Epigenetic prediction of complex traits and death

McCartney, Daniel L.; Hillary, Robert F.; Stevenson, Anna J.; Ritchie, Stuart J.; Martin, Richard M; Zhang, Qian; Morris, Stewart W.; Bermingham, Mairead Lesley; Campbell, Archie; Murray, Alison; Whalley, Heather C.; Galé, Catharine R.; Porteous, David J.; Haley, Chris; McRae, Allan F.; Wray, Naomi R.; Visscher, Peter M.; McIntosh, Andrew M.; Evans, Kathryn; Deary, Ian J.; Marioni, Riccardo E.

doi:10.1186/s13059-018-1514-1

Cited by 156 publications

(193 citation statements)

References 45 publications

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“…DNA methylation data from whole blood was obtained on a subset of 5,200 participants. The Illumina HumanMethylationEPIC Bead Chips array was used to measure methylation and quality control details have been reported previously [27]. Briefly, outliers based on the visual inspection of methylated to unmethylated log intensities were excluded, along with poorly performing probes and samples, and sex mismatches (predicted based on genetics versus questionnaire data) yielding an analysis dataset of 5,101.…”

Section: Methodsmentioning

confidence: 99%

“…Briefly, outliers based on the visual inspection of methylated to unmethylated log intensities were excluded, along with poorly performing probes and samples, and sex mismatches (predicted based on genetics versus questionnaire data) yielding an analysis dataset of 5,101. As reported in McCartney et al [27], further filtering was performed to exclude non-autosomal CpG sites and sites that were exclusive to the EPIC array. This allowed for the predictors to be applied to datasets that collected DNA methylation using an earlier version of the Illumina arrays (450k array) giving a total of 370,262 probes.…”

Section: Methodsmentioning

confidence: 99%

“…In the present study, we considered DNA methylation data (Illumina 450k array) from whole blood, taken at mean age 70, for analysis. Details of the collection and processing of the data have been reported previously [27]. Briefly, after quality control to remove poorly performing methylation sites, samples, and individuals with mismatching genotypes or predicted sex, a sample of 906 individuals was available for prediction analysis.…”

Section: Methodsmentioning

confidence: 99%

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Bayesian reassessment of the epigenetic architecture of complex traits

Baños

McCartney

Battram

et al. 2018

Preprint

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Epigenetic DNA modification is partly under genetic control, and occurs in response to a wide range of environmental exposures. Linking epigenetic marks to clinical outcomes may provide greater insight into underlying molecular processes of disease, assist in the identification of therapeutic targets, and improve risk prediction. Here, we present a statistical approach, based on Bayesian inference, that estimates associations between disease risk and all measured epigenetic probes jointly, automatically controlling for both data structure (including cell-count effects, relatedness, and experimental batch effects) and correlations among probes. We benchmark our approach in simulation study, finding improved estimation of probe associations across a wide range of scenarios over existing approaches. Our method estimates the total proportion of disease risk captured by epigenetic probe variation, and when we applied it to measures of body mass index (BMI) and cigarette consumption behaviour in 5,101 individuals, we find that 66.7% (95% CI 60.0-72.8) of the variation in BMI and 67.7% (95% CI 58.4-76.9) of the variation in cigarette consumption can be captured by methylation array data from whole blood, independent of the variation explained by single nucleotide polymorphism markers. We find novel associations, with smoking behaviour associated with a methylation probe at the MNDA gene with >95% posterior inclusion probability, which is a myeloid cell nuclear differentiation antigen gene previously implicated as a biomarker for inflammation and non-Hodgkin lymphoma risk. We conduct unique genome-wide enrichment analyses, identifying blood cholesterol, lipid transport and sterol metabolism pathways for BMI, and response to xenobiotic stimulus and negative regulation of RNA polymerase II promoter transcription for smoking, all with >95% posterior inclusion probability of having methylation probes with associations >1.5 times larger than the average. Finally, we improve phenotypic prediction in two independent cohorts by 28.7% and 10.2% for BMI and smoking respectively over a LASSO model. These results imply that probe measures may capture large amounts of variance because they are likely a consequence of the phenotype rather than a cause. As a result, 'omics' data may enable accurate characterization of disease progression and identification of individuals who are on a path to disease. Our approach facilitates better understanding of the underlying epigenetic architecture of complex common disease and is applicable to any kind of genomics data.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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Bayesian reassessment of the epigenetic architecture of complex traits

Baños

McCartney

Battram

et al. 2018

Preprint

Self Cite

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show abstract

“…Though DNA methylation is dynamic, the short-term variability in adults is thought to be relatively stable, marking it as a potentially useful signature of chronic exposure (18-20). Through epigenome-wide association studies (EWAS), DNA methylation signals at individual CpG sites have been associated with various health and lifestyle factors, permitting the creation of methylation-based phenotypic predictors and signatures (21-24). Recently, a large-scale EWAS of serum CRP in adults identified potential DNA methylation correlates of chronic low-grade inflammation (25).…”

Section: Introductionmentioning

confidence: 99%

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

Stevenson

McCartney

Hillary

et al. 2019

Preprint

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Results from large cohort studies investigating the association between inflammation and cognition have been mixed, possibly due to methodological disparities. However, a key issue in research utilising inflammatory biomarkers is their typically phasic responses. C-reactive protein (CRP) is widely used to investigate the association between chronic inflammation and cognition, but its plasma concentrations can markedly deviate in response to acute infection. Recently a large-scale epigenome-wide association study identified DNA methylation correlates of CRP. DNA methylation is thought to be relatively stable in the short term, marking it as a potentially useful signature of exposure. Here, we generate an epigenetic CRP score and investigate its trajectories with age, and associations with cognitive ability, in comparison to serum CRP in two cohorts: a longitudinal study of older adults (the Lothian Birth Cohort 1936, n=889) and a large, cross-sectional cohort (Generation Scotland, n=7,028).We identified differing trajectories of serum CRP across the cohorts, with no homogeneous trends seen with age. Conversely, the epigenetic score was consistently found to increase with age, and to do so more rapidly in males compared to females. Higher levels of serum CRP were found to associate with poorer cognition in Lothian Birth Cohort 1936, but not in Generation Scotland. However, a consistent negative association was identified between cognition and the epigenetic score in both cohorts. Furthermore, the epigenetic score accounted for a greater proportion of variance in cognitive ability.Our results suggest that epigenetic signatures of acute inflammatory markers may provide an enhanced signature of chronic inflammation, allowing for more reliable stratification of individuals, and thus clearer inference of associations with incident health outcomes.

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“…In the GS:SFHS cohort, DNA methylation data was obtained from 5,190 participants using peripheral blood collected at baseline. 13 DNA methylation was assessed using the Infinium MethylationEPIC BeadChip. Quality control procedures were implemented to identify and remove unreliable probes and samples, and probes on the X and Y chromosomes were excluded leaving data for 735,418 methylation loci in 5,190 individuals.…”

Section: Genome-wide Methylation Profilingmentioning

confidence: 99%