The circulating proteome and brain health: Mendelian randomisation and cross-sectional analyses.

Decline in cognitive function is the most feared aspect of ageing. Poorer midlife cognitive function is associated with increased dementia and stroke risk. The mechanisms underlying variation in cognitive function are uncertain. Here, we assessed associations between 1160 proteins' plasma levels and two measures of cognitive function, the digit symbol substitution test (DSST) and the Montreal Cognitive Assessment in 1198 PURE-MIND participants. We identified five DSST performance-associated proteins (NCAN, BCAN, CA14, MOG, CDCP1), with NCAN and CDCP1 showing replicated association in an independent cohort, GS (N = 1053). MRI-assessed structural brain phenotypes partially mediated (8-19%) associations between NCAN, BCAN, and MOG, and DSST performance. Mendelian randomisation analyses suggested higher CA14 levels might cause larger hippocampal volume and increased stroke risk, whilst higher CDCP1 levels might increase intracranial aneurysm risk. Our findings highlight candidates for further study and the potential for drug repurposing to reduce the risk of stroke and cognitive decline.

Blood-based epigenome-wide analyses of chronic low-grade inflammation across diverse population cohorts.

Chronic inflammation is a hallmark of age-related disease states. The effectiveness of inflammatory proteins including C-reactive protein (CRP) in assessing long-term inflammation is hindered by their phasic nature. DNA methylation (DNAm) signatures of CRP may act as more reliable markers of chronic inflammation. We show that inter-individual differences in DNAm capture 50% of the variance in circulating CRP (N = 17,936, Generation Scotland). We develop a series of DNAm predictors of CRP using state-of-the-art algorithms. An elastic-net-regression-based predictor outperformed competing methods and explained 18% of phenotypic variance in the Lothian Birth Cohort of 1936 (LBC1936) cohort, doubling that of existing DNAm predictors. DNAm predictors performed comparably in four additional test cohorts (Avon Longitudinal Study of Parents and Children, Health for Life in Singapore, Southall and Brent Revisited, and LBC1921), including for individuals of diverse genetic ancestry and different age groups. The best-performing predictor surpassed assay-measured CRP and a genetic score in its associations with 26 health outcomes. Our findings forge new avenues for assessing chronic low-grade inflammation in diverse populations.

Epigenetic Contributions to Clinical Risk Prediction of Cardiovascular Disease.

BACKGROUND: Cardiovascular disease (CVD) is among the leading causes of death worldwide. The discovery of new omics biomarkers could help to improve risk stratification algorithms and expand our understanding of molecular pathways contributing to the disease. Here, ASSIGN-a cardiovascular risk prediction tool recommended for use in Scotland-was examined in tandem with epigenetic and proteomic features in risk prediction models in ≥12 657 participants from the Generation Scotland cohort. METHODS: Previously generated DNA methylation-derived epigenetic scores (EpiScores) for 109 protein levels were considered, in addition to both measured levels and an EpiScore for cTnI (cardiac troponin I). The associations between individual protein EpiScores and the CVD risk were examined using Cox regression (ncases≥1274; ncontrols≥11 383) and visualized in a tailored R application. Splitting the cohort into independent training (n=6880) and test (n=3659) subsets, a composite CVD EpiScore was then developed. RESULTS: Sixty-five protein EpiScores were associated with incident CVD independently of ASSIGN and the measured concentration of cTnI (P<0.05), over a follow-up of up to 16 years of electronic health record linkage. The most significant EpiScores were for proteins involved in metabolic, immune response, and tissue development/regeneration pathways. A composite CVD EpiScore (based on 45 protein EpiScores) was a significant predictor of CVD risk independent of ASSIGN and the concentration of cTnI (hazard ratio, 1.32; P=3.7×10-3; 0.3% increase in C-statistic). CONCLUSIONS: EpiScores for circulating protein levels are associated with CVD risk independent of traditional risk factors and may increase our understanding of the etiology of the disease.

Comprehensive evaluation of smoking exposures and their interactions on DNA methylation.

BACKGROUND: Smoking impacts DNA methylation, but data are lacking on smoking-related differential methylation by sex or dietary intake, recent smoking cessation (<1 year), persistence of differential methylation from in utero smoking exposure, and effects of environmental tobacco smoke (ETS). METHODS: We meta-analysed data from up to 15,014 adults across 5 cohorts with DNA methylation measured in blood using Illumina's EPIC array for current smoking (2560 exposed), quit < 1 year (500 exposed), in utero (286 exposed), and ETS exposure (676 exposed). We also evaluated the interaction of current smoking with sex or diet (fibre, folate, and vitamin C). FINDINGS: Using false discovery rate (FDR < 0.05), 65,857 CpGs were differentially methylated in relation to current smoking, 4025 with recent quitting, 594 with in utero exposure, and 6 with ETS. Most current smoking CpGs attenuated within a year of quitting. CpGs related to in utero exposure in adults were enriched for those previously observed in newborns. Differential methylation by current smoking at 4-71 CpGs may be modified by sex or dietary intake. Nearly half (35-50%) of differentially methylated CpGs on the 450 K array were associated with blood gene expression. Current smoking and in utero smoking CpGs implicated 3049 and 1067 druggable targets, including chemotherapy drugs. INTERPRETATION: Many smoking-related methylation sites were identified with Illumina's EPIC array. Most signals revert to levels observed in never smokers within a year of cessation. Many in utero smoking CpGs persist into adulthood. Smoking-related druggable targets may provide insights into cancer treatment response and shared mechanisms across smoking-related diseases. FUNDING: Intramural Research Program of the National Institutes of Health, Norwegian Ministry of Health and Care Services and the Ministry of Education and Research, Chief Scientist Office of the Scottish Government Health Directorates and the Scottish Funding Council, Medical Research Council UK and the Wellcome Trust.

Blood-based epigenome-wide analyses of 19 common disease states: A longitudinal, population-based linked cohort study of 18,413 Scottish individuals.

BACKGROUND: 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 individual 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 individual 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 individuals. METHODS AND FINDINGS: DNA methylation was assayed at 752,722 CpG sites in whole-blood samples 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 >20 individuals 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 >2 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 individuals that are not of Scottish and European ancestry. CONCLUSIONS: 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.

Phenome-wide analyses identify an association between the parent-of-origin effects dependent methylome and the rate of aging in humans.

BACKGROUND: The variation in the rate at which humans age may be rooted in early events acting through the genomic regions that are influenced by such events and subsequently are related to health phenotypes in later life. The parent-of-origin-effect (POE)-regulated methylome includes regions enriched for genetically controlled imprinting effects (the typical type of POE) and regions influenced by environmental effects associated with parents (the atypical POE). This part of the methylome is heavily influenced by early events, making it a potential route connecting early exposures, the epigenome, and aging. We aim to test the association of POE-CpGs with early and later exposures and subsequently with health-related phenotypes and adult aging. RESULTS: We perform a phenome-wide association analysis for the POE-influenced methylome using GS:SFHS (Ndiscovery = 5087, Nreplication = 4450). We identify and replicate 92 POE-CpG-phenotype associations. Most of the associations are contributed by the POE-CpGs belonging to the atypical class where the most strongly enriched associations are with aging (DNAmTL acceleration), intelligence, and parental (maternal) smoking exposure phenotypes. A proportion of the atypical POE-CpGs form co-methylation networks (modules) which are associated with these phenotypes, with one of the aging-associated modules displaying increased within-module methylation connectivity with age. The atypical POE-CpGs also display high levels of methylation heterogeneity, fast information loss with age, and a strong correlation with CpGs contained within epigenetic clocks. CONCLUSIONS: These results identify the association between the atypical POE-influenced methylome and aging and provide new evidence for the "early development of origin" hypothesis for aging in humans.

Development and validation of DNA methylation scores in two European cohorts augment 10-year risk prediction of type 2 diabetes.

Type 2 diabetes mellitus (T2D) presents a major health and economic burden that could be alleviated with improved early prediction and intervention. While standard risk factors have shown good predictive performance, we show that the use of blood-based DNA methylation information leads to a significant improvement in the prediction of 10-year T2D incidence risk. Previous studies have been largely constrained by linear assumptions, the use of cytosine-guanine pairs one-at-a-time and binary outcomes. We present a flexible approach (via an R package, MethylPipeR) based on a range of linear and tree-ensemble models that incorporate time-to-event data for prediction. Using the Generation Scotland cohort (training set ncases = 374, ncontrols = 9,461; test set ncases = 252, ncontrols = 4,526) our best-performing model (area under the receiver operating characteristic curve (AUC) = 0.872, area under the precision-recall curve (PRAUC) = 0.302) showed notable improvement in 10-year onset prediction beyond standard risk factors (AUC = 0.839, precision-recall AUC = 0.227). Replication was observed in the German-based KORA study (n = 1,451, ncases = 142, P = 1.6 × 10-5).

Phenome-wide analysis identifies parent-of-origin effects on the human methylome associated with changes in the rate of aging.

Variation in the rate at which humans age may be rooted in early life events acting through genomic regions that are influenced by such events and subsequently are related to health phenotypes in later life. The parent-of-origin-effect (POE)-regulated methylome includes regions either enriched for genetically controlled imprinting effects (the typical type of POE) or atypical POE introduced by environmental effects associated with parents. This part of the methylome is heavily influenced by early life events, making it a potential route connecting early environmental exposures, the epigenome and the rate of aging. Here, we aim to test the association of POE-influenced methylation of CpG dinucleotides (POE-CpG sites) with early and later environmental exposures and subsequently with health-related phenotypes and adult aging phenotypes. We do this by performing phenome-wide association analyses of the POE-influenced methylome using a large family-based population cohort (GS:SFHS, Ndiscovery=5,087, Nreplication=4,450). At the single CpG level, 92 associations of POE-CpGs with phenotypic variation were identified and replicated. Most of the associations were contributed by POE-CpGs belonging to the atypical class and the most strongly enriched associations were with aging (DNAmTL acceleration), intelligence and parental (maternal) smoking exposure phenotypes. We further found that a proportion of the atypical-POE-CpGs formed co-methylation networks (modules) which are associated with these phenotypes, with one of the aging-associated modules displaying increased internal module connectivity (strength of methylation correlation across constituent CpGs) with age. Atypical POE-CpGs also displayed high levels of methylation heterogeneity and epigenetic drift (i.e. information loss with age) and a strong correlation with CpGs contained within epigenetic clocks. These results identified associations between the atypical-POE-influenced methylome and aging and provided new evidence for the "early development of origin" hypothesis for aging in humans.

Loss of SORCS2 is Associated with Neuronal DNA Double-Strand Breaks.

SORCS2 is one of five proteins that constitute the Vps10p-domain receptor family. Members of this family play important roles in cellular processes linked to neuronal survival, differentiation and function. Genetic and functional studies implicate SORCS2 in cognitive function, as well as in neurodegenerative and psychiatric disorders. DNA damage and DNA repair deficits are linked to ageing and neurodegeneration, and transient neuronal DNA double-strand breaks (DSBs) also occur as a result of neuronal activity. Here, we report a novel role for SORCS2 in DSB formation. We show that SorCS2 loss is associated with elevated DSB levels in the mouse dentate gyrus and that knocking out SORCS2 in a human neuronal cell line increased Topoisomerase IIß-dependent DSB formation and reduced neuronal viability. Neuronal stimulation had no impact on levels of DNA breaks in vitro, suggesting that the observed differences may not be the result of aberrant neuronal activity in these cells. Our findings are consistent with studies linking the VPS10 receptors and DNA damage to neurodegenerative conditions.

Additive Effects of Stress and Alcohol Exposure on Accelerated Epigenetic Aging in Alcohol Use Disorder.

BACKGROUND: Stress contributes to premature aging and susceptibility to alcohol use disorder (AUD), and AUD itself is a factor in premature aging; however, the interrelationships of stress, AUD, and premature aging are poorly understood. METHODS: We constructed a composite score of stress from 13 stress-related outcomes in a discovery cohort of 317 individuals with AUD and control subjects. We then developed a novel methylation score of stress (MS stress) as a proxy of composite score of stress comprising 211 CpGs selected using a penalized regression model. The effects of MS stress on health outcomes and epigenetic aging were assessed in a sample of 615 patients with AUD and control subjects using epigenetic clocks and DNA methylation-based telomere length. Statistical analysis with an additive model using MS stress and a MS for alcohol consumption (MS alcohol) was conducted. Results were replicated in 2 independent cohorts (Generation Scotland, N = 7028 and the Grady Trauma Project, N = 795). RESULTS: Composite score of stress and MS stress were strongly associated with heavy alcohol consumption, trauma experience, epigenetic age acceleration (EAA), and shortened DNA methylation-based telomere length in AUD. Together, MS stress and MS alcohol additively showed strong stepwise increases in EAA. Replication analyses showed robust association between MS stress and EAA in the Generation Scotland and Grady Trauma Project cohorts. CONCLUSIONS: A methylation-derived score tracking stress exposure is associated with various stress-related phenotypes and EAA. Stress and alcohol have additive effects on aging, offering new insights into the pathophysiology of premature aging in AUD and, potentially, other aspects of gene dysregulation in this disorder.

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

BACKGROUND: DNA 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. RESULTS: We 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. CONCLUSIONS: Our 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.

Integrated methylome and phenome study of the circulating proteome reveals markers pertinent to brain health.

Characterising associations between the methylome, proteome and phenome may provide insight into biological pathways governing brain health. Here, we report an integrated DNA methylation and phenotypic study of the circulating proteome in relation to brain health. Methylome-wide association studies of 4058 plasma proteins are performed (N = 774), identifying 2928 CpG-protein associations after adjustment for multiple testing. These are independent of known genetic protein quantitative trait loci (pQTLs) and common lifestyle effects. Phenome-wide association studies of each protein are then performed in relation to 15 neurological traits (N = 1,065), identifying 405 associations between the levels of 191 proteins and cognitive scores, brain imaging measures or APOE e4 status. We uncover 35 previously unreported DNA methylation signatures for 17 protein markers of brain health. The epigenetic and proteomic markers we identify are pertinent to understanding and stratifying brain health.

Identification of influential probe types in epigenetic predictions of human traits: implications for microarray design.

BACKGROUND: CpG methylation levels can help to explain inter-individual differences in phenotypic traits. Few studies have explored whether identifying probe subsets based on their biological and statistical properties can maximise predictions whilst minimising array content. Variance component analyses and penalised regression (epigenetic predictors) were used to test the influence of (i) the number of probes considered, (ii) mean probe variability and (iii) methylation QTL status on the variance captured in eighteen traits by blood DNA methylation. Training and test samples comprised ≤ 4450 and ≤ 2578 unrelated individuals from Generation Scotland, respectively. RESULTS: As the number of probes under consideration decreased, so too did the estimates from variance components and prediction analyses. Methylation QTL status and mean probe variability did not influence variance components. However, relative effect sizes were 15% larger for epigenetic predictors based on probes with known or reported methylation QTLs compared to probes without reported methylation QTLs. Relative effect sizes were 45% larger for predictors based on probes with mean Beta-values between 10 and 90% compared to those based on hypo- or hypermethylated probes (Beta-value ≤ 10% or ≥ 90%). CONCLUSIONS: Arrays with fewer probes could reduce costs, leading to increased sample sizes for analyses. Our results show that reducing array content can restrict prediction metrics and careful attention must be given to the biological and distribution properties of CpG probes in array content selection.

Alcohol use disorder is associated with DNA methylation-based shortening of telomere length and regulated by TESPA1: implications for aging.

Chronic heavy alcohol consumption is associated with increased mortality and morbidity and often leads to premature aging; however, the mechanisms of alcohol-associated cellular aging are not well understood. In this study, we used DNA methylation derived telomere length (DNAmTL) as a novel approach to investigate the role of alcohol use on the aging process. DNAmTL was estimated by 140 cytosine phosphate guanines (CpG) sites in 372 individuals with alcohol use disorder (AUD) and 243 healthy controls (HC) and assessed using various endophenotypes and clinical biomarkers. Validation in an independent sample of DNAmTL on alcohol consumption was performed (N = 4219). Exploratory genome-wide association studies (GWAS) on DNAmTL were also performed to identify genetic variants contributing to DNAmTL shortening. Top GWAS findings were analyzed using in-silico expression quantitative trait loci analyses and related to structural MRI hippocampus volumes of individuals with AUD. DNAmTL was 0.11-kilobases shorter per year in AUD compared to HC after adjustment for age, sex, race, and blood cell composition (p = 4.0 × 10-12). This association was partially attenuated but remained significant after additionally adjusting for BMI, and smoking status (0.06 kilobases shorter per year, p = 0.002). DNAmTL shortening was strongly associated with chronic heavy alcohol use (ps < 0.001), elevated gamma-glutamyl transferase (GGT), and aspartate aminotransferase (AST) (ps < 0.004). Comparison of DNAmTL with PCR-based methods of assessing TL revealed positive correlations (R = 0.3, p = 2.2 × 10-5), highlighting the accuracy of DNAmTL as a biomarker. The GWAS meta-analysis identified a single nucleotide polymorphism (SNP), rs4374022 and 18 imputed ones in Thymocyte Expressed, Positive Selection Associated 1(TESPA1), at the genome-wide level (p = 3.75 × 10-8). The allele C of rs4374022 was associated with DNAmTL shortening, lower hippocampus volume (p < 0.01), and decreased mRNA expression in hippocampus tissue (p = 0.04). Our study demonstrates DNAmTL-related aging acceleration in AUD and suggests a functional role for TESPA1 in regulating DNAmTL length, possibly via the immune system with subsequent biological effects on brain regions negatively affected by alcohol and implicated in aging.

Pulmonary Function and Blood DNA Methylation: A Multiancestry Epigenome-Wide Association Meta-analysis.

Rationale: Methylation integrates factors present at birth and modifiable across the lifespan that can influence pulmonary function. Studies are limited in scope and replication. Objectives: To conduct large-scale epigenome-wide meta-analyses of blood DNA methylation and pulmonary function. Methods: Twelve cohorts analyzed associations of methylation at cytosine-phosphate-guanine probes (CpGs), using Illumina 450K or EPIC/850K arrays, with FEV1, FVC, and FEV1/FVC. We performed multiancestry epigenome-wide meta-analyses (total of 17,503 individuals; 14,761 European, 2,549 African, and 193 Hispanic/Latino ancestries) and interpreted results using integrative epigenomics. Measurements and Main Results: We identified 1,267 CpGs (1,042 genes) differentially methylated (false discovery rate, <0.025) in relation to FEV1, FVC, or FEV1/FVC, including 1,240 novel and 73 also related to chronic obstructive pulmonary disease (1,787 cases). We found 294 CpGs unique to European or African ancestry and 395 CpGs unique to never or ever smokers. The majority of significant CpGs correlated with nearby gene expression in blood. Findings were enriched in key regulatory elements for gene function, including accessible chromatin elements, in both blood and lung. Sixty-nine implicated genes are targets of investigational or approved drugs. One example novel gene highlighted by integrative epigenomic and druggable target analysis is TNFRSF4. Mendelian randomization and colocalization analyses suggest that epigenome-wide association study signals capture causal regulatory genomic loci. Conclusions: We identified numerous novel loci differentially methylated in relation to pulmonary function; few were detected in large genome-wide association studies. Integrative analyses highlight functional relevance and potential therapeutic targets. This comprehensive discovery of potentially modifiable, novel lung function loci expands knowledge gained from genetic studies, providing insights into lung pathogenesis.

Complex trait methylation scores in the prediction of major depressive disorder.

BACKGROUND: DNA methylation (DNAm) is associated with time-varying environmental factors that contribute to major depressive disorder (MDD) risk. We sought to test whether DNAm signatures of lifestyle and biochemical factors were associated with MDD to reveal dynamic biomarkers of MDD risk that may be amenable to lifestyle interventions. METHODS: Here, we calculated methylation scores (MS) at multiple p-value thresholds for lifestyle (BMI, smoking, alcohol consumption, and educational attainment) and biochemical (high-density lipoprotein (HDL) and total cholesterol) factors in Generation Scotland (GS) (N=9,502) and in a replication cohort (ALSPACadults, N=565), using CpG sites reported in previous well-powered methylome-wide association studies. We also compared their predictive accuracy for MDD to a MDD MS in an independent GS sub-sample (N=4,432). FINDINGS: Each trait MS was significantly associated with its corresponding phenotype in GS (ßrange=0.089-1.457) and in ALSPAC (ßrange=0.078-2.533). Each MS was also significantly associated with MDD before and after adjustment for its corresponding phenotype in GS (ßrange=0.053-0.145). After accounting for relevant lifestyle factors, MS for educational attainment (ß=0.094) and alcohol consumption (MSp-value<0.01-0.5; ßrange=-0.069-0.083) remained significantly associated with MDD in GS. Smoking (AUC=0.569) and educational attainment (AUC=0.585) MSs could discriminate MDD from controls better than the MDD MS (AUC=0.553) in the independent GS sub-sample. Analyses implicating MDD did not replicate across ALSPAC, although the direction of effect was consistent for all traits when adjusting for the MS corresponding phenotypes. INTERPRETATION: We showed that lifestyle and biochemical MS were associated with MDD before and after adjustment for their corresponding phenotypes (pnominal<0.05), but not when smoking, alcohol consumption, and BMI were also included as covariates. MDD results did not replicate in the smaller, female-only independent ALSPAC cohort (NALSPAC=565; NGS=9,502), potentially due to demographic differences or low statistical power, but effect sizes were consistent with the direction reported in GS. DNAm scores for modifiable MDD risk factors may contribute to disease vulnerability and, in some cases, explain additional variance to their observed phenotypes. FUNDING: Wellcome Trust.

Genome- and epigenome-wide studies of plasma protein biomarkers for Alzheimer's disease implicate TBCA and TREM2 in disease risk.

Introduction: The levels of many blood proteins are associated with Alzheimer's disease (AD) or its pathological hallmarks. Elucidating the molecular factors that control circulating levels of these proteins may help to identify proteins associated with disease risk mechanisms. Methods: Genome-wide and epigenome-wide studies (nindividuals ≤1064) were performed on plasma levels of 282 AD-associated proteins, identified by a structured literature review. Bayesian penalized regression estimated contributions of genetic and epigenetic variation toward inter-individual differences in plasma protein levels. Mendelian randomization (MR) and co-localization tested associations between proteins and disease-related phenotypes. Results: Sixty-four independent genetic and 26 epigenetic loci were associated with 45 proteins. Novel findings included an association between plasma triggering receptor expressed on myeloid cells 2 (TREM2) levels and a polymorphism and cytosine-phosphate-guanine (CpG) site within the MS4A4A locus. Higher plasma tubulin-specific chaperone A (TBCA) and TREM2 levels were significantly associated with lower AD risk. Discussion: Our data inform the regulation of biomarker levels and their relationships with AD.

DNA methylome-wide association study of genetic risk for depression implicates antigen processing and immune responses.

BACKGROUND: Depression is a disabling and highly prevalent condition where genetic and epigenetic, such as DNA methylation (DNAm), differences contribute to disease risk. DNA methylation is influenced by genetic variation but the association between polygenic risk of depression and DNA methylation is unknown. METHODS: We investigated the association between polygenic risk scores (PRS) for depression and DNAm by conducting a methylome-wide association study (MWAS) in Generation Scotland (N = 8898, mean age = 49.8 years) with replication in the Lothian Birth Cohorts of 1921 and 1936 and adults in the Avon Longitudinal Study of Parents and Children (ALSPAC) (Ncombined = 2049, mean age = 79.1, 69.6 and 47.2 years, respectively). We also conducted a replication MWAS in the ALSPAC children (N = 423, mean age = 17.1 years). Gene ontology analysis was conducted for the cytosine-guanine dinucleotide (CpG) probes significantly associated with depression PRS, followed by Mendelian randomisation (MR) analysis to infer the causal relationship between depression and DNAm. RESULTS: Widespread associations (NCpG = 71, pBonferroni < 0.05, p < 6.3 × 10-8) were found between PRS constructed using genetic risk variants for depression and DNAm in CpG probes that localised to genes involved in immune responses and neural development. The effect sizes for the significant associations were highly correlated between the discovery and replication samples in adults (r = 0.79) and in adolescents (r = 0.82). Gene Ontology analysis showed that significant CpG probes are enriched in immunological processes in the human leukocyte antigen system. Additional MWAS was conducted for each lead genetic risk variant. Over 47.9% of the independent genetic risk variants included in the PRS showed associations with DNAm in CpG probes located in both the same (cis) and distal (trans) locations to the genetic loci (pBonferroni < 0.045). Subsequent MR analysis showed that there are a greater number of causal effects found from DNAm to depression than vice versa (DNAm to depression: pFDR ranged from 0.024 to 7.45 × 10-30; depression to DNAm: pFDR ranged from 0.028 to 0.003). CONCLUSIONS: PRS for depression, especially those constructed from genome-wide significant genetic risk variants, showed methylome-wide differences associated with immune responses. Findings from MR analysis provided evidence for causal effect of DNAm to depression.

Epigenetic scores for the circulating proteome as tools for disease prediction.

Protein biomarkers have been identified across many age-related morbidities. However, characterising epigenetic influences could further inform disease predictions. Here, we leverage epigenome-wide data to study links between the DNA methylation (DNAm) signatures of the circulating proteome and incident diseases. Using data from four cohorts, we trained and tested epigenetic scores (EpiScores) for 953 plasma proteins, identifying 109 scores that explained between 1% and 58% of the variance in protein levels after adjusting for known protein quantitative trait loci (pQTL) genetic effects. By projecting these EpiScores into an independent sample (Generation Scotland; n = 9537) and relating them to incident morbidities over a follow-up of 14 years, we uncovered 137 EpiScore-disease associations. These associations were largely independent of immune cell proportions, common lifestyle and health factors, and biological aging. Notably, we found that our diabetes-associated EpiScores highlighted previous top biomarker associations from proteome-wide assessments of diabetes. These EpiScores for protein levels can therefore be a valuable resource for disease prediction and risk stratification.

Although our genetic code does not change throughout our lives, our genes can be turned on and off as a result of epigenetics. Epigenetics can track how the environment and even certain behaviors add or remove small chemical markers to the DNA that makes up the genome. The type and location of these markers may affect whether genes are active or silent, this is, whether the protein coded for by that gene is being produced or not. One common epigenetic marker is known as DNA methylation. DNA methylation has been linked to the levels of a range of proteins in our cells and the risk people have of developing chronic diseases. Blood samples can be used to determine the epigenetic markers a person has on their genome and to study the abundance of many proteins. Gadd, Hillary, McCartney, Zaghlool et al. studied the relationships between DNA methylation and the abundance of 953 different proteins in blood samples from individuals in the German KORA cohort and the Scottish Lothian Birth Cohort 1936. They then used machine learning to analyze the relationship between epigenetic markers found in people's blood and the abundance of proteins, obtaining epigenetic scores or 'EpiScores' for each protein. They found 109 proteins for which DNA methylation patterns explained between at least 1% and up to 58% of the variation in protein levels. Integrating the 'EpiScores' with 14 years of medical records for more than 9000 individuals from the Generation Scotland study revealed 130 connections between EpiScores for proteins and a future diagnosis of common adverse health outcomes. These included diabetes, stroke, depression, various cancers, and inflammatory conditions such as rheumatoid arthritis and inflammatory bowel disease. Age-related chronic diseases are a growing issue worldwide and place pressure on healthcare systems. They also severely reduce quality of life for individuals over many years. This work shows how epigenetic scores based on protein levels in the blood could predict a person's risk of several of these diseases. In the case of type 2 diabetes, the EpiScore results replicated previous research linking protein levels in the blood to future diagnosis of diabetes. Protein EpiScores could therefore allow researchers to identify people with the highest risk of disease, making it possible to intervene early and prevent these people from developing chronic conditions as they age.