland; R 2=4.4%, p=2.1x10 -5). In the independent test cohort, both measured IL-6 and the DNA methylation proxy increased with age (serum IL-6: n=417, β=0.02, SE=0.004 p=1.3x10 -7; DNAm IL-6 score: n=7,028, β=0.02, SE=0.0009, p<2x10 -16). Serum IL-6 did not associate with cognitive ability (n=417, β=-0.06, SE=0.05, p=0.19); however, an inverse association was identified between the DNA methylation score and cognitive functioning (n=7,028, β=-0.16, SE=0.02, pFDR<2x10 -16).

Conclusions: These results suggest methylation-based predictors can be used as proxies for inflammatory markers, potentially allowing for further insight into the relationship between inflammation and pertinent health outcomes.
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http://dx.doi.org/10.1093/gerona/glab046DOI Listing
February 2021

Genome-wide association study of circulating interleukin 6 levels identifies novel loci.

Hum Mol Genet 2021 04;30(5):393-409

Institute of Cardiovascular Science, University College London, London WC1E 6BT, UK.

Interleukin 6 (IL-6) is a multifunctional cytokine with both pro- and anti-inflammatory properties with a heritability estimate of up to 61%. The circulating levels of IL-6 in blood have been associated with an increased risk of complex disease pathogenesis. We conducted a two-staged, discovery and replication meta genome-wide association study (GWAS) of circulating serum IL-6 levels comprising up to 67 428 (ndiscovery = 52 654 and nreplication = 14 774) individuals of European ancestry. The inverse variance fixed effects based discovery meta-analysis, followed by replication led to the identification of two independent loci, IL1F10/IL1RN rs6734238 on chromosome (Chr) 2q14, (Pcombined = 1.8 × 10-11), HLA-DRB1/DRB5 rs660895 on Chr6p21 (Pcombined = 1.5 × 10-10) in the combined meta-analyses of all samples. We also replicated the IL6R rs4537545 locus on Chr1q21 (Pcombined = 1.2 × 10-122). Our study identifies novel loci for circulating IL-6 levels uncovering new immunological and inflammatory pathways that may influence IL-6 pathobiology.
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http://dx.doi.org/10.1093/hmg/ddab023DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8098112PMC
April 2021

Identification of epigenome-wide DNA methylation differences between carriers of APOE ε4 and APOE ε2 alleles.

Genome Med 2021 01 4;13(1). Epub 2021 Jan 4.

Centre for Genomic and Experimental Medicine, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, EH4 2XU, UK.

Background: The apolipoprotein E (APOE) ε4 allele is the strongest genetic risk factor for late onset Alzheimer's disease, whilst the ε2 allele confers protection. Previous studies report differential DNA methylation of APOE between ε4 and ε2 carriers, but associations with epigenome-wide methylation have not previously been characterised.

Methods: Using the EPIC array, we investigated epigenome-wide differences in whole blood DNA methylation patterns between Alzheimer's disease-free APOE ε4 (n = 2469) and ε2 (n = 1118) carriers from the two largest single-cohort DNA methylation samples profiled to date. Using a discovery, replication and meta-analysis study design, methylation differences were identified using epigenome-wide association analysis and differentially methylated region (DMR) approaches. Results were explored using pathway and methylation quantitative trait loci (meQTL) analyses.

Results: We obtained replicated evidence for DNA methylation differences in a ~ 169 kb region, which encompasses part of APOE and several upstream genes. Meta-analytic approaches identified DNA methylation differences outside of APOE: differentially methylated positions were identified in DHCR24, LDLR and ABCG1 (2.59 × 10 ≤ P ≤ 2.44 × 10) and DMRs were identified in SREBF2 and LDLR (1.63 × 10 ≤ P ≤ 3.01 × 10). Pathway and meQTL analyses implicated lipid-related processes and high-density lipoprotein cholesterol was identified as a partial mediator of the methylation differences in ABCG1 and DHCR24.

Conclusions: APOE ε4 vs. ε2 carrier status is associated with epigenome-wide methylation differences in the blood. The loci identified are located in trans as well as cis to APOE and implicate genes involved in lipid homeostasis.
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http://dx.doi.org/10.1186/s13073-020-00808-4DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7784364PMC
January 2021

Structural brain correlates of serum and epigenetic markers of inflammation in major depressive disorder.

Brain Behav Immun 2021 02 19;92:39-48. Epub 2020 Nov 19.

Division of Psychiatry, University of Edinburgh, Edinburgh, UK.

Inflammatory processes are implicated in the aetiology of Major Depressive Disorder (MDD); however, the relationship between peripheral inflammation, brain structure and depression remains unclear, partly due to complexities around the use of acute/phasic inflammatory biomarkers. Here, we report the first large-scale study of both serological and methylomic signatures of CRP (considered to represent acute and chronic measures of inflammation respectively) and their associations with depression status/symptoms, and structural neuroimaging phenotypes (T1 and diffusion MRI) in a large community-based sample (Generation Scotland; N = 271, N = 609). Serum CRP was associated with overall MDD severity, and specifically with current somatic symptoms- general interest (β = 0.145, P = 6 × 10) and energy levels (β = 0.101, P = 0.027), along with reduced entorhinal cortex thickness (β = -0.095, P = 0.037). DNAm CRP was significantly associated with reduced global grey matter/cortical volume and widespread reductions in integrity of 16/24 white matter tracts (with greatest regional effects in the external and internal capsules, β= -0.12 to -0.14). In general, the methylation-based measures showed stronger associations with imaging metrics than serum-based CRP measures (βaverage = -0.15 versus βaverage = 0.01 respectively). These findings provide evidence for central effects of peripheral inflammation from both serological and epigenetic markers of inflammation, including in brain regions previously implicated in depression. This suggests that these imaging measures may be involved in the relationship between peripheral inflammation and somatic/depressive symptoms. Notably, greater effects on brain morphology were seen for methylation-based rather than serum-based measures of inflammation, indicating the importance of such measures for future studies.
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http://dx.doi.org/10.1016/j.bbi.2020.11.024DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7910280PMC
February 2021

Birth weight associations with DNA methylation differences in an adult population.

Epigenetics 2021 Jun-Jul;16(7):783-796. Epub 2020 Oct 20.

Centre for Genomic and Experimental Medicine, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, UK.

The Developmental Origins of Health and Disease (DOHaD) theory predicts that prenatal and early life events shape adult health outcomes. Birth weight is a useful indicator of the foetal experience and has been associated with multiple adult health outcomes. DNA methylation (DNAm) is one plausible mechanism behind the relationship of birth weight to adult health. Through data linkage between Generation Scotland and historic Scottish birth cohorts, and birth records held through the NHS Information and Statistics Division, a sample of 1,757 individuals with available birth weight and DNAm data was derived. Epigenome-wide association studies (EWAS) were performed in two independently generated DNAm subgroups (n = 1,395, n = 362), relating adult DNAm from whole blood to birth weight. Meta-analysis yielded one genome-wide significant CpG site (p = 5.97x10), cg00966482. There was minimal evidence for attenuation of the effect sizes for the lead loci upon adjustment for numerous potential confounder variables (body mass index, educational attainment, and socioeconomic status). Associations between birth weight and epigenetic measures of biological age were also assessed. Associations between lower birth weight and higher Grim Age acceleration (p = 3.6x10) and shorter DNAm-derived telomere length (p = 1.7x10) are described, although results for three other epigenetic clocks were null. Our results provide support for an association between birth weight and DNAm both locally at one CpG site, and globally via biological ageing estimates.
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http://dx.doi.org/10.1080/15592294.2020.1827713DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8216207PMC
October 2020

Risk prediction of late-onset Alzheimer's disease implies an oligogenic architecture.

Nat Commun 2020 09 23;11(1):4799. Epub 2020 Sep 23.

Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, 4072, Australia.

Genetic association studies have identified 44 common genome-wide significant risk loci for late-onset Alzheimer's disease (LOAD). However, LOAD genetic architecture and prediction are unclear. Here we estimate the optimal P-threshold (P) of a genetic risk score (GRS) for prediction of LOAD in three independent datasets comprising 676 cases and 35,675 family history proxy cases. We show that the discriminative ability of GRS in LOAD prediction is maximised when selecting a small number of SNPs. Both simulation results and direct estimation indicate that the number of causal common SNPs for LOAD may be less than 100, suggesting LOAD is more oligogenic than polygenic. The best GRS explains approximately 75% of SNP-heritability, and individuals in the top decile of GRS have ten-fold increased odds when compared to those in the bottom decile. In addition, 14 variants are identified that contribute to both LOAD risk and age at onset of LOAD.
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http://dx.doi.org/10.1038/s41467-020-18534-1DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7511365PMC
September 2020

Epigenome-wide analyses identify DNA methylation signatures of dementia risk.

Alzheimers Dement (Amst) 2020 10;12(1):e12078. Epub 2020 Aug 10.

Centre for Genomic and Experimental Medicine Institute of Genetics and Molecular Medicine University of Edinburgh Edinburgh UK.

Introduction: Dementia pathogenesis begins years before clinical symptom onset, necessitating the understanding of premorbid risk mechanisms. Here we investigated potential pathogenic mechanisms by assessing DNA methylation associations with dementia risk factors in Alzheimer's disease (AD)-free participants.

Methods: Associations between dementia risk measures (family history, AD genetic risk score [GRS], and dementia risk scores [combining lifestyle, demographic, and genetic factors]) and whole-blood DNA methylation were assessed in discovery and replication samples (n = ~400 to ~5000) from Generation Scotland.

Results: AD genetic risk and two dementia risk scores were associated with differential methylation. The GRS associated predominantly with methylation differences in but also identified a genomic region implicated in Parkinson disease. Loci associated with dementia risk scores were enriched for those previously associated with body mass index and alcohol consumption.

Discussion: Dementia risk measures show widespread association with blood-based methylation, generating several hypotheses for assessment by future studies.
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http://dx.doi.org/10.1002/dad2.12078DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7416667PMC
August 2020

Epigenetic measures of ageing predict the prevalence and incidence of leading causes of death and disease burden.

Clin Epigenetics 2020 07 31;12(1):115. Epub 2020 Jul 31.

Centre for Genomic and Experimental Medicine, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, EH4 2XU, UK.

Background: Individuals of the same chronological age display different rates of biological ageing. A number of measures of biological age have been proposed which harness age-related changes in DNA methylation profiles. These measures include five 'epigenetic clocks' which provide an index of how much an individual's biological age differs from their chronological age at the time of measurement. The five clocks encompass methylation-based predictors of chronological age (HorvathAge, HannumAge), all-cause mortality (DNAm PhenoAge, DNAm GrimAge) and telomere length (DNAm Telomere Length). A sixth epigenetic measure of ageing differs from these clocks in that it acts as a speedometer providing a single time-point measurement of the pace of an individual's biological ageing. This measure of ageing is termed DunedinPoAm. In this study, we test the association between these six epigenetic measures of ageing and the prevalence and incidence of the leading causes of disease burden and mortality in high-income countries (n ≤ 9537, Generation Scotland: Scottish Family Health Study).

Results: DNAm GrimAge predicted incidence of clinically diagnosed chronic obstructive pulmonary disease (COPD), type 2 diabetes and ischemic heart disease after 13 years of follow-up (hazard ratios = 2.22, 1.52 and 1.41, respectively). DunedinPoAm predicted the incidence of COPD and lung cancer (hazard ratios = 2.02 and 1.45, respectively). DNAm PhenoAge predicted incidence of type 2 diabetes (hazard ratio = 1.54). DNAm Telomere Length associated with the incidence of ischemic heart disease (hazard ratio = 0.80). DNAm GrimAge associated with all-cause mortality, the prevalence of COPD and spirometry measures at the study baseline. These associations were present after adjusting for possible confounding risk factors including alcohol consumption, body mass index, deprivation, education and tobacco smoking and surpassed stringent Bonferroni-corrected significance thresholds.

Conclusions: Our data suggest that epigenetic measures of ageing may have utility in clinical settings to complement gold-standard methods for disease assessment and management.
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http://dx.doi.org/10.1186/s13148-020-00905-6DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7394682PMC
July 2020

Characterisation of an inflammation-related epigenetic score and its association with cognitive ability.

Clin Epigenetics 2020 07 27;12(1):113. Epub 2020 Jul 27.

Centre for Genomic and Experimental Medicine, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, EH4 2XU, UK.

Background: Chronic systemic inflammation has been associated with incident dementia, but its association with age-related cognitive decline is less clear. The acute responses of many inflammatory biomarkers mean they may provide an unreliable picture of the chronicity of inflammation. Recently, a large-scale epigenome-wide association study identified DNA methylation correlates of C-reactive protein (CRP)-a widely used acute-phase inflammatory biomarker. DNA methylation is thought to be relatively stable in the short term, marking it as a potentially useful signature of exposure.

Methods: We utilise a DNA methylation-based score for CRP and investigate its trajectories with age, and associations with cognitive ability in comparison with serum CRP and a genetic CRP score in a longitudinal study of older adults (n = 889) and a large, cross-sectional cohort (n = 7028).

Results: We identified no homogeneous trajectories of serum CRP with age across the cohorts, whereas the epigenetic CRP score was consistently found to increase with age (standardised β = 0.07 and 0.01) and to do so more rapidly in males compared to females. Additionally, the epigenetic CRP score had higher test-retest reliability compared to serum CRP, indicating its enhanced temporal stability. Higher serum CRP was not found to be associated with poorer cognitive ability (standardised β = - 0.08 and - 0.05); however, a consistent negative association was identified between cognitive ability and the epigenetic CRP score in both cohorts (standardised β = - 0.15 and - 0.08).

Conclusions: An epigenetic proxy of CRP may provide a more reliable signature of chronic inflammation, allowing for more accurate stratification of individuals, and thus clearer inference of associations with incident health outcomes.
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http://dx.doi.org/10.1186/s13148-020-00903-8DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7385981PMC
July 2020

Multi-method genome- and epigenome-wide studies of inflammatory protein levels in healthy older adults.

Genome Med 2020 07 8;12(1):60. Epub 2020 Jul 8.

Centre for Genomic and Experimental Medicine, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, EH4 2XU, UK.

Background: The molecular factors which control circulating levels of inflammatory proteins are not well understood. Furthermore, association studies between molecular probes and human traits are often performed by linear model-based methods which may fail to account for complex structure and interrelationships within molecular datasets.

Methods: In this study, we perform genome- and epigenome-wide association studies (GWAS/EWAS) on the levels of 70 plasma-derived inflammatory protein biomarkers in healthy older adults (Lothian Birth Cohort 1936; n = 876; Olink® inflammation panel). We employ a Bayesian framework (BayesR+) which can account for issues pertaining to data structure and unknown confounding variables (with sensitivity analyses using ordinary least squares- (OLS) and mixed model-based approaches).

Results: We identified 13 SNPs associated with 13 proteins (n = 1 SNP each) concordant across OLS and Bayesian methods. We identified 3 CpG sites spread across 3 proteins (n = 1 CpG each) that were concordant across OLS, mixed-model and Bayesian analyses. Tagged genetic variants accounted for up to 45% of variance in protein levels (for MCP2, 36% of variance alone attributable to 1 polymorphism). Methylation data accounted for up to 46% of variation in protein levels (for CXCL10). Up to 66% of variation in protein levels (for VEGFA) was explained using genetic and epigenetic data combined. We demonstrated putative causal relationships between CD6 and IL18R1 with inflammatory bowel disease and between IL12B and Crohn's disease.

Conclusions: Our data may aid understanding of the molecular regulation of the circulating inflammatory proteome as well as causal relationships between inflammatory mediators and disease.
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http://dx.doi.org/10.1186/s13073-020-00754-1DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7346642PMC
July 2020

Ultra-High-Throughput Clinical Proteomics Reveals Classifiers of COVID-19 Infection.

Cell Syst 2020 07 2;11(1):11-24.e4. Epub 2020 Jun 2.

Charité Universitätsmedizin, Berlin, Department of Infectious Diseases and Respiratory Medicine, 10117 Berlin, Germany.

The COVID-19 pandemic is an unprecedented global challenge, and point-of-care diagnostic classifiers are urgently required. Here, we present a platform for ultra-high-throughput serum and plasma proteomics that builds on ISO13485 standardization to facilitate simple implementation in regulated clinical laboratories. Our low-cost workflow handles up to 180 samples per day, enables high precision quantification, and reduces batch effects for large-scale and longitudinal studies. We use our platform on samples collected from a cohort of early hospitalized cases of the SARS-CoV-2 pandemic and identify 27 potential biomarkers that are differentially expressed depending on the WHO severity grade of COVID-19. They include complement factors, the coagulation system, inflammation modulators, and pro-inflammatory factors upstream and downstream of interleukin 6. All protocols and software for implementing our approach are freely available. In total, this work supports the development of routine proteomic assays to aid clinical decision making and generate hypotheses about potential COVID-19 therapeutic targets.
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http://dx.doi.org/10.1016/j.cels.2020.05.012DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7264033PMC
July 2020

Epigenetic prediction of major depressive disorder.

Mol Psychiatry 2020 Jun 10. Epub 2020 Jun 10.

Division of Psychiatry, Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK.

Variation in DNA methylation (DNAm) is associated with lifestyle factors such as smoking and body mass index (BMI) but there has been little research exploring its ability to identify individuals with major depressive disorder (MDD). Using penalised regression on genome-wide CpG methylation, we tested whether DNAm risk scores (MRS), trained on 1223 MDD cases and 1824 controls, could discriminate between cases (n = 363) and controls (n = 1417) in an independent sample, comparing their predictive accuracy to polygenic risk scores (PRS). The MRS explained 1.75% of the variance in MDD (β = 0.338, p = 1.17 × 10) and remained associated after adjustment for lifestyle factors (β = 0.219, p = 0.001, R = 0.68%). When modelled alongside PRS (β = 0.384, p = 4.69 × 10) the MRS remained associated with MDD (β = 0.327, p = 5.66 × 10). The MRS was also associated with incident cases of MDD who were well at recruitment but went on to develop MDD at a later assessment (β = 0.193, p = 0.016, R = 0.52%). Heritability analyses found additive genetic effects explained 22% of variance in the MRS, with a further 19% explained by pedigree-associated genetic effects and 16% by the shared couple environment. Smoking status was also strongly associated with MRS (β = 0.440, p ≤ 2 × 10). After removing smokers from the training set, the MRS strongly associated with BMI (β = 0.053, p = 0.021). We tested the association of MRS with 61 behavioural phenotypes and found that whilst PRS were associated with psychosocial and mental health phenotypes, MRS were more strongly associated with lifestyle and sociodemographic factors. DNAm-based risk scores of MDD significantly discriminated MDD cases from controls in an independent dataset and may represent an archive of exposures to lifestyle factors that are relevant to the prediction of MDD.
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http://dx.doi.org/10.1038/s41380-020-0808-3DOI Listing
June 2020

Bayesian reassessment of the epigenetic architecture of complex traits.

Nat Commun 2020 06 8;11(1):2865. Epub 2020 Jun 8.

Institute of Science and Technology Austria, Klosterneuburg, Austria.

Linking epigenetic marks to clinical outcomes improves insight into molecular processes, disease prediction, and therapeutic target identification. Here, a statistical approach is presented to infer the epigenetic architecture of complex disease, determine the variation captured by epigenetic effects, and estimate phenotype-epigenetic probe associations jointly. Implicitly adjusting for probe correlations, data structure (cell-count or relatedness), and single-nucleotide polymorphism (SNP) marker effects, improves association estimates and in 9,448 individuals, 75.7% (95% CI 71.70-79.3) of body mass index (BMI) variation and 45.6% (95% CI 37.3-51.9) of cigarette consumption variation was captured by whole blood methylation array data. Pathway-linked probes of blood cholesterol, lipid transport and sterol metabolism for BMI, and xenobiotic stimuli response for smoking, showed >1.5 times larger associations with >95% posterior inclusion probability. Prediction accuracy improved by 28.7% for BMI and 10.2% for smoking over a LASSO model, with age-, and tissue-specificity, implying associations are a phenotypic consequence rather than causal.
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http://dx.doi.org/10.1038/s41467-020-16520-1DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7280277PMC
June 2020

DNA methylation in APOE: The relationship with Alzheimer's and with cardiovascular health.

Alzheimers Dement (N Y) 2020 27;6(1):e12026. Epub 2020 Apr 27.

Centre for Genomic and Experimental Medicine Institute of Genetics and Molecular Medicine University of Edinburgh Edinburgh UK.

Introduction: Genetic variation in the apolipoprotein E () gene is associated with Alzheimer's disease (AD) and risk factors for cardiovascular disease (CVD). DNA methylationat has been associated with altered cognition and AD. It is unclear if epigenetic marks could be used for predicting future disease.

Methods: We assessed blood-based DNA methylation at 13 CpGs in the gene in 5828 participants from the Generation Scotland (GS) cohort. Using linear mixed models regression, we examined the relationships among methylation, cognition, cholesterol, the family history of AD and the risk for CVD.

Results: DNA methylation at two CpGs was associated with the ratio of total cholesterol and HDL cholesterol, but not with cognition, family history of AD, or the risk of CVD.

Discussion: methylation is associated with the levels of blood cholesterol, but there is no evidence for the utility of methylation as a biomarker for predicting AD or CVD.
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http://dx.doi.org/10.1002/trc2.12026DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7185210PMC
April 2020

Exploratory analysis of age and sex dependent DNA methylation patterns on the X-chromosome in whole blood samples.

Genome Med 2020 04 28;12(1):39. Epub 2020 Apr 28.

Epidemiology and Biostatistics, Department of Public Health, Faculty of Health Science, University of Southern Denmark, J. B. Winsløws Vej 9B, DK-5000, Odense C, Denmark.

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.
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http://dx.doi.org/10.1186/s13073-020-00736-3DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7189689PMC
April 2020

Epigenetic prediction of complex traits and mortality in a cohort of individuals with oropharyngeal cancer.

Clin Epigenetics 2020 04 22;12(1):58. Epub 2020 Apr 22.

MRC Integrative Epidemiology Unit at the University of Bristol, Bristol, UK.

Background: DNA methylation (DNAm) variation is an established predictor for several traits. In the context of oropharyngeal cancer (OPC), where 5-year survival is ~ 65%, DNA methylation may act as a prognostic biomarker. We examined the accuracy of DNA methylation biomarkers of 4 complex exposure traits (alcohol consumption, body mass index [BMI], educational attainment and smoking status) in predicting all-cause mortality in people with OPC.

Results: DNAm predictors of alcohol consumption, BMI, educational attainment and smoking status were applied to 364 individuals with OPC in the Head and Neck 5000 cohort (HN5000; 19.6% of total OPC cases in the study), followed up for median 3.9 years; inter-quartile range (IQR) 3.3 to 5.2 years (time-to-event-death or censor). The proportion of phenotypic variance explained in each trait was as follows: 16.5% for alcohol consumption, 22.7% for BMI, 0.4% for educational attainment and 51.1% for smoking. We then assessed the relationship between each DNAm predictor and all-cause mortality using Cox proportional-hazard regression analysis. DNAm prediction of smoking was most consistently associated with mortality risk (hazard ratio [HR], 1.38 per standard deviation (SD) increase in smoking DNAm score; 95% confidence interval [CI] 1.04 to 1.83; P 0.025, in a model adjusted for demographic, lifestyle, health and biological variables). Finally, we examined the accuracy of each DNAm predictor of mortality. DNAm predictors explained similar levels of variance in mortality to self-reported phenotypes. Receiver operator characteristic (ROC) curves for the DNAm predictors showed a moderate discrimination of alcohol consumption (area under the curve [AUC] 0.63), BMI (AUC 0.61) and smoking (AUC 0.70) when predicting mortality. The DNAm predictor for education showed poor discrimination (AUC 0.57). Z tests comparing AUCs between self-reported phenotype ROC curves and DNAm score ROC curves did not show evidence for difference between the two (alcohol consumption P 0.41, BMI P 0.62, educational attainment P 0.49, smoking P 0.19).

Conclusions: In the context of a clinical cohort of individuals with OPC, DNAm predictors for smoking, alcohol consumption, educational attainment and BMI exhibit similar predictive values for all-cause mortality compared to self-reported data. These findings may have translational utility in prognostic model development, particularly where phenotypic data are not available.
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http://dx.doi.org/10.1186/s13148-020-00850-4DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7178612PMC
April 2020

DNA methylation outlier burden, health, and ageing in Generation Scotland and the Lothian Birth Cohorts of 1921 and 1936.

Clin Epigenetics 2020 03 26;12(1):49. Epub 2020 Mar 26.

Centre for Genomic and Experimental Medicine, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, EH4 2XU, UK.

Background: DNA methylation outlier burden has been suggested as a potential marker of biological age. An outlier is typically defined as DNA methylation levels at any one CpG site that are three times beyond the inter-quartile range from the 25th or 75th percentiles compared to the rest of the population. DNA methylation outlier burden (the number of such outlier sites per individual) increases exponentially with age. However, these findings have been observed in small samples.

Results: Here, we showed an association between age and log-transformed DNA methylation outlier burden in a large cross-sectional cohort, the Generation Scotland Family Health Study (N = 7010, β = 0.0091, p < 2 × 10), and in two longitudinal cohort studies, the Lothian Birth Cohorts of 1921 (N = 430, β = 0.033, p = 7.9 × 10) and 1936 (N = 898, β = 0.0079, p = 0.074). Significant confounders of both cross-sectional and longitudinal associations between outlier burden and age included white blood cell proportions, body mass index (BMI), smoking, and batch effects. In Generation Scotland, the increase in epigenetic outlier burden with age was not purely an artefact of an increase in DNA methylation level variability with age (epigenetic drift). Log-transformed DNA methylation outlier burden in Generation Scotland was not related to self-reported, or family history of, age-related diseases, and it was not heritable (SNP-based heritability of 4.4%, p = 0.18). Finally, DNA methylation outlier burden was not significantly related to survival in either of the Lothian Birth Cohorts individually or in the meta-analysis after correction for multiple testing (HR = 1.12; 95% CI = [1.02; 1.21]; p = 0.021).

Conclusions: These findings suggest that, while it does not associate with ageing-related health outcomes, DNA methylation outlier burden does track chronological ageing and may also relate to survival. DNA methylation outlier burden may thus be useful as a marker of biological ageing.
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http://dx.doi.org/10.1186/s13148-020-00838-0DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7098133PMC
March 2020

Weighted Gene Coregulation Network Analysis of Promoter DNA Methylation on All-Cause Mortality in Old-Aged Birth Cohorts Finds Modules of High-Risk Associated Biomarkers.

J Gerontol A Biol Sci Med Sci 2020 11;75(12):2249-2257

Epidemiology and Biostatistics, Department of Public Health, University of Southern Denmark, Odense, Denmark.

Overall or all-cause mortality is a key measure of health in a population. Multiple epigenome-wide association studies have been conducted on all-cause mortality with limited significant findings and low replication. To elucidate the coregulated DNA methylation patterns associated with all-cause mortality, we conducted a weighted DNA methylation coregulation network analysis on whole-blood samples of 1,425 older individuals from the Lothian Birth Cohorts of 1921 and 1936. Our network-based analysis defined coregulated DNA methylation patterns in gene promoters into clusters or modules whose correlation with all-cause mortality was assessed by survival analysis. We found two significant modules or gene clusters associated with all-cause mortality in LBC1921 based on their eigengenes; one negatively correlated (p = 8.14E-03, 698 genes) and one positively correlated (p = 4.26E-02, 1,431 genes) with the risk of death. The two modules were replicated in LBC1936 with the same directions of correlation (p = 6.35E-02 and p = 3.64E-02, respectively). Furthermore, the modules revealed 32 genes associated with all-cause mortality (FDR < 0.05) linked to various diseases, including cancer and diabetes. Additionally, we performed pathway analysis and found 22 pathways (FDR < 0.05), including a pathway for taste transduction, which has been shown to be associated with poor prognosis in acutely hospitalized patients, and several pathways were linked to different types of cancer. The results from our network analysis show that DNA methylation of multiple genes could have been coregulated in an association with the overall risk of death. The identified epigenetic markers might help with our understanding of the molecular basis of all-cause mortality and general health.
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http://dx.doi.org/10.1093/gerona/glaa066DOI Listing
November 2020
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