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. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13148-022-01320-9

Clinical Outcome Predictions for the VerICiguaT Global Study in Subjects With Heart Failure With Reduced Ejection Fraction (VICTORIA) Trial

Background: The prediction of outcomes in patients with heart failure (HF) may inform prognosis, clinical decisions regarding treatment selection, and new trial planning. The VerICiguaT Global Study in Subjects With Heart Failure With Reduced Ejection Fraction included high-risk patients with HF with reduced ejection fraction and a recent worsening HF event. The study participants had a high event rate despite the use of contemporary guideline-based therapies. To provide generalizable predictive data for a broad population with a recent worsening HF event, we focused on risk prognostication in the placebo group. Methods and Results: Data from 2524 participants randomized to placebo with chronic HF (New York Heart Association functional class II–IV) and an ejection fraction of less than 45% were studied and backward variable selection was used to create Cox proportional hazards models for clinical end points, selecting from 66 candidate predictors. Final model results were produced, accounting for missing data, and nonlinearities. Optimism-corrected c-indices were calculated using 200 bootstrap samples. Over a median follow-up of 10.4 months, the primary outcome of HF hospitalization or cardiovascular death occurred in 972 patients (38.5%). Independent predictors of increased risk for the primary end point included HF characteristics (longer HF duration and worse New York Heart Association functional class), medical history (prior myocardial infarction), and laboratory values (higher N-terminal pro-hormone B-type natriuretic peptide, bilirubin, urate; lower chloride and albumin). Optimism-corrected c-indices were 0.68 for the HF hospitalization/cardiovascular death model, 0.68 for HF hospitalization/all-cause death, 0.72 for cardiovascular death, and 0.73 for all-cause death. Conclusions: Predictive models developed in a large diverse clinical trial with comprehensive clinical and laboratory baseline data—including novel measures—performed well in high-risk patients with HF who were receiving excellent guideline-based clinical care. Clinical Trial Registration: Clinicaltrials.gov identifier, NCT02861534. Lay Summary: Patients with heart failure may benefit from tools that help clinicians to better understand a patient's risk for future events like hospitalization. Relatively few risk models have been created after the worsening of heart failure in a contemporary cohort. We provide insights on the risk factors for clinical events from a recent, large, global trial of patients with worsening heart failure to help clinicians better understand and communicate prognosis and select treatment options

Age-related clonal haematopoiesis is associated with increased epigenetic age

Age-related clonal haemopoiesis (ARCH) in healthy individuals was initially observed through an increased skewing in X-chromosome inactivation [1]. More recently, several groups reported that ARCH is driven by somatic mutations [2], with the most prevalent ARCH mutations being in the DNMT3A and TET2 genes, previously described as drivers of myeloid malignancies. ARCH is associated with an increased risk for haematological cancers [2]. ARCH also confers an increased risk for non-haematological diseases, such as cardiovascular disease, atherosclerosis, and chronic ischemic heart failure, for which age is a main risk factor 3, 4. Whether ARCH is linked to accelerated ageing has remained unexplored. The most accurate and commonly used tools to measure age acceleration are epigenetic clocks: they are based on age-related methylation differences at specific CpG sites [5]. Deviations from chronological age towards an increased epigenetic age have been associated with increased risk of earlier mortality and age-related morbidities 5, 6. Here we present evidence of accelerated epigenetic age in individuals with ARCH

An epigenome-wide association study of sex-specific chronological ageing

Background Advanced age is associated with cognitive and physical decline and is a major risk factor for a multitude of disorders. There is also a gap in life expectancy between males and females. DNA methylation differences have been shown to be associated with both age and sex. Here, we investigate age-by-sex differences in blood-based DNA methylation in an unrelated cohort of 2586 individuals between the ages of 18 and 87 years, with replication in a further 4450 individuals between the ages of 18 and 93 years. Methods Linear regression models were applied, with stringent genome-wide significance thresholds (p < 3.6 x 10(-8)) used in both the discovery and replication data. A second, highly conservative mixed linear model method that better controls the false-positive rate was also applied, using the same genome-wide significance thresholds. Results Using the linear regression method, 52 autosomal and 597 X-linked CpG sites, mapping to 251 unique genes, replicated with concordant effect size directions in the age-by-sex interaction analysis. The site with the greatest difference mapped to GAGE10, an X-linked gene. Here, DNA methylation levels remained stable across the male adult age range (DNA methylation by age r = 0.02) but decreased across female adult age range (DNA methylation by age r = - 0.61). One site (cg23722529) with a significant age-by-sex interaction also had a quantitative trait locus (rs17321482) that is a genome-wide significant variant for prostate cancer. The mixed linear model method identified 11 CpG sites associated with the age-by-sex interaction. Conclusion The majority of differences in age-associated DNA methylation trajectories between sexes are present on the X chromosome. Several of these differences occur within genes that have been implicated in sexually dimorphic traits

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

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