Have you noticed that some people seem to get older faster than others? Scientists have previously found that a chemical tag on DNA known as DNA methylation can be used to predict an individual’s chronological age. However, age predicted using DNA methylation (also known as biological or epigenetic age) does not always perfectly correspond to chronological age. Indeed, some people’s biological age is higher than their years, while other people’s is lower.

When an individual’s biological age is higher than their chronological age, they are said to be experiencing ‘epigenetic age acceleration’. This type of accelerated ageing, which can be measured with ‘epigenetic clocks’ based on DNA methylation, has been associated with several adverse health outcomes, including cancer. This means that epigenetic clocks may improve our ability to predict cancer risk and detect cancer early. However, it is still unclear whether accelerated biological ageing causes cancer, or whether it simply correlates with the disease.

Morales-Berstein et al. wanted to investigate whether epigenetic age acceleration, as measured by epigenetic clocks, plays a role in the development of several cancers. To do so, they used an approach known as Mendelian randomization. Using genetic variants as natural experiments, they studied the effect of different measures of epigenetic age acceleration on cancer risk.

Their work focused on five types of cancer: breast, colorectal, prostate, ovarian and lung cancer. They used genetic association data from people of European ancestry to determine whether genetic variants that are strongly associated with accelerated ageing are also strongly associated with cancer. The results showed that one of the DNA methylation markers used as an estimate of biological ageing could be directly related to the risk of developing colorectal cancer.

This work provides new insights into the relationship between markers of biological ageing and cancer. Similar relationships should also be studied in other groups of people and for other cancer sites. The results suggest that reversing biological ageing by altering DNA methylation could prevent or delay the development of colorectal cancer.

DNA methylation (DNAm) at specific cytosine-phosphate-guanine (CpG) sites has been found to be strongly correlated with chronological age. Biological age, as predicted by DNAm patterns at specific CpG sites, may differ from chronological age on an individual basis. Observational evidence suggests that epigenetic age acceleration (i.e. when an individual’s biological age is greater than their chronological age) may be associated with an increased risk of mortality and age-related diseases, including cancer (Fransquet et al., 2019).

Epigenetic clocks are heritable indicators of biological ageing derived from DNAm data. Each clock is based on DNAm levels measured at a different set of CpG sites, which capture distinctive features of epigenetic ageing (Liu et al., 2020). ‘First-generation’ epigenetic clocks, such as HannumAge (Hannum et al., 2013) and Intrinsic HorvathAge (Horvath, 2013), have been derived from DNAm levels at CpG sites found to be strongly associated with chronological age. HannumAge is trained on 71 age-related CpGs found in blood (Hannum et al., 2013), while Intrinsic HorvathAge is based on 353 age-related CpGs found in several human tissues and cell types, and is further adjusted for blood cell counts (Horvath, 2013). More recently, ‘second-generation’ epigenetic clocks, such as, PhenoAge (Levine et al., 2018) and GrimAge (Lu et al., 2019a), have been developed to predict age-related morbidity and mortality. PhenoAge incorporates data from 513 CpGs associated with mortality and nine clinical biomarkers (i.e. albumin, creatinine, serum glucose, C-reactive protein, lymphocyte percentage, mean corpuscular volume, red cell distribution width, alkaline phosphatase and leukocyte count) (Levine et al., 2018), and GrimAge includes data from 1,030 CpGs associated with smoking pack-years and seven plasma proteins (i.e. cystatin C, leptin, tissue inhibitor metalloproteinases 1, adrenomedullin, beta-2-microglobulin, growth differentiation factor 15, and plasminogen activation inhibitor 1 (PAI-1)) (Lu et al., 2019a). Due to differences in their composition, HannumAge and Intrinsic HorvathAge are better predictors of chronological age (Hannum et al., 2013; Horvath, 2013), while PhenoAge and GrimAge stand out for their ability to predict health and lifespan (Levine et al., 2018; Lu et al., 2019a; McCrory et al., 2021).

Several studies suggest that HannumAge, Intrinsic HorvathAge, PhenoAge and GrimAge acceleration are associated with cancer risk (Levine et al., 2018; Ambatipudi et al., 2017; Levine et al., 2015; Dugue et al., 2021; Kresovich et al., 2019b; Kresovich et al., 2019a; Zheng et al., 2016). In contrast, others indicate that evidence in support of this claim is weak or non existent (Dugué et al., 2018; Hillary et al., 2020; Durso et al., 2017; Wang et al., 2021). This lack of consensus could be explained by biases that often affect observational research, such as reverse causation (e.g. cancer influencing the epigenome and not the other way around) and residual confounding (e.g. unmeasured, or imprecisely measured confounders of the association between epigenetic age acceleration and cancer) (Relton and Davey Smith, 2012).

The strength of the associations between epigenetic age acceleration and different cancers has also been found to vary across epigenetic clocks. For instance, positive associations between epigenetic age acceleration and colorectal cancer seem to be much stronger when biological age is estimated using second-generation clocks (i.e. PhenoAge and GrimAge) (Dugue et al., 2021) rather than first-generation clocks (i.e. HannumAge and Intrinsic HorvathAge) (Dugué et al., 2018; Durso et al., 2017). Lack of consensus across epigenetic clocks could be explained by differences in their algorithms (which may reflect different mechanisms of biological ageing), as well as heterogeneity in study designs (Fransquet et al., 2019). Furthermore, even if there were a consensus, it would still be unclear whether age-related DNA methylation plays a causal role in cancer risk or if it merely acts as a non-causal prognostic biomarker.

Mendelian randomization (MR), a method that uses genetic variants as instrumental variables to infer causality between a modifiable exposure and an outcome, is less likely to be affected by residual confounding and reverse causation than traditional observational methods (Davey Smith and Ebrahim, 2003). A recent genome-wide association study (GWAS) meta-analysis has revealed 137 genetic loci associated with epigenetic age acceleration (as measured by six epigenetic biomarkers) that may be used within an MR framework (McCartney et al., 2021).

McCartney et al., 2021 used IVW MR, MR-Egger, weighted median and weighted mode methods to explore the genetically predicted effects of HannumAge, Intrinsic HorvathAge, PhenoAge and GrimAge acceleration on breast, ovarian, and lung cancer. Here, we extend this analysis to include colorectal and prostate cancer (two of the most common cancers worldwide Sung et al., 2021) and use additional methods and datasets to verify the robustness of our findings.

The aim of this two-sample MR study was to examine the genetically predicted effects of epigenetic age acceleration (as measured by HannumAge Hannum et al., 2013, Horvath Intrinsic Age Horvath, 2013, PhenoAge Levine et al., 2018 and GrimAge Lu et al., 2019a) on multiple cancers (i.e., breast, prostate, colorectal, ovarian and lung cancer) using summary genetic association data from (1) McCartney et al. (N = 34,710) (McCartney et al., 2021), (2) the UK Biobank (N cases = 2671–13,879; N controls = 173,493–372,016), (3) FinnGen (N cases = 719–8401; N controls = 74,685–174,006) and (4) several international cancer genetic consortia (N cases = 11,348–122,977; N controls = 15,861–105,974).

In this comprehensive two-sample MR study of epigenetic age acceleration and multiple cancers, we found evidence to suggest that genetically predicted GrimAge acceleration may increase the risk of colorectal cancer in both males and females. Among subtypes, effects appeared to be stronger in relation to colon than rectal cancer. Our MR results also suggested that genetically predicted GrimAge acceleration may decrease the risk of prostate cancer and that genetically predicted Intrinsic HorvathAge acceleration may be protective against lung cancer. Nevertheless, these did not pass multiple testing correction. Finally, we found no consistent evidence for other measures of epigenetic age acceleration and cancers.

Our MR estimates for the association between GrimAge and colorectal cancer were consistent with those reported in Dugue et al., 2021, an observational nested case-control study in the Melbourne Collaborative Cohort Study (RR = 1.04 per year increase in GrimAge acceleration, 95% CI 1.01–1.07, p = 0.02). However, our findings contrast with those highlighted in Hillary et al., 2020, an observational cohort study that used Generation Scotland data. The latter authors observed no evidence of an association between GrimAge acceleration and colorectal cancer after correcting for multiple testing. Nevertheless, it is possible that their analyses were underpowered, as their sample only included 63 colorectal cancer cases (0.66%). More importantly, the direction of the reported estimate is consistent with our findings and those presented in Dugue et al., 2021.

Observational evidence for the association between other measures of epigenetic ageing and cancer is inconclusive (the pre-existing evidence has been summarised in Supplementary file 1 — Table s17). For instance, epigenetic clock acceleration has been positively associated with breast (Ambatipudi et al., 2017; Kresovich et al., 2019b; Kresovich et al., 2019a) and lung cancer (Levine et al., 2018; Levine et al., 2015; Dugue et al., 2021) in some studies. However, (Durso et al., 2017, Hillary et al., 2020) and (Dugué et al., 2018) did not find strong evidence to support this. In some cases, observational evidence is stronger for some clocks than it is for others. For example, for colorectal cancer, evidence of a positive association is much stronger for second-generation clocks (Dugue et al., 2021) than for first-generation clocks (Dugué et al., 2018; Durso et al., 2017). In the case of prostate cancer, as in our study, apart from weak evidence of an inverse association with GrimAge, no other associations have been observed (Dugue et al., 2021; Dugué et al., 2018). To date, the association between epigenetic age acceleration and ovarian cancer has not been explored observationally. Although our findings were less susceptible to biases that often influence observational research, they still provide no compelling evidence of a causality between several measures of epigenetic clock acceleration and cancer.

This MR study had several strengths. For instance, we pooled results from multiple sources using fixed effect meta-analysis to improve the precision of the MR estimates presented in McCartney et al., 2021. We also conducted extra sensitivity analyses, such as MR of negative control outcomes, MR of cancer risk factors, single-SNP MR and CAUSE analyses, to assess the validity of the MR assumptions. Moreover, we performed subtype-specific MR analyses and sought to corroborate our results using UK Biobank GWAS data on parental history of cancer and LD Score regression. Additionally, our findings contribute to the identification of modifiable targets for future interventions aimed at reversing epigenetic ageing for the prevention of cancer. Compared to clinical trials, MR provides a cheaper, quicker, and ethical way of assessing the long-term impact of interventions on epigenetic ageing. This is especially relevant while attempts to develop interventions which reverse epigenetic ageing are still in early stages (Fahy et al., 2019; Fitzgerald et al., 2021; Gensous et al., 2020; Chen et al., 2019).

The findings from this study should be interpreted in light of its limitations. We only identified four genetic instruments for GrimAge acceleration, which explained 0.47% of the variance in the trait. This could lead to two issues: low statistical power and horizontal pleiotropy. First, our GrimAge analyses were underpowered to detect ORs < 1.20 for colorectal cancer. Therefore, it is possible that our findings do not reflect a true effect (we identified an OR = 1.12 for colorectal). Similarly, our study was underpowered to detect genetically predicted effects of GrimAge acceleration on cancer subtypes and cancers with smaller sample sizes (i.e. ovarian and lung cancer). Some of our sensitivity analyses, such as the MR-Egger intercept test used to detect uncorrelated horizontal pleiotropy, also had low power, resulting in imprecise estimates. The weighted mode method may also be misleading in this context, as its use is limited in the presence of very few SNPs. Although these limitations potentially undermine the validity of our results, it is reassuring that point estimates for the genetically predicted effect of GrimAge acceleration on colorectal cancer were consistent across MR methods and study populations. However, since CAUSE analyses did not provide evidence against confounding by correlated horizontal pleiotropy, it is possible that the genetically predicted effects identified are attributed to correlated pleiotropy (whereby SNPs are associated with epigenetic age acceleration and cancer through a shared heritable factor) rather than a causal effect of GrimAge on cancer risk.

One could argue that because the results for GrimAge acceleration were inconsistent with those obtained for other measures of epigenetic age acceleration, chance and horizontal pleiotropy are more likely explanations for our findings. However, inconsistencies across epigenetic ageing measures do not necessarily invalidate our results. They may simply reflect differences in how clocks were trained (i.e. they were trained on different outcomes, tissues, and populations). Different clocks may capture information on distinct underlying biological ageing mechanisms (Liu et al., 2020). For example, GrimAge was trained on mortality and smoking (factors which are closely related to cancer risk), which may explain why it outperforms other measures of epigenetic ageing in predicting time-to-cancer (Lu et al., 2019a).

Although little is known about the underlying mechanisms, GrimAge may plausibly influence cancer risk through hormonal, inflammatory and metabolic processes (Yu et al., 2020; Bottazzi et al., 2018; Lau and Robinson, 2021). In bidirectional MR analyses, we found evidence that genetically predicted GrimAge acceleration may be a cause of telomere shortening, another marker of biological ageing. Shorter telomeres have been shown to lower cancer risk in prior MR analyses (Telomeres Mendelian Randomization Collaboration et al., 2017; Gao et al., 2020; Kuo et al., 2019), so it is plausible that GrimAge acceleration decreases cancer risk, at least in part, via its effect on telomere length. GrimAge acceleration may still increase cancer risk via pathways other than those related to cellular division. The positive effect of GrimAge acceleration on cancer via these other pathways may counteract the negative effects mediated via telomere length, resulting in null MR results for GrimAge acceleration and breast, ovarian, prostate and lung cancer, and positive MR results for GrimAge acceleration and colorectal cancer. To better understand the biology of ageing, future studies should consider running MR analyses using data on DNAm-predicted telomere length, since DNAm telomere length is independent of telomerase activity and has been more strongly associated with age than measured telomere length (Lu et al., 2019b).

Although promising in terms of consistency and biological plausibility, further research is required to confirm our findings. For example, multivariable MR (Burgess and Thompson, 2015; Sanderson et al., 2019) could be used to disentangle the causal effects of GrimAge acceleration on cancer from shared heritable factors such as and blood cell composition. Additionally, our analyses could be replicated using other large independent cancer datasets. Although we conducted MR analyses on parental history of cancer, their effect estimates are not directly comparable to those obtained in the main analyses due to cases in the GWAS-by-proxy of parental endpoints being defined as either or both parents reportedly having a type of cancer. Furthermore, it would also be useful to replicate our analyses once a larger GWAS of epigenetic ageing with more genetic instruments for GrimAge acceleration is available. This would allow for a more rigorous assessment of horizontal pleiotropy and may be used to assess clustering of genetic variants to reveal distinct biological mechanisms underlying the effects (Foley et al., 2021). In spite of these suggestions, we acknowledge that it may be challenging to get access to suitable datasets for replication purposes in the short term.

The selection of ‘super controls’ (e.g. in UK Biobank, FinnGen and GECCO), with no other cancers, related lesions (i.e. benign, in situ, uncertain or unspecified behaviour neoplasms) or reported family history of cancer, could have inflated cancer GWAS effect sizes (and our MR estimates), because ‘super controls’ are healthier than the general population and are less likely to be genetically predisposed to develop cancer.

Another limitation is that we did not have access to individual level data. Therefore, we were unable to stratify the analyses by potential effect modifiers, such as sex, smoking, and menopausal status. Moreover, we did not have sex-specific instruments for sex-specific cancers. However, it is unlikely that the genetic architecture of epigenetic clock acceleration differs across sexes, as DNAm levels at individual clock CpGs are highly correlated between males and females (Grodstein et al., 2020; Tajuddin et al., 2019).

Finally, to reduce bias due to population stratification, this study was conducted using data from participants of European ancestry only. The GWAS data used for the analyses had been adjusted for the top genetic principal components for the same reason. Furthermore, our MR of negative control outcomes suggests that our MR results are unlikely to be biased by residual population stratification. Despite this, confounding due to population stratification, dynastic effects and assortative mating cannot be ruled out completely, as it is not possible to test the second MR assumption (i.e. independence assumption). Furthermore, more research is required to see if our results could translate to other ancestries.

From a public health perspective, our work provides potentially relevant findings. Observational and Mendelian randomization studies suggest that GrimAge acceleration may be influenced by several cancer risk factors, such as obesity and smoking (Lu et al., 2019a; McCartney et al., 2021). If GrimAge acceleration is a causal mediator between these risk factors and colorectal cancer, the GrimAge clock may be a treatable intermediary when targeting the underlying risk factors is not feasible or too difficult to accomplish. It could also be targeted in populations at high-risk of colorectal cancer. Nevertheless, we think it may be too early to make claims regarding the clinical utility of our findings. The GrimAge clock has only recently been created and very few studies have assessed its association with colorectal cancer. More research is required to corroborate our results and to evaluate whether GrimAge acceleration can be modified through lifestyle or clinical interventions.

In conclusion, our findings suggest that genetically predicted GrimAge acceleration may increase the risk of colorectal cancer. Findings were less consistent for other epigenetic clocks and cancers. Further work is required to investigate the potential mechanisms underlying the genetically predicted effects identified in this study.

Summary statistics for epigenetic age acceleration measures of HannumAge, Intrinsic HorvathAge, PhenoAge and GrimAge were downloaded from: https://datashare.ed.ac.uk/handle/10283/3645 (Datasets used: European-ancestries meta-analysis summary statistics: Hannum (645.4Mb), European-ancestries meta-analysis summary statistics: IEAA (645.7Mb), European-ancestries meta-analysis summary statistics: GrimAge (645.7Mb), European-ancestries meta-analysis summary statistics: PhenoAge (645.7Mb)). Summary statistics for international cancer genetic consortiums were obtained from their respective data repositories. Colorectal cancer data were obtained following the submission of a written request to the GECCO committee, which may be contacted by email at kafdem@fredhutch.org/upeters@fredhutch.org. Breast, ovarian, prostate and lung cancer data were accessed via MR-Base (http://app.mrbase.org/), which holds complete GWAS summary data from BCAC, OCAC, PRACTICAL and ILCCO. Breast cancer subtype data were obtained from BCAC and can be downloaded from: http://bcac.ccge.medschl.cam.ac.uk/bcacdata/oncoarray/oncoarray-and-combined-summary-result/gwas-summary-associations-breast-cancer-risk-2020/. Data on breast and ovarian cancer in BRCA1 and BRCA2 carriers were obtained from CIMBA and can be downloaded from: http://cimba.ccge.medschl.cam.ac.uk/oncoarray-complete-summary-results/. Prostate cancer subtype data are not publicly available through MR-Base but can be accessed upon request. These data are managed by the PRACTICAL committee, which may be contacted by email at practical@icr.ac.uk. FinnGen data is publicly available and can be accessed here: https://www.finngen.fi/en/access_results (Datasets used from release 5: Malignant neoplasm of breast (all cancers excluded), Malignant neoplasm of bronchus and lung (all cancers excluded), Colorectal cancer (all cancers excluded), Malignant neoplasm of ovary (all cancers excluded), Malignant neoplasm of prostate (all cancers excluded)). UK Biobank data can be accessed through the MR-Base platform. Parental history of cancer data were obtained from the UK Biobank study under application #15825 and can be accessed via an approved application to the UK Biobank (https://www.ukbiobank.ac.uk/enable-your-research/apply-for-access). GWAS data for negative control outcomes and potential confounders were obtained via the MR-Base platform (GWAS IDs for negative control outcomes: "ukb-b-19560", "ukb-b-533"; GWAS IDs for confounders: "ukb-b-10831", "ukb-b-13702", "ukb-b-6134", "ieu-a-1239", "ukb-b-5779", "ieu-a-835", "ieu-a-61"). GWAS data for measured telomere length used in bidirectional MR analyses were also obtained via the MR-Base platform (GWAS ID: "ieu-b-4879").

1. 1. McCartney DL
   2. Min JL
   3. Richmond RC

   (2021) Edinburgh DataShare

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

   https://doi.org/10.7488/ds/2834
2. 1. Phelan, et al

   (2017) IEU OpenGWAS

   ID ieu-a-1120. Ovarian cancer.

   https://gwas.mrcieu.ac.uk/datasets/ieu-a-1120/
3. 1. Schumacher, et al

   (2018) IEU OpenGWAS

   ID ieu-b-85. Prostate cancer.

   https://gwas.mrcieu.ac.uk/datasets/ieu-b-85/
4. 1. Michailidou K

   (2017) IEU OpenGWAS

   ID ieu-a-1126. Breast cancer (Combined Oncoarray; iCOGS; GWAS meta analysis).

   https://gwas.mrcieu.ac.uk/datasets/ieu-a-1126/
5. 1. Wang Y

   (2014) IEU OpenGWAS

   ID ieu-a-966. Lung cancer.

   https://gwas.mrcieu.ac.uk/datasets/ieu-a-966/
6. 1. Wang Y

   (2014) IEU OpenGWAS

   ID ieu-a-965. Lung adenocarcinoma.

   https://gwas.mrcieu.ac.uk/datasets/ieu-a-965/
7. 1. Wang Y

   (2014) IEU OpenGWAS

   ID ieu-a-967. Squamous cell lung cancer.

   https://gwas.mrcieu.ac.uk/datasets/ieu-a-967/
8. 1. Phelan, et al

   (2017) IEU OpenGWAS

   ID ieu-a-1121. High grade serous ovarian cancer.

   https://gwas.mrcieu.ac.uk/datasets/ieu-a-1121/
9. 1. Phelan, et al

   (2017) IEU OpenGWAS

   ID ieu-a-1122. Low grade serous ovarian cancer.

   https://gwas.mrcieu.ac.uk/datasets/ieu-a-1122/
10. 1. Phelan, et al

    (2017) IEU OpenGWAS

    ID ieu-a-1123. Invasive mucinous ovarian cancer.

    https://gwas.mrcieu.ac.uk/datasets/ieu-a-1123/
11. 1. Phelan, et al

    (2017) IEU OpenGWAS

    ID ieu-a-1124. Clear cell ovarian cancer.

    https://gwas.mrcieu.ac.uk/datasets/ieu-a-1124/
12. 1. Phelan, et al

    (2017) IEU OpenGWAS

    ID ieu-a-1125. Endometrioid ovarian cancer.

    https://gwas.mrcieu.ac.uk/datasets/ieu-a-1125/
13. 1. Michailidou K

    (2017) IEU OpenGWAS

    ID ieu-a-1127. ER+ Breast cancer (Combined Oncoarray; iCOGS; GWAS meta analysis).

    https://gwas.mrcieu.ac.uk/datasets/ieu-a-1127/
14. 1. Michailidou K

    (2017) IEU OpenGWAS

    ID ieu-a-1128. ER- Breast cancer (Combined Oncoarray; iCOGS; GWAS meta analysis).

    https://gwas.mrcieu.ac.uk/datasets/ieu-a-1128/
15. 1. FinnGen consortium

    (2021) FinnGen public data r5

    ID r5.finngen.fi. r5.finngen.fi.

    https://www.finngen.fi/en/access_results
16. 1. Codd, et al

    (2021) IEU OpenGWAS

    ID ieu-b-4879. Telomere length.

    https://gwas.mrcieu.ac.uk/datasets/ieu-b-4879/
17. 1. Ben Elsworth

    (2018) IEU OpenGWAS

    ID ukb-b-19560. Skin colour.

    https://gwas.mrcieu.ac.uk/datasets/ukb-b-19560/
18. 1. Ben Elsworth

    (2018) IEU OpenGWAS

    ID ukb-b-533. Ease of skin tanning.

    https://gwas.mrcieu.ac.uk/datasets/ukb-b-533/
19. 1. Ben Elsworth

    (2018) IEU OpenGWAS

    ID ukb-b-10831. Pack years of smoking.

    https://gwas.mrcieu.ac.uk/datasets/ukb-b-10831/
20. 1. Ben Elsworth

    (2018) IEU OpenGWAS

    ID ukb-b-13702. Time spent doing vigorous physical activity.

    https://gwas.mrcieu.ac.uk/datasets/ukb-b-13702/
21. 1. Ben Elsworth

    (2018) IEU OpenGWAS

    ID ukb-b-6134. Age completed full time education.

    https://gwas.mrcieu.ac.uk/datasets/ukb-b-6134/
22. 1. Lee, et al

    (2018) IEU OpenGWAS

    ID ieu-a-1239. Years of schooling.

    https://gwas.mrcieu.ac.uk/datasets/ieu-a-1239/
23. 1. Ben Elsworth

    (2018) IEU OpenGWAS

    ID ukb-b-5779. Alcohol intake frequency.

    https://gwas.mrcieu.ac.uk/datasets/ukb-b-5779/
24. 1. Locke, et al

    (2015) IEU OpenGWAS

    ID ieu-a-835. Body mass index.

    https://gwas.mrcieu.ac.uk/datasets/ieu-a-835/
25. 1. Shungin, et al

    (2015) IEU OpenGWAS

    ID ieu-a-61. Waist circumference.

    https://gwas.mrcieu.ac.uk/datasets/ieu-a-61/

1. 1. Ambatipudi S
   2. Horvath S
   3. Perrier F

   (2017) DNA methylome analysis identifies accelerated epigenetic ageing associated with postmenopausal breast cancer susceptibility

   European Journal of Cancer 75:299–307.

   https://doi.org/10.1016/j.ejca.2017.01.014

   • Google Scholar
2. 1. Amos CI
   2. Wu X
   3. Broderick P
   4. Gorlov IP
   5. Gu J
   6. Eisen T
   7. Dong Q
   8. Zhang Q
   9. Gu X
   10. Vijayakrishnan J
   11. Sullivan K
   12. Matakidou A
   13. Wang Y
   14. Mills G
   15. Doheny K
   16. Tsai Y-Y
   17. Chen WV
   18. Shete S
   19. Spitz MR
   20. Houlston RS

   (2008) Genome-wide association scan of tag SNPs identifies a susceptibility locus for lung cancer at 15q25.1

   Nature Genetics 40:616–622.

   https://doi.org/10.1038/ng.109

   • PubMed
   • Google Scholar
3. 1. Benjamini Y
   2. Hochberg Y

   (1995) Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing

   Journal of the Royal Statistical Society 57:289–300.

   https://doi.org/10.1111/j.2517-6161.1995.tb02031.x

   • Google Scholar
4. 1. Bottazzi B
   2. Riboli E
   3. Mantovani A

   (2018) Aging, inflammation and cancer

   Seminars in Immunology 40:74–82.

   https://doi.org/10.1016/j.smim.2018.10.011

   • PubMed
   • Google Scholar
5. 1. Bowden J
   2. Davey Smith G
   3. Burgess S

   (2015) Mendelian randomization with invalid instruments: effect estimation and bias detection through Egger regression

   International Journal of Epidemiology 44:512–525.

   https://doi.org/10.1093/ije/dyv080

   • PubMed
   • Google Scholar
6. 1. Bowden Jack
   2. Davey Smith G
   3. Haycock PC
   4. Burgess S

   (2016) Consistent Estimation in Mendelian Randomization with Some Invalid Instruments Using a Weighted Median Estimator

   Genetic Epidemiology 40:304–314.

   https://doi.org/10.1002/gepi.21965

   • PubMed
   • Google Scholar
7. 1. Bowden J
   2. Del Greco MF
   3. Minelli C

   (2017) A framework for the investigation of pleiotropy in two-sample summary data Mendelian randomization

   Statistics in Medicine 36:1783–1802.

   https://doi.org/10.1002/sim.7221

   • Google Scholar
8. 1. Bowden J
   2. Del Greco MF
   3. Minelli C

   (2019) Improving the accuracy of two-sample summary-data Mendelian randomization: moving beyond the NOME assumption

   International Journal of Epidemiology 48:728–742.

   https://doi.org/10.1093/ije/dyy258

   • Google Scholar
9. 1. Bulik-Sullivan B
   2. Finucane HK
   3. Anttila V

   (2015) An atlas of genetic correlations across human diseases and traits

   Nature Genetics 47:1236–1241.

   https://doi.org/10.1038/ng.3406

   • Google Scholar
10. 1. Burgess S
    2. Thompson SG

    (2011) Bias in causal estimates from Mendelian randomization studies with weak instruments

    Statistics in Medicine 30:1312–1323.

    https://doi.org/10.1002/sim.4197

    • PubMed
    • Google Scholar
11. 1. Burgess S
    2. Butterworth A
    3. Thompson SG

    (2013) Mendelian randomization analysis with multiple genetic variants using summarized data

    Genetic Epidemiology 37:658–665.

    https://doi.org/10.1002/gepi.21758

    • Google Scholar
12. 1. Burgess S
    2. Thompson SG

    (2015) Multivariable Mendelian randomization: the use of pleiotropic genetic variants to estimate causal effects

    American Journal of Epidemiology 181:251–260.

    https://doi.org/10.1093/aje/kwu283

    • PubMed
    • Google Scholar
13. 1. Bycroft C
    2. Freeman C
    3. Petkova D

    (2018) The UK Biobank resource with deep phenotyping and genomic data

    Nature 562:203–209.

    https://doi.org/10.1038/s41586-018-0579-z

    • Google Scholar
14. 1. Chen L
    2. Dong Y
    3. Bhagatwala J

    (2019) Effects of Vitamin D3 Supplementation on Epigenetic Aging in Overweight and Obese African Americans With Suboptimal Vitamin D Status: A Randomized Clinical Trial

    The Journals of Gerontology. Series A, Biological Sciences and Medical Sciences 74:91–98.

    https://doi.org/10.1093/gerona/gly223

    • Google Scholar
15. 1. Codd V
    2. Wang Q
    3. Allara E
    4. Musicha C
    5. Kaptoge S
    6. Stoma S
    7. Jiang T
    8. Hamby SE
    9. Braund PS
    10. Bountziouka V
    11. Budgeon CA
    12. Denniff M
    13. Swinfield C
    14. Papakonstantinou M
    15. Sheth S
    16. Nanus DE
    17. Warner SC
    18. Wang M
    19. Khera AV
    20. Eales J
    21. Ouwehand WH
    22. Thompson JR
    23. Di Angelantonio E
    24. Wood AM
    25. Butterworth AS
    26. Danesh JN
    27. Nelson CP
    28. Samani NJ

    (2021) Polygenic basis and biomedical consequences of telomere length variation

    Nature Genetics 53:1425–1433.

    https://doi.org/10.1038/s41588-021-00944-6

    • PubMed
    • Google Scholar
16. 1. Davey Smith G
    2. Ebrahim S

    (2003) ‘Mendelian randomization’: can genetic epidemiology contribute to understanding environmental determinants of disease

    International Journal of Epidemiology 32:1–22.

    https://doi.org/10.1093/ije/dyg070

    • PubMed
    • Google Scholar
17. 1. Davies NM
    2. Holmes MV
    3. Davey Smith G

    (2018) Reading Mendelian randomisation studies: a guide, glossary, and checklist for clinicians

    BMJ (Clinical Research Ed.) 362:k601.

    https://doi.org/10.1136/bmj.k601

    • PubMed
    • Google Scholar
18. 1. DeBoever C
    2. Tanigawa Y
    3. Aguirre M

    (2020) Assessing Digital Phenotyping to Enhance Genetic Studies of Human Diseases

    American Journal of Human Genetics 106:611–622.

    https://doi.org/10.1016/j.ajhg.2020.03.007

    • Google Scholar
19. 1. Didelez V
    2. Sheehan N

    (2007) Mendelian randomization as an instrumental variable approach to causal inference

    Statistical Methods in Medical Research 16:309–330.

    https://doi.org/10.1177/0962280206077743

    • Google Scholar
20. 1. Dugué P-A
    2. Bassett JK
    3. Joo JE
    4. Jung C-H
    5. Ming Wong E
    6. Moreno-Betancur M
    7. Schmidt D
    8. Makalic E
    9. Li S
    10. Severi G
    11. Hodge AM
    12. Buchanan DD
    13. English DR
    14. Hopper JL
    15. Southey MC
    16. Giles GG
    17. Milne RL

    (2018) DNA methylation-based biological aging and cancer risk and survival: Pooled analysis of seven prospective studies

    International Journal of Cancer 142:1611–1619.

    https://doi.org/10.1002/ijc.31189

    • PubMed
    • Google Scholar
21. 1. Dugue PA
    2. Bassett JK
    3. Wong EM

    (2021) Biological Aging Measures Based on Blood DNA Methylation and Risk of Cancer: A Prospective Study

    JNCI Cancer Spectr 5:pkaa109.

    https://doi.org/10.1093/jncics/pkaa109

    • Google Scholar
22. 1. Durso DF
    2. Bacalini MG
    3. Sala C
    4. Pirazzini C
    5. Marasco E
    6. Bonafé M
    7. do Valle ÍF
    8. Gentilini D
    9. Castellani G
    10. Faria AMC
    11. Franceschi C
    12. Garagnani P
    13. Nardini C

    (2017) Acceleration of leukocytes’ epigenetic age as an early tumor- and sex-specific marker of breast and colorectal cancer

    Oncotarget 8:23237–23245.

    https://doi.org/10.18632/oncotarget.15573

    • PubMed
    • Google Scholar
23. Software

    1. Elsworth BL
    2. Mitchell R
    3. Raistrick CA

    (2019) MRC IEU UK Biobank GWAS pipeline

    Version 2.

    https://doi.org/10.5523/bris.pnoat8cxo0u52p6ynfaekeigi
24. 1. Fahy GM
    2. Brooke RT
    3. Watson JP

    (2019) Reversal of epigenetic aging and immunosenescent trends in humans

    Aging Cell 18:e13028.

    https://doi.org/10.1111/acel.13028

    • Google Scholar
25. Website

    1. FinnGen

    (2021) FinnGen Documentation of R5 Release

    Accessed August 6, 2021.

    https://finngen.gitbook.io/documentation/
26. 1. Fitzgerald KN
    2. Hodges R
    3. Hanes D

    (2021) Potential reversal of epigenetic age using a diet and lifestyle intervention: a pilot randomized clinical trial

    AGING 13:9419–9432.

    https://doi.org/10.18632/aging.202913

    • Google Scholar
27. 1. Foley CN
    2. Mason AM
    3. Kirk PDW

    (2021) MR-Clust: clustering of genetic variants in Mendelian randomization with similar causal estimates

    Bioinformatics (Oxford, England) 37:531–541.

    https://doi.org/10.1093/bioinformatics/btaa778

    • Google Scholar
28. 1. Fransquet PD
    2. Wrigglesworth J
    3. Woods RL
    4. Ernst ME
    5. Ryan J

    (2019) The epigenetic clock as a predictor of disease and mortality risk: a systematic review and meta-analysis

    Clinical Epigenetics 11:62.

    https://doi.org/10.1186/s13148-019-0656-7

    • PubMed
    • Google Scholar
29. 1. Gao Y
    2. Wei Y
    3. Zhou X
    4. Huang S
    5. Zhao H
    6. Zeng P

    (2020) Assessing the Relationship Between Leukocyte Telomere Length and Cancer Risk/Mortality in UK Biobank and TCGA Datasets With the Genetic Risk Score and Mendelian Randomization Approaches

    Frontiers in Genetics 11:583106.

    https://doi.org/10.3389/fgene.2020.583106

    • PubMed
    • Google Scholar
30. 1. Gensous N
    2. Garagnani P
    3. Santoro A

    (2020) One-year Mediterranean diet promotes epigenetic rejuvenation with country- and sex-specific effects: a pilot study from the NU-AGE project

    GeroScience 42:687–701.

    https://doi.org/10.1007/s11357-019-00149-0

    • Google Scholar
31. 1. Grodstein F
    2. Lemos B
    3. Yu L

    (2020) Characteristics of Epigenetic Clocks Across Blood and Brain Tissue in Older Women and Men

    Frontiers in Neuroscience 14:555307.

    https://doi.org/10.3389/fnins.2020.555307

    • Google Scholar
32. 1. Hannum G
    2. Guinney J
    3. Zhao L
    4. Zhang L
    5. Hughes G
    6. Sadda S
    7. Klotzle B
    8. Bibikova M
    9. Fan J-B
    10. Gao Y
    11. Deconde R
    12. Chen M
    13. Rajapakse I
    14. Friend S
    15. Ideker T
    16. Zhang K

    (2013) Genome-wide methylation profiles reveal quantitative views of human aging rates

    Molecular Cell 49:359–367.

    https://doi.org/10.1016/j.molcel.2012.10.016

    • PubMed
    • Google Scholar
33. 1. Hartwig FP
    2. Davey Smith G
    3. Bowden J

    (2017) Robust inference in summary data Mendelian randomization via the zero modal pleiotropy assumption

    International Journal of Epidemiology 46:1985–1998.

    https://doi.org/10.1093/ije/dyx102

    • PubMed
    • Google Scholar
34. 1. Hemani G
    2. Tilling K
    3. Davey Smith G

    (2017) Orienting the causal relationship between imprecisely measured traits using GWAS summary data

    PLOS Genetics 13:1007081.

    https://doi.org/10.1371/journal.pgen.1007081

    • PubMed
    • Google Scholar
35. 1. Hemani G
    2. Zheng J
    3. Elsworth B

    (2018) The MR-Base platform supports systematic causal inference across the human phenome

    eLife 7:e34408.

    https://doi.org/10.7554/eLife.34408

    • Google Scholar
36. 1. Hillary RF
    2. Stevenson AJ
    3. McCartney DL
    4. Campbell A
    5. Walker RM
    6. Howard DM
    7. Ritchie CW
    8. Horvath S
    9. Hayward C
    10. McIntosh AM
    11. Porteous DJ
    12. Deary IJ
    13. Evans KL
    14. Marioni RE

    (2020) Epigenetic measures of ageing predict the prevalence and incidence of leading causes of death and disease burden

    Clinical Epigenetics 12:115.

    https://doi.org/10.1186/s13148-020-00905-6

    • PubMed
    • Google Scholar
37. 1. Horvath S

    (2013) DNA methylation age of human tissues and cell types

    Genome Biology 14:R115.

    https://doi.org/10.1186/gb-2013-14-10-r115

    • PubMed
    • Google Scholar
38. 1. Hung RJ
    2. McKay JD
    3. Gaborieau V
    4. Boffetta P
    5. Hashibe M
    6. Zaridze D
    7. Mukeria A
    8. Szeszenia-Dabrowska N
    9. Lissowska J
    10. Rudnai P
    11. Fabianova E
    12. Mates D
    13. Bencko V
    14. Foretova L
    15. Janout V
    16. Chen C
    17. Goodman G
    18. Field JK
    19. Liloglou T
    20. Xinarianos G
    21. Cassidy A
    22. McLaughlin J
    23. Liu G
    24. Narod S
    25. Krokan HE
    26. Skorpen F
    27. Elvestad MB
    28. Hveem K
    29. Vatten L
    30. Linseisen J
    31. Clavel-Chapelon F
    32. Vineis P
    33. Bueno-de-Mesquita HB
    34. Lund E
    35. Martinez C
    36. Bingham S
    37. Rasmuson T
    38. Hainaut P
    39. Riboli E
    40. Ahrens W
    41. Benhamou S
    42. Lagiou P
    43. Trichopoulos D
    44. Holcátová I
    45. Merletti F
    46. Kjaerheim K
    47. Agudo A
    48. Macfarlane G
    49. Talamini R
    50. Simonato L
    51. Lowry R
    52. Conway DI
    53. Znaor A
    54. Healy C
    55. Zelenika D
    56. Boland A
    57. Delepine M
    58. Foglio M
    59. Lechner D
    60. Matsuda F
    61. Blanche H
    62. Gut I
    63. Heath S
    64. Lathrop M
    65. Brennan P

    (2008) A susceptibility locus for lung cancer maps to nicotinic acetylcholine receptor subunit genes on 15q25

    Nature 452:633–637.

    https://doi.org/10.1038/nature06885

    • PubMed
    • Google Scholar
39. 1. Huyghe JR
    2. Bien SA
    3. Harrison TA
    4. Kang HM
    5. Chen S
    6. Schmit SL
    7. Conti DV
    8. Qu C
    9. Jeon J
    10. Edlund CK
    11. Greenside P
    12. Wainberg M
    13. Schumacher FR
    14. Smith JD
    15. Levine DM
    16. Nelson SC
    17. Sinnott-Armstrong NA
    18. Albanes D
    19. Alonso MH
    20. Anderson K
    21. Arnau-Collell C
    22. Arndt V
    23. Bamia C
    24. Banbury BL
    25. Baron JA
    26. Berndt SI
    27. Bézieau S
    28. Bishop DT
    29. Boehm J
    30. Boeing H
    31. Brenner H
    32. Brezina S
    33. Buch S
    34. Buchanan DD
    35. Burnett-Hartman A
    36. Butterbach K
    37. Caan BJ
    38. Campbell PT
    39. Carlson CS
    40. Castellví-Bel S
    41. Chan AT
    42. Chang-Claude J
    43. Chanock SJ
    44. Chirlaque M-D
    45. Cho SH
    46. Connolly CM
    47. Cross AJ
    48. Cuk K
    49. Curtis KR
    50. de la Chapelle A
    51. Doheny KF
    52. Duggan D
    53. Easton DF
    54. Elias SG
    55. Elliott F
    56. English DR
    57. Feskens EJM
    58. Figueiredo JC
    59. Fischer R
    60. FitzGerald LM
    61. Forman D
    62. Gala M
    63. Gallinger S
    64. Gauderman WJ
    65. Giles GG
    66. Gillanders E
    67. Gong J
    68. Goodman PJ
    69. Grady WM
    70. Grove JS
    71. Gsur A
    72. Gunter MJ
    73. Haile RW
    74. Hampe J
    75. Hampel H
    76. Harlid S
    77. Hayes RB
    78. Hofer P
    79. Hoffmeister M
    80. Hopper JL
    81. Hsu W-L
    82. Huang W-Y
    83. Hudson TJ
    84. Hunter DJ
    85. Ibañez-Sanz G
    86. Idos GE
    87. Ingersoll R
    88. Jackson RD
    89. Jacobs EJ
    90. Jenkins MA
    91. Joshi AD
    92. Joshu CE
    93. Keku TO
    94. Key TJ
    95. Kim HR
    96. Kobayashi E
    97. Kolonel LN
    98. Kooperberg C
    99. Kühn T
    100. Küry S
    101. Kweon S-S
    102. Larsson SC
    103. Laurie CA
    104. Le Marchand L
    105. Leal SM
    106. Lee SC
    107. Lejbkowicz F
    108. Lemire M
    109. Li CI
    110. Li L
    111. Lieb W
    112. Lin Y
    113. Lindblom A
    114. Lindor NM
    115. Ling H
    116. Louie TL
    117. Männistö S
    118. Markowitz SD
    119. Martín V
    120. Masala G
    121. McNeil CE
    122. Melas M
    123. Milne RL
    124. Moreno L
    125. Murphy N
    126. Myte R
    127. Naccarati A
    128. Newcomb PA
    129. Offit K
    130. Ogino S
    131. Onland-Moret NC
    132. Pardini B
    133. Parfrey PS
    134. Pearlman R
    135. Perduca V
    136. Pharoah PDP
    137. Pinchev M
    138. Platz EA
    139. Prentice RL
    140. Pugh E
    141. Raskin L
    142. Rennert G
    143. Rennert HS
    144. Riboli E
    145. Rodríguez-Barranco M
    146. Romm J
    147. Sakoda LC
    148. Schafmayer C
    149. Schoen RE
    150. Seminara D
    151. Shah M
    152. Shelford T
    153. Shin M-H
    154. Shulman K
    155. Sieri S
    156. Slattery ML
    157. Southey MC
    158. Stadler ZK
    159. Stegmaier C
    160. Su Y-R
    161. Tangen CM
    162. Thibodeau SN
    163. Thomas DC
    164. Thomas SS
    165. Toland AE
    166. Trichopoulou A
    167. Ulrich CM
    168. Van Den Berg DJ
    169. van Duijnhoven FJB
    170. Van Guelpen B
    171. van Kranen H
    172. Vijai J
    173. Visvanathan K
    174. Vodicka P
    175. Vodickova L
    176. Vymetalkova V
    177. Weigl K
    178. Weinstein SJ
    179. White E
    180. Win AK
    181. Wolf CR
    182. Wolk A
    183. Woods MO
    184. Wu AH
    185. Zaidi SH
    186. Zanke BW
    187. Zhang Q
    188. Zheng W
    189. Scacheri PC
    190. Potter JD
    191. Bassik MC
    192. Kundaje A
    193. Casey G
    194. Moreno V
    195. Abecasis GR
    196. Nickerson DA
    197. Gruber SB
    198. Hsu L
    199. Peters U

    (2019) Discovery of common and rare genetic risk variants for colorectal cancer

    Nature Genetics 51:76–87.

    https://doi.org/10.1038/s41588-018-0286-6

    • PubMed
    • Google Scholar
40. 1. Huyghe JR
    2. Harrison TA
    3. Bien SA
    4. Hampel H
    5. Figueiredo JC
    6. Schmit SL
    7. Conti DV
    8. Chen S
    9. Qu C
    10. Lin Y
    11. Barfield R
    12. Baron JA
    13. Cross AJ
    14. Diergaarde B
    15. Duggan D
    16. Harlid S
    17. Imaz L
    18. Kang HM
    19. Levine DM
    20. Perduca V
    21. Perez-Cornago A
    22. Sakoda LC
    23. Schumacher FR
    24. Slattery ML
    25. Toland AE
    26. van Duijnhoven FJB
    27. Van Guelpen B
    28. Agudo A
    29. Albanes D
    30. Alonso MH
    31. Anderson K
    32. Arnau-Collell C
    33. Arndt V
    34. Banbury BL
    35. Bassik MC
    36. Berndt SI
    37. Bézieau S
    38. Bishop DT
    39. Boehm J
    40. Boeing H
    41. Boutron-Ruault M-C
    42. Brenner H
    43. Brezina S
    44. Buch S
    45. Buchanan DD
    46. Burnett-Hartman A
    47. Caan BJ
    48. Campbell PT
    49. Carr PR
    50. Castells A
    51. Castellví-Bel S
    52. Chan AT
    53. Chang-Claude J
    54. Chanock SJ
    55. Curtis KR
    56. de la Chapelle A
    57. Easton DF
    58. English DR
    59. Feskens EJM
    60. Gala M
    61. Gallinger SJ
    62. Gauderman WJ
    63. Giles GG
    64. Goodman PJ
    65. Grady WM
    66. Grove JS
    67. Gsur A
    68. Gunter MJ
    69. Haile RW
    70. Hampe J
    71. Hoffmeister M
    72. Hopper JL
    73. Hsu W-L
    74. Huang W-Y
    75. Hudson TJ
    76. Jenab M
    77. Jenkins MA
    78. Joshi AD
    79. Keku TO
    80. Kooperberg C
    81. Kühn T
    82. Küry S
    83. Le Marchand L
    84. Lejbkowicz F
    85. Li CI
    86. Li L
    87. Lieb W
    88. Lindblom A
    89. Lindor NM
    90. Männistö S
    91. Markowitz SD
    92. Milne RL
    93. Moreno L
    94. Murphy N
    95. Nassir R
    96. Offit K
    97. Ogino S
    98. Panico S
    99. Parfrey PS
    100. Pearlman R
    101. Pharoah PDP
    102. Phipps AI
    103. Platz EA
    104. Potter JD
    105. Prentice RL
    106. Qi L
    107. Raskin L
    108. Rennert G
    109. Rennert HS
    110. Riboli E
    111. Schafmayer C
    112. Schoen RE
    113. Seminara D
    114. Song M
    115. Su Y-R
    116. Tangen CM
    117. Thibodeau SN
    118. Thomas DC
    119. Trichopoulou A
    120. Ulrich CM
    121. Visvanathan K
    122. Vodicka P
    123. Vodickova L
    124. Vymetalkova V
    125. Weigl K
    126. Weinstein SJ
    127. White E
    128. Wolk A
    129. Woods MO
    130. Wu AH
    131. Abecasis GR
    132. Nickerson DA
    133. Scacheri PC
    134. Kundaje A
    135. Casey G
    136. Gruber SB
    137. Hsu L
    138. Moreno V
    139. Hayes RB
    140. Newcomb PA
    141. Peters U

    (2021) Genetic architectures of proximal and distal colorectal cancer are partly distinct

    Gut 70:1325–1334.

    https://doi.org/10.1136/gutjnl-2020-321534

    • PubMed
    • Google Scholar
41. Software

    1. Jagerroos V

    (2020) nightingalehealth/ggforestplot, version 547617e

    GitHub.

    https://github.com/nightingalehealth/ggforestplot
42. 1. Kresovich JK
    2. Xu Z
    3. O’Brien KM
    4. Weinberg CR
    5. Sandler DP
    6. Taylor JA

    (2019a) Epigenetic mortality predictors and incidence of breast cancer

    AGING 11:11975–11987.

    https://doi.org/10.18632/aging.102523

    • PubMed
    • Google Scholar
43. 1. Kresovich JK
    2. Xu Z
    3. O’Brien KM
    4. Weinberg CR
    5. Sandler DP
    6. Taylor JA

    (2019b) Methylation-Based Biological Age and Breast Cancer Risk

    Journal of the National Cancer Institute 111:1051–1058.

    https://doi.org/10.1093/jnci/djz020

    • PubMed
    • Google Scholar
44. 1. Kuo C-L
    2. Pilling LC
    3. Kuchel GA
    4. Ferrucci L
    5. Melzer D

    (2019) Telomere length and aging-related outcomes in humans: A Mendelian randomization study in 261,000 older participants

    Aging Cell 18:e13017.

    https://doi.org/10.1111/acel.13017

    • PubMed
    • Google Scholar
45. 1. Lau C-HE
    2. Robinson O

    (2021) DNA methylation age as a biomarker for cancer

    International Journal of Cancer 148:2652–2663.

    https://doi.org/10.1002/ijc.33451

    • PubMed
    • Google Scholar
46. 1. Levine ME
    2. Hosgood HD
    3. Chen B
    4. Absher D
    5. Assimes T
    6. Horvath S

    (2015) DNA methylation age of blood predicts future onset of lung cancer in the women’s health initiative

    AGING 7:690–700.

    https://doi.org/10.18632/aging.100809

    • PubMed
    • Google Scholar
47. 1. Levine ME
    2. Lu AT
    3. Quach A
    4. Chen BH
    5. Assimes TL
    6. Bandinelli S
    7. Hou L
    8. Baccarelli AA
    9. Stewart JD
    10. Li Y
    11. Whitsel EA
    12. Wilson JG
    13. Reiner AP
    14. Aviv A
    15. Lohman K
    16. Liu Y
    17. Ferrucci L
    18. Horvath S

    (2018) An epigenetic biomarker of aging for lifespan and healthspan

    AGING 10:573–591.

    https://doi.org/10.18632/aging.101414

    • PubMed
    • Google Scholar
48. 1. Liu Z
    2. Leung D
    3. Thrush K
    4. Zhao W
    5. Ratliff S
    6. Tanaka T
    7. Schmitz LL
    8. Smith JA
    9. Ferrucci L
    10. Levine ME

    (2020) Underlying features of epigenetic aging clocks in vivo and in vitro

    Aging Cell 19:e13229.

    https://doi.org/10.1111/acel.13229

    • PubMed
    • Google Scholar
49. 1. Loh P-R
    2. Tucker G
    3. Bulik-Sullivan BK
    4. Vilhjálmsson BJ
    5. Finucane HK
    6. Salem RM
    7. Chasman DI
    8. Ridker PM
    9. Neale BM
    10. Berger B
    11. Patterson N
    12. Price AL

    (2015) Efficient Bayesian mixed-model analysis increases association power in large cohorts

    Nature Genetics 47:284–290.

    https://doi.org/10.1038/ng.3190

    • PubMed
    • Google Scholar
50. 1. Lu AT
    2. Quach A
    3. Wilson JG
    4. Reiner AP
    5. Aviv A
    6. Raj K
    7. Hou L
    8. Baccarelli AA
    9. Li Y
    10. Stewart JD
    11. Whitsel EA
    12. Assimes TL
    13. Ferrucci L
    14. Horvath S

    (2019a) DNA methylation GrimAge strongly predicts lifespan and healthspan

    AGING 11:303–327.

    https://doi.org/10.18632/aging.101684

    • PubMed
    • Google Scholar
51. 1. Lu AT
    2. Seeboth A
    3. Tsai PC
    4. Sun D
    5. Quach A
    6. Reiner AP
    7. Kooperberg C
    8. Ferrucci L
    9. Hou L
    10. Baccarelli AA
    11. Li Y
    12. Harris SE
    13. Corley J
    14. Taylor A
    15. Deary IJ
    16. Stewart JD
    17. Whitsel EA
    18. Assimes TL
    19. Chen W
    20. Li S
    21. Mangino M
    22. Bell JT
    23. Wilson JG
    24. Aviv A
    25. Marioni RE
    26. Raj K
    27. Horvath S

    (2019b) DNA methylation-based estimator of telomere length

    AGING 11:5895–5923.

    https://doi.org/10.18632/aging.102173

    • PubMed
    • Google Scholar
52. 1. McCartney DL
    2. Min JL
    3. Richmond RC
    4. Lu AT
    5. Sobczyk MK
    6. Davies G
    7. Broer L
    8. Guo X
    9. Jeong A
    10. Jung J
    11. Kasela S
    12. Katrinli S
    13. Kuo PL
    14. Matias-Garcia PR
    15. Mishra PP
    16. Nygaard M
    17. Palviainen T
    18. Patki A
    19. Raffield LM
    20. Ratliff SM
    21. Richardson TG
    22. Robinson O
    23. Soerensen M
    24. Sun D
    25. Tsai PC
    26. van der Zee MD
    27. Walker RM
    28. Wang X
    29. Wang Y
    30. Xia R
    31. Xu Z
    32. Yao J
    33. Zhao W
    34. Correa A
    35. Boerwinkle E
    36. Dugué PA
    37. Durda P
    38. Elliott HR
    39. Gieger C
    40. de Geus EJC
    41. Harris SE
    42. Hemani G
    43. Imboden M
    44. Kähönen M
    45. Kardia SLR
    46. Kresovich JK
    47. Li S
    48. Lunetta KL
    49. Mangino M
    50. Mason D
    51. McIntosh AM
    52. Mengel-From J
    53. Moore AZ
    54. Murabito JM
    55. Ollikainen M
    56. Pankow JS
    57. Pedersen NL
    58. Peters A
    59. Polidoro S
    60. Porteous DJ
    61. Raitakari O
    62. Rich SS
    63. Sandler DP
    64. Sillanpää E
    65. Smith AK
    66. Southey MC
    67. Strauch K
    68. Tiwari H
    69. Tanaka T
    70. Tillin T
    71. Uitterlinden AG
    72. Van Den Berg DJ
    73. van Dongen J
    74. Wilson JG
    75. Wright J
    76. Yet I
    77. Arnett D
    78. Bandinelli S
    79. Bell JT
    80. Binder AM
    81. Boomsma DI
    82. Chen W
    83. Christensen K
    84. Conneely KN
    85. Elliott P
    86. Ferrucci L
    87. Fornage M
    88. Hägg S
    89. Hayward C
    90. Irvin M
    91. Kaprio J
    92. Lawlor DA
    93. Lehtimäki T
    94. Lohoff FW
    95. Milani L
    96. Milne RL
    97. Probst-Hensch N
    98. Reiner AP
    99. Ritz B
    100. Rotter JI
    101. Smith JA
    102. Taylor JA
    103. van Meurs JBJ
    104. Vineis P
    105. Waldenberger M
    106. Deary IJ
    107. Relton CL
    108. Horvath S
    109. Marioni RE
    110. Genetics of DNA Methylation Consortium
    111. NHLBI Trans-Omics for Precision Medicine TOPMed Consortium

    (2021) Genome-wide association studies identify 137 genetic loci for DNA methylation biomarkers of aging

    Genome Biology 22:194.

    https://doi.org/10.1186/s13059-021-02398-9

    • PubMed
    • Google Scholar
53. 1. McCrory C
    2. Fiorito G
    3. Hernandez B

    (2021) GrimAge Outperforms Other Epigenetic Clocks in the Prediction of Age-Related Clinical Phenotypes and All-Cause Mortality

    The Journals of Gerontology. Series A, Biological Sciences and Medical Sciences 76:741–749.

    https://doi.org/10.1093/gerona/glaa286

    • Google Scholar
54. 1. Michailidou K
    2. Hall P
    3. Gonzalez-Neira A

    (2013) Large-scale genotyping identifies 41 new loci associated with breast cancer risk

    Nature Genetics 45:353–361.

    https://doi.org/10.1038/ng.2563

    • Google Scholar
55. 1. Michailidou K
    2. Beesley J
    3. Lindstrom S

    (2015) Genome-wide association analysis of more than 120,000 individuals identifies 15 new susceptibility loci for breast cancer

    Nature Genetics 47:373–380.

    https://doi.org/10.1038/ng.3242

    • Google Scholar
56. 1. Michailidou K
    2. Lindstrom S
    3. Dennis J

    (2017) Association analysis identifies 65 new breast cancer risk loci

    Nature 551:92–94.

    https://doi.org/10.1038/nature24284

    • Google Scholar
57. 1. Milne RL
    2. Kuchenbaecker KB
    3. Michailidou K
    4. Beesley J
    5. Kar S
    6. Lindström S
    7. Hui S
    8. Lemaçon A
    9. Soucy P
    10. Dennis J
    11. Jiang X
    12. Rostamianfar A
    13. Finucane H
    14. Bolla MK
    15. McGuffog L
    16. Wang Q
    17. Aalfs CM
    18. ABCTB Investigators
    19. Adams M
    20. Adlard J
    21. Agata S
    22. Ahmed S
    23. Ahsan H
    24. Aittomäki K
    25. Al-Ejeh F
    26. Allen J
    27. Ambrosone CB
    28. Amos CI
    29. Andrulis IL
    30. Anton-Culver H
    31. Antonenkova NN
    32. Arndt V
    33. Arnold N
    34. Aronson KJ
    35. Auber B
    36. Auer PL
    37. Ausems MGEM
    38. Azzollini J
    39. Bacot F
    40. Balmaña J
    41. Barile M
    42. Barjhoux L
    43. Barkardottir RB
    44. Barrdahl M
    45. Barnes D
    46. Barrowdale D
    47. Baynes C
    48. Beckmann MW
    49. Benitez J
    50. Bermisheva M
    51. Bernstein L
    52. Bignon Y-J
    53. Blazer KR
    54. Blok MJ
    55. Blomqvist C
    56. Blot W
    57. Bobolis K
    58. Boeckx B
    59. Bogdanova NV
    60. Bojesen A
    61. Bojesen SE
    62. Bonanni B
    63. Børresen-Dale A-L
    64. Bozsik A
    65. Bradbury AR
    66. Brand JS
    67. Brauch H
    68. Brenner H
    69. Bressac-de Paillerets B
    70. Brewer C
    71. Brinton L
    72. Broberg P
    73. Brooks-Wilson A
    74. Brunet J
    75. Brüning T
    76. Burwinkel B
    77. Buys SS
    78. Byun J
    79. Cai Q
    80. Caldés T
    81. Caligo MA
    82. Campbell I
    83. Canzian F
    84. Caron O
    85. Carracedo A
    86. Carter BD
    87. Castelao JE
    88. Castera L
    89. Caux-Moncoutier V
    90. Chan SB
    91. Chang-Claude J
    92. Chanock SJ
    93. Chen X
    94. Cheng T-YD
    95. Chiquette J
    96. Christiansen H
    97. Claes KBM
    98. Clarke CL
    99. Conner T
    100. Conroy DM
    101. Cook J
    102. Cordina-Duverger E
    103. Cornelissen S
    104. Coupier I
    105. Cox A
    106. Cox DG
    107. Cross SS
    108. Cuk K
    109. Cunningham JM
    110. Czene K
    111. Daly MB
    112. Damiola F
    113. Darabi H
    114. Davidson R
    115. De Leeneer K
    116. Devilee P
    117. Dicks E
    118. Diez O
    119. Ding YC
    120. Ditsch N
    121. Doheny KF
    122. Domchek SM
    123. Dorfling CM
    124. Dörk T
    125. Dos-Santos-Silva I
    126. Dubois S
    127. Dugué P-A
    128. Dumont M
    129. Dunning AM
    130. Durcan L
    131. Dwek M
    132. Dworniczak B
    133. Eccles D
    134. Eeles R
    135. Ehrencrona H
    136. Eilber U
    137. Ejlertsen B
    138. Ekici AB
    139. Eliassen AH
    140. EMBRACE
    141. Engel C
    142. Eriksson M
    143. Fachal L
    144. Faivre L
    145. Fasching PA
    146. Faust U
    147. Figueroa J
    148. Flesch-Janys D
    149. Fletcher O
    150. Flyger H
    151. Foulkes WD
    152. Friedman E
    153. Fritschi L
    154. Frost D
    155. Gabrielson M
    156. Gaddam P
    157. Gammon MD
    158. Ganz PA
    159. Gapstur SM
    160. Garber J
    161. Garcia-Barberan V
    162. García-Sáenz JA
    163. Gaudet MM
    164. Gauthier-Villars M
    165. Gehrig A
    166. GEMO Study Collaborators
    167. Georgoulias V
    168. Gerdes A-M
    169. Giles GG
    170. Glendon G
    171. Godwin AK
    172. Goldberg MS
    173. Goldgar DE
    174. González-Neira A
    175. Goodfellow P
    176. Greene MH
    177. Alnæs GIG
    178. Grip M
    179. Gronwald J
    180. Grundy A
    181. Gschwantler-Kaulich D
    182. Guénel P
    183. Guo Q
    184. Haeberle L
    185. Hahnen E
    186. Haiman CA
    187. Håkansson N
    188. Hallberg E
    189. Hamann U
    190. Hamel N
    191. Hankinson S
    192. Hansen TVO
    193. Harrington P
    194. Hart SN
    195. Hartikainen JM
    196. Healey CS
    197. HEBON
    198. Hein A
    199. Helbig S
    200. Henderson A
    201. Heyworth J
    202. Hicks B
    203. Hillemanns P
    204. Hodgson S
    205. Hogervorst FB
    206. Hollestelle A
    207. Hooning MJ
    208. Hoover B
    209. Hopper JL
    210. Hu C
    211. Huang G
    212. Hulick PJ
    213. Humphreys K
    214. Hunter DJ
    215. Imyanitov EN
    216. Isaacs C
    217. Iwasaki M
    218. Izatt L
    219. Jakubowska A
    220. James P
    221. Janavicius R
    222. Janni W
    223. Jensen UB
    224. John EM
    225. Johnson N
    226. Jones K
    227. Jones M
    228. Jukkola-Vuorinen A
    229. Kaaks R
    230. Kabisch M
    231. Kaczmarek K
    232. Kang D
    233. Kast K
    234. kConFab/AOCS Investigators
    235. Keeman R
    236. Kerin MJ
    237. Kets CM
    238. Keupers M
    239. Khan S
    240. Khusnutdinova E
    241. Kiiski JI
    242. Kim S-W
    243. Knight JA
    244. Konstantopoulou I
    245. Kosma V-M
    246. Kristensen VN
    247. Kruse TA
    248. Kwong A
    249. Lænkholm A-V
    250. Laitman Y
    251. Lalloo F
    252. Lambrechts D
    253. Landsman K
    254. Lasset C
    255. Lazaro C
    256. Le Marchand L
    257. Lecarpentier J
    258. Lee A
    259. Lee E
    260. Lee JW
    261. Lee MH
    262. Lejbkowicz F
    263. Lesueur F
    264. Li J
    265. Lilyquist J
    266. Lincoln A
    267. Lindblom A
    268. Lissowska J
    269. Lo W-Y
    270. Loibl S
    271. Long J
    272. Loud JT
    273. Lubinski J
    274. Luccarini C
    275. Lush M
    276. MacInnis RJ
    277. Maishman T
    278. Makalic E
    279. Kostovska IM
    280. Malone KE
    281. Manoukian S
    282. Manson JE
    283. Margolin S
    284. Martens JWM
    285. Martinez ME
    286. Matsuo K
    287. Mavroudis D
    288. Mazoyer S
    289. McLean C
    290. Meijers-Heijboer H
    291. Menéndez P
    292. Meyer J
    293. Miao H
    294. Miller A
    295. Miller N
    296. Mitchell G
    297. Montagna M
    298. Muir K
    299. Mulligan AM
    300. Mulot C
    301. Nadesan S
    302. Nathanson KL
    303. NBSC Collaborators
    304. Neuhausen SL
    305. Nevanlinna H
    306. Nevelsteen I
    307. Niederacher D
    308. Nielsen SF
    309. Nordestgaard BG
    310. Norman A
    311. Nussbaum RL
    312. Olah E
    313. Olopade OI
    314. Olson JE
    315. Olswold C
    316. Ong K-R
    317. Oosterwijk JC
    318. Orr N
    319. Osorio A
    320. Pankratz VS
    321. Papi L
    322. Park-Simon T-W
    323. Paulsson-Karlsson Y
    324. Lloyd R
    325. Pedersen IS
    326. Peissel B
    327. Peixoto A
    328. Perez JIA
    329. Peterlongo P
    330. Peto J
    331. Pfeiler G
    332. Phelan CM
    333. Pinchev M
    334. Plaseska-Karanfilska D
    335. Poppe B
    336. Porteous ME
    337. Prentice R
    338. Presneau N
    339. Prokofieva D
    340. Pugh E
    341. Pujana MA
    342. Pylkäs K
    343. Rack B
    344. Radice P
    345. Rahman N
    346. Rantala J
    347. Rappaport-Fuerhauser C
    348. Rennert G
    349. Rennert HS
    350. Rhenius V
    351. Rhiem K
    352. Richardson A
    353. Rodriguez GC
    354. Romero A
    355. Romm J
    356. Rookus MA
    357. Rudolph A
    358. Ruediger T
    359. Saloustros E
    360. Sanders J
    361. Sandler DP
    362. Sangrajrang S
    363. Sawyer EJ
    364. Schmidt DF
    365. Schoemaker MJ
    366. Schumacher F
    367. Schürmann P
    368. Schwentner L
    369. Scott C
    370. Scott RJ
    371. Seal S
    372. Senter L
    373. Seynaeve C
    374. Shah M
    375. Sharma P
    376. Shen C-Y
    377. Sheng X
    378. Shimelis H
    379. Shrubsole MJ
    380. Shu X-O
    381. Side LE
    382. Singer CF
    383. Sohn C
    384. Southey MC
    385. Spinelli JJ
    386. Spurdle AB
    387. Stegmaier C
    388. Stoppa-Lyonnet D
    389. Sukiennicki G
    390. Surowy H
    391. Sutter C
    392. Swerdlow A
    393. Szabo CI
    394. Tamimi RM
    395. Tan YY
    396. Taylor JA
    397. Tejada M-I
    398. Tengström M
    399. Teo SH
    400. Terry MB
    401. Tessier DC
    402. Teulé A
    403. Thöne K
    404. Thull DL
    405. Tibiletti MG
    406. Tihomirova L
    407. Tischkowitz M
    408. Toland AE
    409. Tollenaar RAEM
    410. Tomlinson I
    411. Tong L
    412. Torres D
    413. Tranchant M
    414. Truong T
    415. Tucker K
    416. Tung N
    417. Tyrer J
    418. Ulmer H-U
    419. Vachon C
    420. van Asperen CJ
    421. Van Den Berg D
    422. van den Ouweland AMW
    423. van Rensburg EJ
    424. Varesco L
    425. Varon-Mateeva R
    426. Vega A
    427. Viel A
    428. Vijai J
    429. Vincent D
    430. Vollenweider J
    431. Walker L
    432. Wang Z
    433. Wang-Gohrke S
    434. Wappenschmidt B
    435. Weinberg CR
    436. Weitzel JN
    437. Wendt C
    438. Wesseling J
    439. Whittemore AS
    440. Wijnen JT
    441. Willett W
    442. Winqvist R
    443. Wolk A
    444. Wu AH
    445. Xia L
    446. Yang XR
    447. Yannoukakos D
    448. Zaffaroni D
    449. Zheng W
    450. Zhu B
    451. Ziogas A
    452. Ziv E
    453. Zorn KK
    454. Gago-Dominguez M
    455. Mannermaa A
    456. Olsson H
    457. Teixeira MR
    458. Stone J
    459. Offit K
    460. Ottini L
    461. Park SK
    462. Thomassen M
    463. Hall P
    464. Meindl A
    465. Schmutzler RK
    466. Droit A
    467. Bader GD
    468. Pharoah PDP
    469. Couch FJ
    470. Easton DF
    471. Kraft P
    472. Chenevix-Trench G
    473. García-Closas M
    474. Schmidt MK
    475. Antoniou AC
    476. Simard J

    (2017) Identification of ten variants associated with risk of estrogen-receptor-negative breast cancer

    Nature Genetics 49:1767–1778.

    https://doi.org/10.1038/ng.3785

    • PubMed
    • Google Scholar
58. Software

    1. Mitchell R
    2. Hemani G
    3. Dudding T

    (2019)

    UK Biobank Genetic Data: MRC-IEU Quality Control

    Version 2.
59. Software

    1. Morales Berstein F

    (2021) epiclocks_cancer, version 9a5c846

    GitHub.

    https://github.com/fernandam93/epiclocks_cancer
60. 1. Morrison J
    2. Knoblauch N
    3. Marcus JH

    (2020) Mendelian randomization accounting for correlated and uncorrelated pleiotropic effects using genome-wide summary statistics

    Nature Genetics 52:740–747.

    https://doi.org/10.1038/s41588-020-0631-4

    • Google Scholar
61. Software

    1. Morrison J

    (2021) jean997/cause, version 6257e6c

    GitHub.

    https://github.com/jean997/cause
62. Software

    1. Myers T

    (2021) CBIIT/LDlinkR, version dd48dc0

    GitHub.

    https://github.com/CBIIT/LDlinkR
63. 1. Palmer TM
    2. Lawlor DA
    3. Harbord RM

    (2012) Using multiple genetic variants as instrumental variables for modifiable risk factors

    Statistical Methods in Medical Research 21:223–242.

    https://doi.org/10.1177/0962280210394459

    • Google Scholar
64. Software

    1. Parker T

    (2021) MRCIEU/TwoSampleMR, version 2bb4947

    GitHub.

    https://github.com/MRCIEU/TwoSampleMR
65. 1. Phelan CM
    2. Kuchenbaecker KB
    3. Tyrer JP

    (2017) Identification of 12 new susceptibility loci for different histotypes of epithelial ovarian cancer

    Nature Genetics 49:680–691.

    https://doi.org/10.1038/ng.3826

    • Google Scholar
66. 1. Relton CL
    2. Davey Smith G

    (2012) Two-step epigenetic Mendelian randomization: a strategy for establishing the causal role of epigenetic processes in pathways to disease

    International Journal of Epidemiology 41:161–176.

    https://doi.org/10.1093/ije/dyr233

    • PubMed
    • Google Scholar
67. 1. Richardson TG
    2. Wang Q
    3. Sanderson E
    4. Mahajan A
    5. McCarthy MI
    6. Frayling TM
    7. Ala-Korpela M
    8. Sniderman A
    9. Davey Smith G
    10. Holmes MV

    (2021) Effects of apolipoprotein B on lifespan and risks of major diseases including type 2 diabetes: a mendelian randomisation analysis using outcomes in first-degree relatives

    The Lancet. Healthy Longevity 2:e317–e326.

    https://doi.org/10.1016/S2666-7568(21)00086-6

    • PubMed
    • Google Scholar
68. 1. Sanderson E
    2. Davey Smith G
    3. Windmeijer F
    4. Bowden J

    (2019) An examination of multivariable Mendelian randomization in the single-sample and two-sample summary data settings

    International Journal of Epidemiology 48:713–727.

    https://doi.org/10.1093/ije/dyy262

    • PubMed
    • Google Scholar
69. 1. Sanderson E
    2. Richardson TG
    3. Hemani G

    (2021) The use of negative control outcomes in Mendelian randomization to detect potential population stratification

    International Journal of Epidemiology 50:1350–1361.

    https://doi.org/10.1093/ije/dyaa288

    • Google Scholar
70. Software

    1. Schorsch E

    (2020) bulik/ldsc, version aa33296

    GitHub.

    https://github.com/bulik/ldsc
71. 1. Schumacher FR
    2. Al Olama AA
    3. Berndt SI
    4. Benlloch S
    5. Ahmed M
    6. Saunders EJ
    7. Dadaev T
    8. Leongamornlert D
    9. Anokian E
    10. Cieza-Borrella C
    11. Goh C
    12. Brook MN
    13. Sheng X
    14. Fachal L
    15. Dennis J
    16. Tyrer J
    17. Muir K
    18. Lophatananon A
    19. Stevens VL
    20. Gapstur SM
    21. Carter BD
    22. Tangen CM
    23. Goodman PJ
    24. Thompson IM
    25. Batra J
    26. Chambers S
    27. Moya L
    28. Clements J
    29. Horvath L
    30. Tilley W
    31. Risbridger GP
    32. Gronberg H
    33. Aly M
    34. Nordström T
    35. Pharoah P
    36. Pashayan N
    37. Schleutker J
    38. Tammela TLJ
    39. Sipeky C
    40. Auvinen A
    41. Albanes D
    42. Weinstein S
    43. Wolk A
    44. Håkansson N
    45. West CML
    46. Dunning AM
    47. Burnet N
    48. Mucci LA
    49. Giovannucci E
    50. Andriole GL
    51. Cussenot O
    52. Cancel-Tassin G
    53. Koutros S
    54. Beane Freeman LE
    55. Sorensen KD
    56. Orntoft TF
    57. Borre M
    58. Maehle L
    59. Grindedal EM
    60. Neal DE
    61. Donovan JL
    62. Hamdy FC
    63. Martin RM
    64. Travis RC
    65. Key TJ
    66. Hamilton RJ
    67. Fleshner NE
    68. Finelli A
    69. Ingles SA
    70. Stern MC
    71. Rosenstein BS
    72. Kerns SL
    73. Ostrer H
    74. Lu YJ
    75. Zhang HW
    76. Feng N
    77. Mao X
    78. Guo X
    79. Wang G
    80. Sun Z
    81. Giles GG
    82. Southey MC
    83. MacInnis RJ
    84. FitzGerald LM
    85. Kibel AS
    86. Drake BF
    87. Vega A
    88. Gómez-Caamaño A
    89. Szulkin R
    90. Eklund M
    91. Kogevinas M
    92. Llorca J
    93. Castaño-Vinyals G
    94. Penney KL
    95. Stampfer M
    96. Park JY
    97. Sellers TA
    98. Lin HY
    99. Stanford JL
    100. Cybulski C
    101. Wokolorczyk D
    102. Lubinski J
    103. Ostrander EA
    104. Geybels MS
    105. Nordestgaard BG
    106. Nielsen SF
    107. Weischer M
    108. Bisbjerg R
    109. Røder MA
    110. Iversen P
    111. Brenner H
    112. Cuk K
    113. Holleczek B
    114. Maier C
    115. Luedeke M
    116. Schnoeller T
    117. Kim J
    118. Logothetis CJ
    119. John EM
    120. Teixeira MR
    121. Paulo P
    122. Cardoso M
    123. Neuhausen SL
    124. Steele L
    125. Ding YC
    126. De Ruyck K
    127. De Meerleer G
    128. Ost P
    129. Razack A
    130. Lim J
    131. Teo SH
    132. Lin DW
    133. Newcomb LF
    134. Lessel D
    135. Gamulin M
    136. Kulis T
    137. Kaneva R
    138. Usmani N
    139. Singhal S
    140. Slavov C
    141. Mitev V
    142. Parliament M
    143. Claessens F
    144. Joniau S
    145. Van den Broeck T
    146. Larkin S
    147. Townsend PA
    148. Aukim-Hastie C
    149. Gago-Dominguez M
    150. Castelao JE
    151. Martinez ME
    152. Roobol MJ
    153. Jenster G
    154. van Schaik RHN
    155. Menegaux F
    156. Truong T
    157. Koudou YA
    158. Xu J
    159. Khaw KT
    160. Cannon-Albright L
    161. Pandha H
    162. Michael A
    163. Thibodeau SN
    164. McDonnell SK
    165. Schaid DJ
    166. Lindstrom S
    167. Turman C
    168. Ma J
    169. Hunter DJ
    170. Riboli E
    171. Siddiq A
    172. Canzian F
    173. Kolonel LN
    174. Le Marchand L
    175. Hoover RN
    176. Machiela MJ
    177. Cui Z
    178. Kraft P
    179. Amos CI
    180. Conti DV
    181. Easton DF
    182. Wiklund F
    183. Chanock SJ
    184. Henderson BE
    185. Kote-Jarai Z
    186. Haiman CA
    187. Eeles RA
    188. Profile Study
    189. IMPACT Study
    190. Canary PASS Investigators

    (2018) Association analyses of more than 140,000 men identify 63 new prostate cancer susceptibility loci

    Nature Genetics 50:928–936.

    https://doi.org/10.1038/s41588-018-0142-8

    • PubMed
    • Google Scholar
72. Software

    1. Schwarzer G

    (2022) guido-s/meta/, version af96027

    GitHub.

    https://github.com/guido-s/meta/
73. 1. Skrivankova VW
    2. Richmond RC
    3. Woolf BAR

    (2021) Strengthening the reporting of observational studies in epidemiology using mendelian randomisation (STROBE-MR): explanation and elaboration

    BMJ (Clinical Research Ed.) 10:2233.

    https://doi.org/10.1136/bmj.n2233

    • Google Scholar
74. 1. Sudlow C
    2. Gallacher J
    3. Allen N

    (2015) UK biobank: an open access resource for identifying the causes of a wide range of complex diseases of middle and old age

    PLOS Medicine 12:e1001779.

    https://doi.org/10.1371/journal.pmed.1001779

    • Google Scholar
75. 1. Sung H
    2. Ferlay J
    3. Siegel RL

    (2021) Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries

    CA: A Cancer Journal for Clinicians 71:209–249.

    https://doi.org/10.3322/caac.21660

    • Google Scholar
76. 1. Tajuddin SM
    2. Hernandez DG
    3. Chen BH

    (2019) Novel age-associated DNA methylation changes and epigenetic age acceleration in middle-aged African Americans and whites

    Clinical Epigenetics 11:119.

    https://doi.org/10.1186/s13148-019-0722-1

    • Google Scholar
77. 1. Telomeres Mendelian Randomization Collaboration
    2. Haycock PC
    3. Burgess S
    4. Nounu A
    5. Zheng J
    6. Okoli GN
    7. Bowden J
    8. Wade KH
    9. Timpson NJ
    10. Evans DM
    11. Willeit P
    12. Aviv A
    13. Gaunt TR
    14. Hemani G
    15. Mangino M
    16. Ellis HP
    17. Kurian KM
    18. Pooley KA
    19. Eeles RA
    20. Lee JE
    21. Fang S
    22. Chen WV
    23. Law MH
    24. Bowdler LM
    25. Iles MM
    26. Yang Q
    27. Worrall BB
    28. Markus HS
    29. Hung RJ
    30. Amos CI
    31. Spurdle AB
    32. Thompson DJ
    33. O’Mara TA
    34. Wolpin B
    35. Amundadottir L
    36. Stolzenberg-Solomon R
    37. Trichopoulou A
    38. Onland-Moret NC
    39. Lund E
    40. Duell EJ
    41. Canzian F
    42. Severi G
    43. Overvad K
    44. Gunter MJ
    45. Tumino R
    46. Svenson U
    47. van Rij A
    48. Baas AF
    49. Bown MJ
    50. Samani NJ
    51. van t’Hof FNG
    52. Tromp G
    53. Jones GT
    54. Kuivaniemi H
    55. Elmore JR
    56. Johansson M
    57. Mckay J
    58. Scelo G
    59. Carreras-Torres R
    60. Gaborieau V
    61. Brennan P
    62. Bracci PM
    63. Neale RE
    64. Olson SH
    65. Gallinger S
    66. Li D
    67. Petersen GM
    68. Risch HA
    69. Klein AP
    70. Han J
    71. Abnet CC
    72. Freedman ND
    73. Taylor PR
    74. Maris JM
    75. Aben KK
    76. Kiemeney LA
    77. Vermeulen SH
    78. Wiencke JK
    79. Walsh KM
    80. Wrensch M
    81. Rice T
    82. Turnbull C
    83. Litchfield K
    84. Paternoster L
    85. Standl M
    86. Abecasis GR
    87. SanGiovanni JP
    88. Li Y
    89. Mijatovic V
    90. Sapkota Y
    91. Low S-K
    92. Zondervan KT
    93. Montgomery GW
    94. Nyholt DR
    95. van Heel DA
    96. Hunt K
    97. Arking DE
    98. Ashar FN
    99. Sotoodehnia N
    100. Woo D
    101. Rosand J
    102. Comeau ME
    103. Brown WM
    104. Silverman EK
    105. Hokanson JE
    106. Cho MH
    107. Hui J
    108. Ferreira MA
    109. Thompson PJ
    110. Morrison AC
    111. Felix JF
    112. Smith NL
    113. Christiano AM
    114. Petukhova L
    115. Betz RC
    116. Fan X
    117. Zhang X
    118. Zhu C
    119. Langefeld CD
    120. Thompson SD
    121. Wang F
    122. Lin X
    123. Schwartz DA
    124. Fingerlin T
    125. Rotter JI
    126. Cotch MF
    127. Jensen RA
    128. Munz M
    129. Dommisch H
    130. Schaefer AS
    131. Han F
    132. Ollila HM
    133. Hillary RP
    134. Albagha O
    135. Ralston SH
    136. Zeng C
    137. Zheng W
    138. Shu X-O
    139. Reis A
    140. Uebe S
    141. Hüffmeier U
    142. Kawamura Y
    143. Otowa T
    144. Sasaki T
    145. Hibberd ML
    146. Davila S
    147. Xie G
    148. Siminovitch K
    149. Bei J-X
    150. Zeng Y-X
    151. Försti A
    152. Chen B
    153. Landi S
    154. Franke A
    155. Fischer A
    156. Ellinghaus D
    157. Flores C
    158. Noth I
    159. Ma S-F
    160. Foo JN
    161. Liu J
    162. Kim J-W
    163. Cox DG
    164. Delattre O
    165. Mirabeau O
    166. Skibola CF
    167. Tang CS
    168. Garcia-Barcelo M
    169. Chang K-P
    170. Su W-H
    171. Chang Y-S
    172. Martin NG
    173. Gordon S
    174. Wade TD
    175. Lee C
    176. Kubo M
    177. Cha P-C
    178. Nakamura Y
    179. Levy D
    180. Kimura M
    181. Hwang S-J
    182. Hunt S
    183. Spector T
    184. Soranzo N
    185. Manichaikul AW
    186. Barr RG
    187. Kahali B
    188. Speliotes E
    189. Yerges-Armstrong LM
    190. Cheng C-Y
    191. Jonas JB
    192. Wong TY
    193. Fogh I
    194. Lin K
    195. Powell JF
    196. Rice K
    197. Relton CL
    198. Martin RM
    199. Davey Smith G

    (2017) Association Between Telomere Length and Risk of Cancer and Non-Neoplastic Diseases: A Mendelian Randomization Study

    JAMA Oncology 3:636–651.

    https://doi.org/10.1001/jamaoncol.2016.5945

    • PubMed
    • Google Scholar
78. 1. Timofeeva MN
    2. Hung RJ
    3. Rafnar T
    4. Christiani DC
    5. Field JK
    6. Bickeböller H
    7. Risch A
    8. McKay JD
    9. Wang Y
    10. Dai J
    11. Gaborieau V
    12. McLaughlin J
    13. Brenner D
    14. Narod SA
    15. Caporaso NE
    16. Albanes D
    17. Thun M
    18. Eisen T
    19. Wichmann HE
    20. Rosenberger A
    21. Han Y
    22. Chen W
    23. Zhu D
    24. Spitz M
    25. Wu X
    26. Pande M
    27. Zhao Y
    28. Zaridze D
    29. Szeszenia-Dabrowska N
    30. Lissowska J
    31. Rudnai P
    32. Fabianova E
    33. Mates D
    34. Bencko V
    35. Foretova L
    36. Janout V
    37. Krokan HE
    38. Gabrielsen ME
    39. Skorpen F
    40. Vatten L
    41. Njølstad I
    42. Chen C
    43. Goodman G
    44. Lathrop M
    45. Benhamou S
    46. Vooder T
    47. Välk K
    48. Nelis M
    49. Metspalu A
    50. Raji O
    51. Chen Y
    52. Gosney J
    53. Liloglou T
    54. Muley T
    55. Dienemann H
    56. Thorleifsson G
    57. Shen H
    58. Stefansson K
    59. Brennan P
    60. Amos CI
    61. Houlston R
    62. Landi MT

    (2012) Influence of common genetic variation on lung cancer risk: meta-analysis of 14 900 cases and 29 485 controls

    Human Molecular Genetics 21:4980–4995.

    https://doi.org/10.1093/hmg/dds334

    • PubMed
    • Google Scholar
79. 1. von Hippel PT

    (2015) The heterogeneity statistic I(2) can be biased in small meta-analyses

    BMC Medical Research Methodology 15:35.

    https://doi.org/10.1186/s12874-015-0024-z

    • Google Scholar
80. 1. Wang Y
    2. Broderick P
    3. Webb E
    4. Wu X
    5. Vijayakrishnan J
    6. Matakidou A
    7. Qureshi M
    8. Dong Q
    9. Gu X
    10. Chen WV
    11. Spitz MR
    12. Eisen T
    13. Amos CI
    14. Houlston RS

    (2008) Common 5p15.33 and 6p21.33 variants influence lung cancer risk

    Nature Genetics 40:1407–1409.

    https://doi.org/10.1038/ng.273

    • PubMed
    • Google Scholar
81. 1. Wang Y
    2. McKay JD
    3. Rafnar T
    4. Wang Z
    5. Timofeeva MN
    6. Broderick P
    7. Zong X
    8. Laplana M
    9. Wei Y
    10. Han Y
    11. Lloyd A
    12. Delahaye-Sourdeix M
    13. Chubb D
    14. Gaborieau V
    15. Wheeler W
    16. Chatterjee N
    17. Thorleifsson G
    18. Sulem P
    19. Liu G
    20. Kaaks R
    21. Henrion M
    22. Kinnersley B
    23. Vallée M
    24. LeCalvez-Kelm F
    25. Stevens VL
    26. Gapstur SM
    27. Chen WV
    28. Zaridze D
    29. Szeszenia-Dabrowska N
    30. Lissowska J
    31. Rudnai P
    32. Fabianova E
    33. Mates D
    34. Bencko V
    35. Foretova L
    36. Janout V
    37. Krokan HE
    38. Gabrielsen ME
    39. Skorpen F
    40. Vatten L
    41. Njølstad I
    42. Chen C
    43. Goodman G
    44. Benhamou S
    45. Vooder T
    46. Välk K
    47. Nelis M
    48. Metspalu A
    49. Lener M
    50. Lubiński J
    51. Johansson M
    52. Vineis P
    53. Agudo A
    54. Clavel-Chapelon F
    55. Bueno-de-Mesquita HB
    56. Trichopoulos D
    57. Khaw K-T
    58. Johansson M
    59. Weiderpass E
    60. Tjønneland A
    61. Riboli E
    62. Lathrop M
    63. Scelo G
    64. Albanes D
    65. Caporaso NE
    66. Ye Y
    67. Gu J
    68. Wu X
    69. Spitz MR
    70. Dienemann H
    71. Rosenberger A
    72. Su L
    73. Matakidou A
    74. Eisen T
    75. Stefansson K
    76. Risch A
    77. Chanock SJ
    78. Christiani DC
    79. Hung RJ
    80. Brennan P
    81. Landi MT
    82. Houlston RS
    83. Amos CI

    (2014) Rare variants of large effect in BRCA2 and CHEK2 affect risk of lung cancer

    Nature Genetics 46:736–741.

    https://doi.org/10.1038/ng.3002

    • PubMed
    • Google Scholar
82. 1. Wang C
    2. Ni W
    3. Yao Y
    4. Just A
    5. Heiss J
    6. Wei Y
    7. Gao X
    8. Coull BA
    9. Kosheleva A
    10. Baccarelli AA
    11. Peters A
    12. Schwartz JD

    (2021) DNA methylation-based biomarkers of age acceleration and all-cause death, myocardial infarction, stroke, and cancer in two cohorts: The NAS, and KORA F4

    EBioMedicine 63:103151.

    https://doi.org/10.1016/j.ebiom.2020.103151

    • PubMed
    • Google Scholar
83. 1. Willer CJ
    2. Li Y
    3. Abecasis GR

    (2010) METAL: fast and efficient meta-analysis of genomewide association scans

    Bioinformatics (Oxford, England) 26:2190–2191.

    https://doi.org/10.1093/bioinformatics/btq340

    • Google Scholar
84. 1. Yu M
    2. Hazelton WD
    3. Luebeck GE

    (2020) Epigenetic Aging: More Than Just a Clock When It Comes to Cancer

    Cancer Research 80:367–374.

    https://doi.org/10.1158/0008-5472.CAN-19-0924

    • Google Scholar
85. 1. Zhang H
    2. Ahearn TU
    3. Lecarpentier J

    (2020) Genome-wide association study identifies 32 novel breast cancer susceptibility loci from overall and subtype-specific analyses

    Nature Genetics 52:572–581.

    https://doi.org/10.1038/s41588-020-0609-2

    • Google Scholar
86. 1. Zheng Y
    2. Joyce BT
    3. Colicino E
    4. Liu L
    5. Zhang W
    6. Dai Q
    7. Shrubsole MJ
    8. Kibbe WA
    9. Gao T
    10. Zhang Z
    11. Jafari N
    12. Vokonas P
    13. Schwartz J
    14. Baccarelli AA
    15. Hou L

    (2016) Blood Epigenetic Age may Predict Cancer Incidence and Mortality

    EBioMedicine 5:68–73.

    https://doi.org/10.1016/j.ebiom.2016.02.008

    • PubMed
    • Google Scholar

Author details

1. Fernanda Morales Berstein

   1. MRC Integrative Epidemiology Unit, University of Bristol, Bristol, United Kingdom
   2. Population Health Sciences, Bristol Medical School, Bristol, United Kingdom

   Contribution

   Conceptualization, Formal analysis, Visualization, Writing – original draft, Writing – review and editing

   For correspondence

   dy20206@bristol.ac.uk

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-8237-2021

2. Daniel L McCartney

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

   Contribution

   Writing – review and editing

   Competing interests

   No competing interests declared

3. Ake T Lu

   Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, United States

   Contribution

   Writing – review and editing

   Competing interests

   declares that UC Regents filed the patent "DNA METHYLATION BASED BIOMARKERS FOR LIFE EXPECTANCY AND MORBIDITY" (International Application Number PCT/US2019/055444; in pending status) and that the Epigenetic Clock Development Foundation and Foxo Labs hold licenses

4. Konstantinos K Tsilidis

   1. Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, United Kingdom
   2. Department of Hygiene and Epidemiology, School of Medicine, University of Ioannina, Ioannina, Greece

   Contribution

   Writing – review and editing

   Competing interests

   No competing interests declared

5. Emmanouil Bouras

   Department of Hygiene and Epidemiology, School of Medicine, University of Ioannina, Ioannina, Greece

   Contribution

   Formal analysis, Writing – review and editing

   Competing interests

   No competing interests declared

6. Philip Haycock

   1. MRC Integrative Epidemiology Unit, University of Bristol, Bristol, United Kingdom
   2. Population Health Sciences, Bristol Medical School, Bristol, United Kingdom

   Contribution

   Writing – review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-5001-3350

7. Kimberley Burrows

   1. MRC Integrative Epidemiology Unit, University of Bristol, Bristol, United Kingdom
   2. Population Health Sciences, Bristol Medical School, Bristol, United Kingdom

   Contribution

   Writing – review and editing

   Competing interests

   No competing interests declared

8. Amanda I Phipps

   1. Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, United States
   2. Department of Epidemiology, School of Public Health, University of Washington, Seattle, United States

   Contribution

   Writing – review and editing

   Competing interests

   No competing interests declared

9. Daniel D Buchanan

   Department of Clinical Pathology, Melbourne Medical School, University of Melbourne, Parkville, Australia

   Contribution

   Writing – review and editing

   Competing interests

   No competing interests declared

10. Iona Cheng

    Cancer Prevention Institute of California, Fremont, United States

    Contribution

    Writing – review and editing

    Competing interests

    No competing interests declared

11. the PRACTICAL consortium

    Contribution

    Contributions to the acquisition of data, critical revision of the manuscript and final approval of the version to be published

    Competing interests

    No competing interests declared

    1. Rosalind A Eeles, The Institute of Cancer Research, London, United Kingdom
    2. Christopher A Haiman, Center for Genetic Epidemiology, Department of Preventive Medicine, Keck School of Medicine, University of Southern California/Norris Comprehensive Cancer Center, Los Angeles, United States
    3. Zsofia Kote-Jarai, The Institute of Cancer Research, London, United Kingdom
    4. Fredrick R Schumacher, Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, United States
    5. Sara Benlloch, The Institute of Cancer Research, London, United Kingdom
    6. Ali Amin Al Olama, Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Strangeways Research Laboratory, Cambridge, United Kingdom
    7. Kenneth R Muir, Division of Population Health, Health Services Research and Primary Care, University of Manchester, Manchester, United Kingdom
    8. Sonja I Berndt, Division of Cancer Epidemiology and Genetics, National Cancer Institute, NIH, Bethesda, United States
    9. David V Conti, Center for Genetic Epidemiology, Department of Preventive Medicine, Keck School of Medicine, University of Southern California/Norris Comprehensive Cancer Center, Los Angeles, United States
    10. Fredrik Wiklund, Department of Medical Epidemiology and Biostatistics, Karolinska Institute, Stockholm, Sweden
    11. Stephen Chanock, Division of Cancer Epidemiology and Genetics, National Cancer Institute, NIH, Bethesda, United States
    12. Ying Wang, Department of Population Science, American Cancer Society, Atlanta, United States
    13. Catherine M Tangen, SWOG Statistical Center, Fred Hutchinson Cancer Research Center, Seattle, United States
    14. Jyotsna Batra, Australian Prostate Cancer Research Centre-Qld, Institute of Health and Biomedical Innovation and School of Biomedical Sciences, Queensland University of Technology, Brisbane, Australia
    15. Judith A Clements, Australian Prostate Cancer Research Centre-Qld, Institute of Health and Biomedical Innovation and School of Biomedical Sciences, Queensland University of Technology, Brisbane, Australia
    16. A, Translational Research Institute, Brisbane, Australia
    17. Henrik Grönberg, Department of Medical Epidemiology and Biostatistics, Karolinska Institute, Stockholm, Sweden
    18. Nora Pashayan, Department of Applied Health Research, University College London, London, United Kingdom
    19. Johanna Schleutker, Institute of Biomedicine, University of Turku, Turku, Finland
    20. Demetrius Albanes, Division of Cancer Epidemiology and Genetics, National Cancer Institute, NIH, Bethesda, United States
    21. Stephanie Weinstein, Division of Cancer Epidemiology and Genetics, National Cancer Institute, NIH, Bethesda, United States
    22. Alicja Wolk, Department of Surgical Sciences, Uppsala University, Uppsala, Sweden
    23. Catharine ML West, Division of Cancer Sciences, University of Manchester, Manchester Academic Health Science Centre, Radiotherapy Related Research, The Christie Hospital NHS Foundation Trust, Manchester, United Kingdom
    24. Lorelei A Mucci, Department of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, United States
    25. Géraldine Cancel-Tassin, CeRePP, Tenon Hospital, Paris, France
    26. Stella Koutros, Division of Cancer Epidemiology and Genetics, National Cancer Institute, NIH, Bethesda, United States
    27. Karina Dalsgaard Sørensen, Department of Molecular Medicine, Aarhus University Hospital, Aarhus, Denmark
    28. Eli Marie Grindedal, Department of Medical Genetics, Oslo University Hospital, Oslo, Norway
    29. David E Neal, Nuffield Department of Surgical Sciences, University of Oxford, Room 6603, Level 6, John Radcliffe Hospital, Headley Way, Oxford, United Kingdom
    30. Freddie C Hamdy, Nuffield Department of Surgical Sciences, University of Oxford, Oxford, United Kingdom
    31. Jenny L Donovan, Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom
    32. Ruth C Travis, Cancer Epidemiology Unit, Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom
    33. Robert J Hamilton, Dept. of Surgical Oncology, Princess Margaret Cancer Centre, Toronto, Canada
    34. Sue Ann Ingles, Department of Preventive Medicine, Keck School of Medicine, University of Southern California/Norris Comprehensive Cancer Center, Los Angeles, United States
    35. Barry S Rosenstein, Department of Radiation Oncology and Department of Genetics and Genomic Sciences, Box 1236, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, United States
    36. Yong-Jie Lu, Centre for Cancer Biomarker and Biotherapeutics, Barts Cancer Institute, Queen Mary University of London, John Vane Science Centre, Charterhouse Square, London, United Kingdom
    37. Graham G Giles, Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Australia
    38. Robert J MacInnis, Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Australia
    39. Adam S Kibel, Division of Urologic Surgery, Brigham and Womens Hospital, Boston, United States
    40. Ana Vega, Fundación Pública Galega Medicina Xenómica, Santiago de Compostela, Spain
    41. Manolis Kogevinas, ISGlobal, Barcelona, Spain
    42. Kathryn L Penney, Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital/Harvard Medical School, Boston, United States
    43. Jong Y Park, Department of Cancer Epidemiology, Moffitt Cancer Center, Tampa, United States
    44. Janet L Stanford, Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, United States
    45. Cezary Cybulski, International Hereditary Cancer Center, Department of Genetics and Pathology, Pomeranian Medical University, Szczecin, Poland
    46. Børge G Nordestgaard, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
    47. Sune F Nielsen, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
    48. Hermann Brenner, Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany
    49. Christiane Maier, Humangenetik Tuebingen, Tuebingen, Germany
    50. UJeri Kim, The University of Texas M. D. Anderson Cancer Center, Department of Genitourinary Medical Oncology, Houston, United States
    51. Esther M John, Departments of Epidemiology & Population Health and of Medicine, Division of Oncology, Stanford Cancer Institute, Stanford University School of Medicine, Stanford, United States
    52. Manuel R Teixeira, Department of Genetics, Portuguese Oncology Institute of Porto (IPO-Porto), Porto, Portugal
    53. Susan L Neuhausen, Department of Population Sciences, Beckman Research Institute of the City of Hope, Duarte, United States
    54. Kim De Ruyck, Ghent University, Faculty of Medicine and Health Sciences, Basic Medical Sciences, Ghent, Belgium
    55. Azad Razack, Department of Surgery, Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia
    56. Lisa F Newcomb, Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, United States
    57. Davor Lessel, Institute of Human Genetics, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
    58. Radka Kaneva, Molecular Medicine Center, Department of Medical Chemistry and Biochemistry, Medical University of Sofia, Sofia, Bulgaria
    59. Nawaid Usmani, Department of Oncology, Cross Cancer Institute, University of Alberta, Edmonton, Canada
    60. Frank Claessens, Molecular Endocrinology Laboratory, Department of Cellular and Molecular Medicine, Leuven, Belgium
    61. Paul A Townsend, Division of Cancer Sciences, Manchester Cancer Research Centre, Faculty of Biology, Medicine and Health, Manchester Academic Health Science Centre, NIHR Manchester Biomedical Research Centre, Health Innovation Manchester, Univeristy of Manchester, Manchester, United Kingdom
    62. Jose Esteban Castelao, Genetic Oncology Unit, CHUVI Hospital, Complexo Hospitalario Universitario de Vigo, Instituto de Investigación Biomédica Galicia Sur (IISGS), Vigo, Spain
    63. Monique J Roobol, Department of Urology, Erasmus University Medical Center, Rotterdam, Netherlands
    64. Florence Menegaux, "Exposome and Heredity", CESP (UMR 1018), Faculté de Médecine, Université Paris-Saclay, Villejuif, France
    65. Kay-Tee Khaw, Clinical Gerontology Unit, University of Cambridge, Cambridge, United Kingdom
    66. ULisa Cannon-Albright, Division of Epidemiology, Department of Internal Medicine, University of Utah School of Medicine, Salt Lake City, United States
    67. Hardev Pandha, The University of Surrey, Guildford, Surrey, United Kingdom
    68. Stephen N Thibodeau, Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, United States
    69. David J Hunter, Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom
    70. Peter Kraft, Program in Genetic Epidemiology and Statistical Genetics, Department of Epidemiology, Harvard School of Public Health, Boston, United States
    71. William J Blot, Division of Epidemiology, Department of Medicine, Vanderbilt University Medical Center, Nashville, United States
    72. Elio Riboli, Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, United Kingdom

12. Richard M Martin

    1. MRC Integrative Epidemiology Unit, University of Bristol, Bristol, United Kingdom
    2. Population Health Sciences, Bristol Medical School, Bristol, United Kingdom
    3. National Institute for Health Research (NIHR) Bristol Biomedical Research Centre, University Hospitals Bristol and Weston NHS Foundation Trust and the University of Bristol, Bristol, United Kingdom

    Contribution

    Writing – review and editing

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0002-7992-7719

13. George Davey Smith

    1. MRC Integrative Epidemiology Unit, University of Bristol, Bristol, United Kingdom
    2. Population Health Sciences, Bristol Medical School, Bristol, United Kingdom

    Contribution

    Writing – review and editing

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0002-1407-8314

14. Caroline L Relton

    1. MRC Integrative Epidemiology Unit, University of Bristol, Bristol, United Kingdom
    2. Population Health Sciences, Bristol Medical School, Bristol, United Kingdom

    Contribution

    Writing – review and editing

    Competing interests

    declares that UC Regents filed the patent "DNA METHYLATION BASED BIOMARKERS FOR LIFE EXPECTANCY AND MORBIDITY" (International Application Number PCT/US2019/055444; in pending status) and that the Epigenetic Clock Development Foundation and Foxo Labs hold licenses. SH receives consulting fees from the Epigenetic Clock Development Foundation and royalties for patents involving epigenetic clocks

    "This ORCID iD identifies the author of this article:" 0000-0003-2052-4840

15. Steve Horvath

    1. Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, United States
    2. Department of Biostatistics, Fielding School of Public Health, University of California, Los Angeles, Los Angeles, United States

    Contribution

    Writing – review and editing

    Competing interests

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

16. Riccardo E Marioni

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

    Contribution

    Writing – review and editing

    Competing interests

    is employed part time by Novo Nordisk outside of this work

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

17. Tom G Richardson

    1. MRC Integrative Epidemiology Unit, University of Bristol, Bristol, United Kingdom
    2. Population Health Sciences, Bristol Medical School, Bristol, United Kingdom
    3. Novo Nordisk Research Centre, Oxford, United Kingdom

    Contribution

    Conceptualization, Formal analysis, Supervision, Writing – review and editing

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0002-7918-2040

18. Rebecca C Richmond

    1. MRC Integrative Epidemiology Unit, University of Bristol, Bristol, United Kingdom
    2. Population Health Sciences, Bristol Medical School, Bristol, United Kingdom

    Contribution

    Conceptualization, Supervision, Writing – review and editing

    Competing interests

    No competing interests declared

• 5,188

  views

• 721

  downloads

• 46

  citations

Views, downloads and citations are aggregated across all versions of this paper published by eLife.