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 137 connections between EpiScores for proteins and a future diagnosis of common adverse health outcomes. These included diabetes, stroke, depression, Alzheimer’s dementia, 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.

Chronic morbidities place longstanding burdens on our health as we age. Stratifying an individual’s risk prior to symptom presentation is therefore critical (NHS England, 2016). Though complex morbidities are partially driven by genetic factors (Fuchsberger et al., 2016; Yao et al., 2018), epigenetic modifications have also been associated with disease (Lord and Cruchaga, 2014). DNA methylation (DNAm) encodes information on the epigenetic landscape of an individual and blood-based DNAm signatures have been found to predict all-cause mortality and disease onset, providing strong evidence to suggest that methylation is an important measure of disease risk (Hillary et al., 2020a; Lu et al., 2019; Zhang et al., 2017). DNAm can regulate gene transcription (Lea et al., 2018), and epigenetic differences can be reflected in the variability of the proteome (Hillary et al., 2019; Hillary et al., 2020b; Zaghlool et al., 2020). Low-grade inflammation, which is thought to exacerbate many age-related morbidities, is particularly well captured through DNAm studies of plasma protein levels (Zaghlool et al., 2020). As proteins are the primary effectors of disease, connecting the epigenome, proteome, and time to disease onset may help to resolve predictive biological signatures.

Epigenetic predictors have utilised DNAm from the blood to estimate a person’s ‘biological age’ (Lu et al., 2019), measure their exposure to lifestyle and environmental factors (McCartney et al., 2018c; McCartney et al., 2018a; Peters et al., 2021), and predict circulating levels of inflammatory proteins (Stevenson et al., 2020; Stevenson et al., 2021). A leading epigenetic predictor of biological aging, the GrimAge epigenetic clock incorporates methylation scores for seven proteins along with smoking and chronological age, and is associated with numerous incident disease outcomes independently of smoking (Hillary et al., 2020a; Lu et al., 2019). This suggests there is predictive value gained in utilising DNAm scores relevant to protein levels as intermediaries for predictions. Methylation scores also point towards the pathways that may act on health beyond the protein biomarker that they are trained on. A portfolio of methylation scores for proteins across the circulating proteome could therefore aid in the prediction of disease and offer a different, but additive signal beyond methylation or protein data alone. Generation of an extensive range of epigenetic scores for protein levels has not been attempted to date. The capability of specific protein scores to predict a range of morbidities has also not been tested. However, DNAm scores for interleukin-6 (IL-6) and C-reactive protein (CRP) have been found to associate with a range of phenotypes independently of measured protein levels, show more stable longitudinal trajectories than repeated protein measurements, and, in some cases, outperform blood-based proteomic associations with brain morphology (Stevenson et al., 2021; Conole et al., 2021). This is likely due to DNAm representing the accumulation of more sustained effects over a longer period of time than protein measurements, which have often been shown to be variable in their levels when measured at multiple time points (Koenig et al., 2003; Liu et al., 2015; Moldoveanu et al., 2000; Shah et al., 2014). DNAm scores for proteins could therefore be used to alert clinicians to individuals with high-risk biological signatures, many years prior to disease onset.

Here, we report a comprehensive association study of blood-based DNAm with proteomics and disease (Figure 1). We trained epigenetic scores – referred to as EpiScores – for 953 plasma proteins (with sample size ranging from 706 to 944 individuals) and validated them using two independent cohorts with 778 and 162 participants. We regressed out known genetic pQTL effects from the protein levels prior to generating the EpiScores to preclude the signatures being driven by common SNP data that are invariant across the lifespan. We then examined whether the most robust predictors (n = 109 EpiScores) associated with the incidence of 12 major morbidities (Table 1), over a follow-up period of up to 14 years in the Generation Scotland cohort (n = 9537). We also tested for associations between EpiScore levels and COVID-19 disease outcomes. We regressed out the effects of age on protein levels prior to training and testing; age was also included as a covariate in disease prediction models. We controlled for common risk factors for disease and assessed the capacity of EpiScores to identify previously reported protein-disease associations.

Figure 1

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A video summarising this study and detailing how the 109 EpiScores can be calculated in cohorts with DNAm data is available at: https://youtu.be/xKDWg0Wzvrg. Our MethylDetectR shiny app (Hillary and Marioni, 2020) has CpG weights for the 109 EpiScores integrated such that it automates the process of score generation for any DNAm dataset and is available at: https://www.ed.ac.uk/centre-genomic-medicine/research-groups/marioni-group/methyldetectr. A video on how to use the MethylDetectR shiny app to generate EpiScores is available at: https://youtu.be/65Y2Rv-4tPU.

Here, we report a comprehensive DNAm scoring study of 953 circulating proteins. We define 109 robust EpiScores for plasma protein levels that are independent of known pQTL effects. By projecting these EpiScores into a large cohort with extant data linkage, we show that 78 EpiScores associate with the incidence of 11 leading causes of morbidity (137 EpiScore-disease associations in total), but do not associate with COVID-19 outcomes. Finally, we show that EpiScore-diabetes associations highlight previously measured protein-diabetes relationships. The bulk of EpiScore-disease associations are independent of common lifestyle and health factors, differences in immune cell composition and GrimAge acceleration. EpiScores therefore provide methylation-proteomic signatures for disease prediction and risk stratification.

The consistency between our EpiScore-diabetes associations and previously identified protein-diabetes relationships (Elhadad et al., 2020; Gudmundsdottir et al., 2020; Ngo et al., 2021) suggests that epigenetic scores identify disease-relevant biological signals. In addition to the comprehensive lookup of SOMAscan proteins with diabetes, several of the markers we identified for COPD and IHD also reflect previous associations with measured proteins (Ganz et al., 2016; Serban et al., 2021). The three studies used for the diabetes comparison represent the largest candidate protein characterisations of type 2 diabetes to date and the top markers identified included aminoacylase-1 (ACY-1), sex hormone-binding globulin (SHBG), growth hormone receptor (GHR), and insulin-like growth factor-binding protein 2 (IFGBP-2) (Elhadad et al., 2020; Gudmundsdottir et al., 2020; Ngo et al., 2021). Our EpiScores for these top markers were also associated with diabetes, in addition to EpiScores for several other protein markers reported in these studies. A growing body of evidence suggests that type 2 diabetes is mediated by genetic and epigenetic regulators (Kwak and Park, 2016) and proteins such as ACY-1 and GHR are thought to influence a range of diabetes-associated metabolic mechanisms (Kim and Park, 2017; Pérez-Pérez et al., 2012). Proteins that we identify through EpiScore associations, such as NTR domain-containing protein 2 (WFIKKN2), have also been causally implicated in type 2 diabetes onset through Mendelian randomisation analysis (Ngo et al., 2021). In the case of diabetes, EpiScores may therefore be used as disease-relevant risk biomarkers, many years prior to onset. Validation should be tested when sufficient data become available for the remaining morbidities.

With modest test set performances (e.g., SHBG r = 0.18 and ACY-1 r = 0.25), it is perhaps surprising that such strong synergy is observed between EpiScores for proteins that associated with diabetes and the trends seen with measured proteins. Nonetheless, DNAm scores for CRP and IL-6 have previously been shown to perform modestly in test sets (r ~ 0.2, equivalent to ~4% explained variance in protein level), but augment and often outperform the measured protein related to a range of phenotypes (Stevenson et al., 2020; Stevenson et al., 2021). Compared to scores utilising DNAm for the prediction of singular diseases, our EpiScores enable the granular study of individual protein predictor signatures with clinical outcomes. For example, levels of the acid sphingomyelinase (ASM) EpiScore predicted onset of Alzheimer’s dementia, several years prior to diagnosis. ASM (encoded by SMPD1) has been discussed as a therapeutic candidate for Alzheimer’s disease (Cataldo et al., 2004; Lee et al., 2014; Park et al., 2020) and has been shown to disrupt autophagic protein degradation and associate with accumulation of amyloid-beta in murine models of Alzheimer’s pathology (Lee et al., 2014; Park et al., 2020).

Our large-scale assessment of EpiScores provides a platform for future studies, as composite predictors for traits may be created using our EpiScore database. These should be tested in incident disease predictions when sufficient case data are available. Our results indicated that the set of 109 EpiScores are likely to be heavily enriched for inflammatory, complement system and innate immune system pathways, in addition to extracellular matrix, cell remodelling, and cell adhesion pathways. This reinforces previous work linking chronic inflammation and the epigenome (Zaghlool et al., 2020). It also suggests that EpiScores could be useful in the prediction of morbidities that are characterised by differential inflammatory states. An example of this is the EpiScore for Complement Component 5 (C5), which was associated with the onset of five morbidities, the highest number for any EpiScore (Figure 5). The EpiScore for C5 is likely to reflect the biological pathways occurring in individuals with heightened complement cascade activity and could be utilised to alert clinicians to individuals at high risk of multimorbidity. Elevated levels of C5 peptides have been associated with severe inflammatory, autoimmune, and neurodegenerative states (Ma et al., 2019; Mantovani et al., 2014; Morgan and Harris, 2015) and a range of C5-targetting therapeutic approaches are in development (Alawieh et al., 2018; Brandolini et al., 2019; Hawksworth et al., 2017; Hernandez et al., 2017; Morgan and Harris, 2015; Ort et al., 2020).

Though EpiScores such as C5 – which occupy central hubs in the disease prediction framework – may provide evidence of early methylation signatures common to the onset of multiple diseases, we did not observe associations between EpiScores and COVID-19 hospitalisation or long-COVID status. This is perhaps surprising, given that many of the morbidities that our EpiScores predicted are also known risk factors for increased risk of death due to COVID-19 (Williamson et al., 2020). Many of the proteins corresponding to EpiScores in our study were also associated with COVID-19 severity and progression in two previous studies that included a pilot sample (N = 199) from the Generation Scotland cohort at baseline as control data (Demichev et al., 2021; Messner et al., 2020). COVID-19 likely has multiple intersecting risk factors that impact severity and recovery, and the lack of associations we observe is likely to be in part due to the limited number of COVID-19 cases available in Generation Scotland. Additionally, there is a large lag time between baseline biological measurement and COVID-19 in our analyses, whereas the two studies that found protein marker associations integrated protein measurements longitudinally and from samples extracted during COVID-19 progression. With increased power available through continued data linkage, EpiScore relationships with COVID-19 outcomes may be observed in future work.

This study has several limitations. First, we demonstrate that EpiScores carry disease-relevant signals that may be clinically meaningful to delineate early disease risk when comparing relative differences within a cohort. However, projecting a new individual onto a reference set is complicated due to absolute differences in methylation quantification resulting from batch and processing effects. Second, future studies should assess paired protein and EpiScore contributions to traits, as inference from EpiScores alone, while useful for disease risk stratification, is not sufficient to determine mechanisms. This may also highlight EpiScores that outperform the measured protein equivalent in disease. Third, the epitope nature of the protein measurement in the SOMAscan panel may incur probe cross-reactivity and non-specific binding; there may also be differences in how certain proteins are measured across panels (Pietzner et al., 2020; Sun et al., 2018). Comparisons of multiple protein measurement technologies on the same samples would help to explore this in more detail. Fourth, there may be pQTLs with small effect sizes that were not regressed from the proteins prior to generating the EpiScores. Fifth, while training and testing was performed across multiple cohorts, it is likely that further development of EpiScores in larger proteomic samples with diverse ancestries will improve power to generate robust scores. Upper bounds for DNAm prediction of complex traits, such as proteins, can be estimated by variance components analyses (Hillary et al., 2020b; Trejo Banos et al., 2020; Zhang et al., 2019). Finally, associations present between EpiScore measures and disease incidence may have been influenced by external factors such as prescription medications for comorbid conditions and comorbid disease prevalence.

We have shown that EpiScores for circulating protein levels predict the incidence of multiple diseases, up to 14 years prior to diagnosis. Our findings suggest that DNAm phenotyping approaches and data linkage to electronic health records in large, population-based studies have the potential to (1) capture inter-individual variability in protein levels; (2) predict incident disease risk many years prior to morbidity onset; and (3) highlight disease-relevant biological signals for further exploration. The EpiScore weights are publicly available, enabling any cohort with Illumina DNAm data to generate them and to relate them to various outcomes. Given the increasingly widespread assessment of DNAm in cohort studies (McCartney et al., 2020; Min et al., 2021), EpiScores offer an affordable and consistent (i.e., array-based) way to utilise these signatures. This information is likely to be important in risk stratification and prevention of age-related morbidities.

Datasets generated in this study are made available in Supplementary file 1; this file includes the protein EpiScore weights for the 109 EpiScores we provide for future studies to use. A video summarising this study and detailing how the 109 EpiScores can be calculated in cohorts with DNAm data is available at: https://youtu.be/xKDWg0Wzvrg. Our MethylDetectR shiny app (Hillary and Marioni, 2020) has CpG weights for the 109 EpiScores integrated such that it automates the process of score generation for any DNAm dataset and is available at: https://www.ed.ac.uk/centre-genomic-medicine/research-groups/marioni-group/methyldetectr. A video on how to use the MethylDetectR shiny app to generate EpiScores is available at: https://youtu.be/65Y2Rv-4tPU. All datasets used to create figures are included in Supplementary file 1 and specific locations for these are noted in figure legends. All code used in the analyses is available with open access at the following Gitlab repository: https://github.com/DanniGadd/EpiScores-for-protein-levels (copy archived at swh:1:rev:a5130fab3895a0d95f0dcc8826aa9fb5e8c0fa86). The source datasets analysed during the current study are not publicly available due to them containing information that could compromise participant consent and confidentiality. Data can be obtained from the data owners. Instructions for Lothian Birth Cohort data access can be found here: https://www.ed.ac.uk/lothian-birth-cohorts/data-access-collaboration. Dr Simon Cox must be contacted to obtain a Lothian Birth Cohort 'Data Request Form' by email: simon.cox@ed.ac.uk. Instructions for accessing Generation Scotland data can be found here: https://www.ed.ac.uk/generation-scotland/for-researchers/access; the 'GS Access Request Form' can be downloaded from this site. Completed request forms must be sent to access@generationscotland.org to be approved by the Generation Scotland access committee. Data from the KORA study can be requested from KORA-gen: https://www.helmholtz-munich.de/en/kora/for-scientists/cooperation-with-kora/index.html. Requests are submitted online and are subject to approval by the KORA board.

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Author details

1. Danni A Gadd

   Centre for Genomic and Experimental Medicine, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, United Kingdom

   Contribution

   Conceptualization, Formal analysis, Investigation, Methodology, Project administration, Software, Validation, Visualization, Writing – original draft, Writing – review and editing

   Contributed equally with

   Robert F Hillary, Daniel L McCartney and Shaza B Zaghlool

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-6398-5407

2. Robert F Hillary

   Centre for Genomic and Experimental Medicine, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, United Kingdom

   Contribution

   Conceptualization, Formal analysis, Investigation, Methodology, Software, Visualization

   Contributed equally with

   Danni A Gadd, Daniel L McCartney and Shaza B Zaghlool

   Competing interests

   has received consultant fees from Illumina

3. Daniel L McCartney

   Centre for Genomic and Experimental Medicine, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, United Kingdom

   Contribution

   Conceptualization, Formal analysis, Investigation, Methodology, Software, Validation

   Contributed equally with

   Danni A Gadd, Robert F Hillary and Shaza B Zaghlool

   Competing interests

   No competing interests declared

4. Shaza B Zaghlool

   1. Department of Physiology and Biophysics, Weill Cornell Medicine-Qatar, Education City, Doha, Qatar
   2. Computer Engineering Department, Virginia Tech, Blacksburg, United States

   Contribution

   Conceptualization, Formal analysis, Investigation, Methodology, Software, Validation

   Contributed equally with

   Danni A Gadd, Robert F Hillary and Daniel L McCartney

   Competing interests

   No competing interests declared

5. Anna J Stevenson

   Centre for Genomic and Experimental Medicine, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, United Kingdom

   Contribution

   Investigation, Methodology

   Competing interests

   No competing interests declared

6. Yipeng Cheng

   Centre for Genomic and Experimental Medicine, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, United Kingdom

   Contribution

   Data curation, Formal analysis

   Competing interests

   No competing interests declared

7. Chloe Fawns-Ritchie

   1. Centre for Genomic and Experimental Medicine, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, United Kingdom
   2. Department of Psychology, University of Edinburgh, Edinburgh, United Kingdom

   Contribution

   Data curation, Formal analysis

   Competing interests

   No competing interests declared

8. Cliff Nangle

   Centre for Genomic and Experimental Medicine, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, United Kingdom

   Contribution

   Data curation, Investigation

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-5432-1158

9. Archie Campbell

   Centre for Genomic and Experimental Medicine, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, United Kingdom

   Contribution

   Data curation, Investigation

   Competing interests

   No competing interests declared

10. Robin Flaig

    Centre for Genomic and Experimental Medicine, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, United Kingdom

    Contribution

    Data curation, Investigation

    Competing interests

    No competing interests declared

11. Sarah E Harris

    1. Department of Psychology, University of Edinburgh, Edinburgh, United Kingdom
    2. Lothian Birth Cohorts, University of Edinburgh, Edinburgh, United Kingdom

    Contribution

    Data curation, Investigation

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0002-4941-5106

12. Rosie M Walker

    Centre for Clinical Brain Sciences, Chancellor’s Building, University of Edinburgh, Edinburgh, United Kingdom

    Contribution

    Data curation, Investigation

    Competing interests

    No competing interests declared

13. Liu Shi

    Department of Psychiatry, University of Oxford, Oxford, United Kingdom

    Contribution

    Data curation, Investigation

    Competing interests

    No competing interests declared

14. Elliot M Tucker-Drob

    1. Department of Psychology, The University of Texas at Austin, Austin, United States
    2. Population Research Center, The University of Texas at Austin, Austin, United States

    Contribution

    Data curation, Investigation

    Competing interests

    No competing interests declared

15. Christian Gieger

    1. Research Unit Molecular Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
    2. Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
    3. German Center for Cardiovascular Research (DZHK), partner site Munich Heart Alliance, Munich, Germany
    4. German Center for Diabetes Research (DZD), Neuherberg, Germany

    Contribution

    Data curation, Investigation

    Competing interests

    No competing interests declared

16. Annette Peters

    1. Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
    2. German Center for Cardiovascular Research (DZHK), partner site Munich Heart Alliance, Munich, Germany
    3. German Center for Diabetes Research (DZD), Neuherberg, Germany

    Contribution

    Data curation, Investigation

    Competing interests

    No competing interests declared

17. Melanie Waldenberger

    1. Research Unit Molecular Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
    2. Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
    3. German Center for Cardiovascular Research (DZHK), partner site Munich Heart Alliance, Munich, Germany

    Contribution

    Data curation, Investigation

    Competing interests

    No competing interests declared

18. Johannes Graumann

    1. Scientific Service Group Biomolecular Mass Spectrometry, Max Planck Institute for Heart and Lung Research, W.G. Kerckhoff Institute, Bad Nauheim, Germany
    2. German Centre for Cardiovascular Research (DZHK), Partner Site Rhine-Main, Max Planck Institute of Heart and Lung Research, Bad Nauheim, Germany

    Contribution

    Data curation, Investigation

    Competing interests

    No competing interests declared

19. Allan F McRae

    Institute for Molecular Bioscience, University of Queensland, Brisbane, Australia

    Contribution

    Data curation, Investigation

    Competing interests

    No competing interests declared

20. Ian J Deary

    1. Department of Psychology, University of Edinburgh, Edinburgh, United Kingdom
    2. Lothian Birth Cohorts, University of Edinburgh, Edinburgh, United Kingdom

    Contribution

    Data curation, Investigation

    Competing interests

    No competing interests declared

21. David J Porteous

    Centre for Genomic and Experimental Medicine, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, United Kingdom

    Contribution

    Data curation, Investigation

    Competing interests

    No competing interests declared

22. Caroline Hayward

    1. Centre for Genomic and Experimental Medicine, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, United Kingdom
    2. Medical Research Council Human Genetics Unit, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, United Kingdom

    Contribution

    Data curation, Investigation

    Competing interests

    No competing interests declared

23. Peter M Visscher

    Institute for Molecular Bioscience, University of Queensland, Brisbane, Australia

    Contribution

    Data curation, Investigation

    Competing interests

    No competing interests declared

24. Simon R Cox

    1. Department of Psychology, University of Edinburgh, Edinburgh, United Kingdom
    2. Lothian Birth Cohorts, University of Edinburgh, Edinburgh, United Kingdom

    Contribution

    Data curation, Investigation

    Competing interests

    No competing interests declared

25. Kathryn L Evans

    Centre for Genomic and Experimental Medicine, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, United Kingdom

    Contribution

    Data curation, Investigation

    Competing interests

    No competing interests declared

26. Andrew M McIntosh

    1. Centre for Genomic and Experimental Medicine, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, United Kingdom
    2. Division of Psychiatry, University of Edinburgh, Royal Edinburgh Hospital, Edinburgh, United Kingdom

    Contribution

    Data curation, Investigation

    Competing interests

    No competing interests declared

27. Karsten Suhre

    Department of Physiology and Biophysics, Weill Cornell Medicine-Qatar, Education City, Doha, Qatar

    Contribution

    Conceptualization, Data curation, Investigation, Methodology

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0001-9638-3912

28. Riccardo E Marioni

    Centre for Genomic and Experimental Medicine, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, United Kingdom

    Contribution

    Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Resources, Software, Supervision, Writing – original draft, Writing – review and editing

    For correspondence

    Riccardo.Marioni@ed.ac.uk

    Competing interests

    has received speaker fees from Illumina and is an advisor to the Epigenetic Clock Development Foundation

    "This ORCID iD identifies the author of this article:" 0000-0003-4430-4260

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