Author response: Assessing the causal role of epigenetic clocks in the development of multiple cancers: a Mendelian randomization study
Year: 2022
Type: peer-review
Abstract: Article Figures and data Abstract Editor's evaluation eLife digest Introduction Materials and methods Results Discussion Appendix 1 Appendix 2 Data availability References Decision letter Author response Article and author information Metrics Abstract Background: Epigenetic clocks have been associated with cancer risk in several observational studies. Nevertheless, it is unclear whether they play a causal role in cancer risk or if they act as a non-causal biomarker. Methods: We conducted a two-sample Mendelian randomization (MR) study to examine the genetically predicted effects of epigenetic age acceleration as measured by HannumAge (nine single-nucleotide polymorphisms (SNPs)), Horvath Intrinsic Age (24 SNPs), PhenoAge (11 SNPs), and GrimAge (4 SNPs) on multiple cancers (i.e. breast, prostate, colorectal, ovarian and lung cancer). We obtained genome-wide association data for biological ageing from a meta-analysis (N = 34,710), and for cancer from the UK Biobank (N cases = 2671–13,879; N controls = 173,493–372,016), FinnGen (N cases = 719–8401; N controls = 74,685–174,006) and several international cancer genetic consortia (N cases = 11,348–122,977; N controls = 15,861–105,974). Main analyses were performed using multiplicative random effects inverse variance weighted (IVW) MR. Individual study estimates were pooled using fixed effect meta-analysis. Sensitivity analyses included MR-Egger, weighted median, weighted mode and Causal Analysis using Summary Effect Estimates (CAUSE) methods, which are robust to some of the assumptions of the IVW approach. Results: Meta-analysed IVW MR findings suggested that higher GrimAge acceleration increased the risk of colorectal cancer (OR = 1.12 per year increase in GrimAge acceleration, 95% CI 1.04–1.20, p = 0.002). The direction of the genetically predicted effects was consistent across main and sensitivity MR analyses. Among subtypes, the genetically predicted effect of GrimAge acceleration was greater for colon cancer (IVW OR = 1.15, 95% CI 1.09–1.21, p = 0.006), than rectal cancer (IVW OR = 1.05, 95% CI 0.97–1.13, p = 0.24). Results were less consistent for associations between other epigenetic clocks and cancers. Conclusions: GrimAge acceleration may increase the risk of colorectal cancer. Findings for other clocks and cancers were inconsistent. Further work is required to investigate the potential mechanisms underlying the results. Funding: FMB was supported by a Wellcome Trust PhD studentship in Molecular, Genetic and Lifecourse Epidemiology (224982/Z/22/Z which is part of grant 218495/Z/19/Z). KKT was supported by a Cancer Research UK (C18281/A29019) programme grant (the Integrative Cancer Epidemiology Programme) and by the Hellenic Republic’s Operational Programme ‘Competitiveness, Entrepreneurship & Innovation’ (OΠΣ 5047228). PH was supported by Cancer Research UK (C18281/A29019). RMM was supported by the NIHR Biomedical Research Centre at University Hospitals Bristol and Weston NHS Foundation Trust and the University of Bristol and by a Cancer Research UK (C18281/A29019) programme grant (the Integrative Cancer Epidemiology Programme). RMM is a National Institute for Health Research Senior Investigator (NIHR202411). The views expressed are those of the author(s) and not necessarily those of the NIHR or the Department of Health and Social Care. GDS and CLR were supported by the Medical Research Council (MC_UU_00011/1 and MC_UU_00011/5, respectively) and by a Cancer Research UK (C18281/A29019) programme grant (the Integrative Cancer Epidemiology Programme). REM was supported by an Alzheimer’s Society project grant (AS-PG-19b-010) and NIH grant (U01 AG-18-018, PI: Steve Horvath). RCR is a de Pass Vice Chancellor’s Research Fellow at the University of Bristol. Editor's evaluation This paper is of broad interest to researchers seeking to disentangle the health impact of epigenetic age acceleration, and will provide a substantive empirical contribution to the literature. The authors were very meticulous in addressing all the concerns from the reviewers, which has further improved the paper. https://doi.org/10.7554/eLife.75374.sa0 Decision letter Reviews on Sciety eLife's review process eLife digest 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. Introduction 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). Materials and methods Reporting guidelines Request a detailed protocol This study has been reported according to the STROBE-MR guidelines (Skrivankova et al., 2021; Supplementary file 2). Genetic instruments for epigenetic age acceleration Request a detailed protocol We obtained summary genetic association estimates for epigenetic age acceleration measures of HannumAge (Hannum et al., 2013), Intrinsic HorvathAge (Horvath, 2013), PhenoAge (Levine et al., 2018), and GrimAge (Lu et al., 2019a) from a recent GWAS meta-analysis of biological ageing (McCartney et al., 2021), which included 34,710 participants of European ancestry. Across the 28 European ancestry studies considered in the analysis, 57.3% of participants were female. A detailed description of the methods that were used can be found in the publication by McCartney et al., 2021. In short, the Horvath epigenetic age calculator software (https://dnamage.genetics.ucla.edu) or standalone scripts were used to calculate age adjusted DNAm estimates. Outlier samples with clock methylation estimates of +/−5 s.d. from the mean were excluded from further analysis. SNPs were genotyped and imputed independently for each cohort included in the meta-analysis. Genotypes were imputed using either the HRC or the 1000 Genomes Project Phase 3 reference panels in all cohorts but the Sister Study (which did not have imputed data at the time of analysis) and the Genetics of Lipid Lowering Drugs and Diet Network Study (which used whole-genome sequencing data). GWAS summary statistics were obtained in each cohort using additive linear models adjusted for sex and genetic principal components, and they were later processed and harmonised using the ‘EasyQC’ R package. Fixed effect meta-analyses were performed using the METAL software (Willer et al., 2010). We used the clump_data function in the ‘TwoSampleMR’ R package to select GWAS-significant SNPs (P < 5 × 10−8) for each epigenetic age acceleration measure and perform linkage disequilibrium (LD) clumping (r2 <0.001) using the European reference panel from the 1000 Genomes Project Phase 3 v5. We identified 9 independent SNPs for HannumAge, 24 for Intrinsic HorvathAge, 11 for PhenoAge and 4 for GrimAge (Supplementary file 1 — Table 1). The proportions of trait variance explained by genetic instruments (R2) and instrument strength (F-statistic) were calculated using the following formulae: R2 = (2β2×MAF×(1-MAF))/(2β2×MAF×(1-MAF) + 2 N × MAF × (1-MAF)×SE2) and F = (R2×(N-2))/(1-R2) (where MAF = effect allele frequency, β = effect estimate of the SNP in the exposure GWAS, SE = standard error, N = sample size) (Palmer et al., 2012). The genetic instruments for HannumAge, Intrinsic HorvathAge, PhenoAge and GrimAge acceleration explained 1.48%, 4.41%, 1.86%, and 0.47% of the trait variance, respectively. All the selected SNPs had F-statistics greater than 10 (HannumAge median 38 and range 31–99, Intrinsic HorvathAge median 47 and range 31–240, PhenoAge median 45 and range 32–89, GrimAge median 36 and range 31–45). Table 1 Numbers of overall cancer cases and controls by data source. Cancer typeSourceN cases (%)*N controlsBreastBCAC122,977 (53.7%)105,974UK Biobank13,879 (6.5%)198,523 FinnGen8401 (7.8%)99,321OvarianOCAC25,509 (38.4%)40,941UK Biobank1218 (0.6%)198,523 FinnGen719 (0.7%)99,321ProstatePRACTICAL79,148 (56.4%)61,106UK Biobank9132 (5.0%)173,493 FinnGen6311 (7.8%)74,685LungILCCO11,348 (41.7%)15,861UK Biobank2671 (0.7%)372,016 FinnGen1681 (1.0%)173,933ColorectalGECCO58,131 (46.3%)67,347UK Biobank5657 (1.5%)372,016 FinnGen3022 (1.7%)174,006 * Percentage (%) of cases within each study source was calculated using the following formula: 100 * N cases / (N cases + N controls). BCAC, Breast Cancer Association Consortium; OCAC, Ovarian Cancer Association Consortium; PRACTICAL, Prostate Cancer Association Group to Investigate Cancer Associated Alterations in the Genome; ILCCO, International Lung Cancer Consortium; GECCO, Genetics and Epidemiology of Colorectal Cancer Consortium. Genetic Association Data sources for cancer outcomes Request a detailed protocol We obtained summary-level genetic association data for cancer outcomes from the UK Biobank, FinnGen and several international cancer genetic consortia: the Breast Cancer Association Consortium (BCAC), the Ovarian Cancer Association Consortium (OCAC), the Consortium of Investigators of Modifiers of BRCA1/2 (CIMBA), the Prostate Cancer Association Group to Investigate Cancer Associated Alterations in the Genome (PRACTICAL), the International Lung Cancer Consortium (ILCCO) and the Genetics and Epidemiology of Colorectal Cancer Consortium (GECCO) (Table 1). Further details of the studies and the data obtained are described in Appendix 1. We extracted genetic association data for the selected SNPs from each cancer GWAS (for breast, prostate, colorectal, ovarian and lung cancers). LD proxies (r2 >0.8) were used when the SNPs of interest were missing from the cancer GWAS dataset. The proxies were located using the MR-Base platform, which calculates LD using the European subset of individuals from the 1000 Genomes Project reference panel as above (Hemani et al., 2018). The ‘LDlinkR’ R package version 1.1.2 was used to find proxies for cancer data that were not included in the MR-Base platform. The exposure and outcome datasets were then harmonised to ensure the genetic associations reflect the same effect allele. Palindromic SNPs with minor allele frequencies (MAF) <0.3 were aligned, while those with MAF ≥0.3 or mismatching strands were excluded. Power calculations Request a detailed protocol Statistical power was calculated using an online calculator for MR available at: https://shiny.cnsgenomics.com/mRnd/. Calculations were performed separately for each clock-cancer combination. They were based on a type one error rate of 0.05, the proportion of phenotypic variance explained by genetic variants (R2) for each measure of epigenetic age acceleration, and the total number of cases and controls included in the meta-analysis for each cancer. Across combinations of the four epigenetic clock acceleration and five cancer measures, we had 80% power to detect ORs as small as 1.04–1.39 (Supplementary file 1 — Table s2). Statistical analysis Request a detailed protocol We estimated 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 a two-sample MR framework (Figure 1). Figure 1 Download asset Open asset Flowchart summarising study methods. Abbreviations: BCAC, Breast Cancer Association Consortium; OCAC, Ovarian Cancer Association Consortium; PRACTICAL, Prostate Cancer Association Group to Investigate Cancer Associated Alterations in the Genome; ILCCO, International Lung Cancer Consortium; GECCO, Genetics and Epidemiology of Colorectal Cancer Consortium; LD, linkage disequilibrium; IVW, inverse variance weighted; MR, Mendelian randomization; FDR, false discovery rate; GWAS, genome-wide association study; CAUSE, Causal Analysis Using Summary Effect estimates, SNP, single-nucleotide polymorphism. Main analyses Request a detailed protocol Main analyses were performed using multiplicative random effects inverse variance weighted (IVW) MR, a method that combines the genetically predicted effect of epigenetic age acceleration on cancer across genetic variants (Burgess et al., 2013). This is the default IVW MR method in the ‘TwoSampleMR’ R package, as it accounts for excess heterogeneity across SNP-specific estimates (as opposed to the fixed effect IVW method) and it does not affect the relative weights of individual SNP estimates (in contrast to the additive random effects IVW method) (Bowden et al., 2017). We used fixed effect meta-analysis to pool results across studies (i.e. UK Biobank, FinnGen and international consortia). For colorectal cancer, we only pooled FinnGen and GECCO estimates, since UK Biobank participants were already included in GECCO. I2 statistics and their corresponding confidence intervals were used to estimate heterogeneity across study estimates (von Hippel, 2015). A Benjamini-Hochberg false discovery rate (FDR) < 5% was used to correct the pooled main IVW results for multiple testing (Benjamini and Hochberg, 1995). This correction was applied considering a total of 20 independent statistical tests (4 clocks x 5 cancers = 20). Sensitivity analyses Request a detailed protocol MR assumes genetic instruments for epigenetic age acceleration are (1) associated with epigenetic age acceleration (relevance assumption), (2) independent of confounders of the association between the instruments and cancer (independence assumption), and (3) only associated with cancer through their effect on epigenetic age acceleration (exclusion restriction assumption) (Didelez and Sheehan, et al., 2018). a sensitivity analysis and to for potential of the we calculated F-statistics and the R2 for each measure of epigenetic age acceleration (Burgess and sensitivity analyses included (Bowden et al., weighted median (Bowden et al., and weighted mode et al., methods, which are robust to some of the assumptions of the IVW approach in Appendix 1). results were also pooled across as explained across different MR methods would suggest that it is less likely that the and restriction assumptions are We further the of the by MR analyses using outcomes (i.e. of of causality between our genetic instruments for epigenetic age acceleration and outcomes would suggest potential to that has not been for through in the GWAS et al., 2021). We also the genetically predicted effect of epigenetic age acceleration on cancer risk (i.e. of time age time of and to detect potential of the restriction GWAS data for outcomes and cancer risk were obtained using the University of (for Appendix 1). associations between genetically predicted epigenetic age acceleration and cancer were we performed two-sample MR analysis to whether the effects were likely to be by a We used the METAL software (Willer et al., to a GWAS meta-analysis of cancer genetic association data obtained from the UK Biobank, FinnGen and international cancer genetic We then used summary statistics in two-sample MR analyses. the effects of genetic instruments on epigenetic clock acceleration their effects on cancer were using the ‘TwoSampleMR’ R package. statistics were used to heterogeneity across SNP-specific MR estimates (Bowden et al., and tests were performed to detect (Bowden et al., 2015). We also used Causal Analysis using Summary Effect Estimates (CAUSE) et al., a method that uses genome-wide summary statistics to disentangle causality (i.e. SNPs are associated with cancer through their effect on epigenetic age from correlated (i.e. SNPs are associated with epigenetic age acceleration and cancer through a heritable while into (i.e. SNPs are associated with epigenetic age acceleration through uses to whether the (i.e. that the causal effect at the data at as well as the causal (i.e. that a causal effect different from analyses Request a detailed protocol a analysis, we conducted two-sample MR of epigenetic age acceleration and cancer (i.e. cancer: and ovarian cancer: and prostate cancer: risk and risk and lung cancer: and colorectal cancer: and 1). We also performed two-sample MR analyses of epigenetic age acceleration and of cancer in the UK Biobank for breast, prostate, lung and cancer 1). Data on of ovarian cancer were not available in UK data with and data and it has been suggested that they provide better power to detect GWAS-significant associations for some in the UK Biobank et al., 2020). we results to be consistent with those obtained in the main analyses. MR results were reported as the of cancer per one year increase in genetically predicted epigenetic age did not as the GWAS of biological ageing (McCartney et al., 2021) reported epigenetic age acceleration in LD et al., was used to genome-wide genetic between epigenetic age acceleration and cancer. Genetic were estimated using GWAS summary statistics for the epigenetic clocks and cancer, as well as the 1000 Genomes Project European LD reference with mean < were excluded from the analyses. MR analyses were conducted to the causality and of the between epigenetic clock acceleration and measure of biological ageing that has been to cancer risk in MR studies Mendelian et al., 2017; et al., 2020; et al., 2019). The MR of was used to the that the exposure causes the outcome is (Hemani et al., 2017). We also our findings by the analyses using data that had to SNPs that explained variance in the outcome than in the risk Genetic association data for measured were obtained from et al., the GWAS of available through the at the time of analysis (N = for Appendix 1). All MR analyses were performed using R software version sample MR analyses were conducted using the ‘TwoSampleMR’ package version of IVW results were performed using the package version GWAS meta-analyses used to perform MR analyses were using the METAL software (Willer et al., 2010). analyses were conducted using the package version were using the package version LD were using the version The used in this study is available at: Results Breast cancer We did not find evidence of causality between epigenetic age acceleration and cancer IVW OR = 95% CI p = PhenoAge IVW OR = 95% CI p = HannumAge IVW OR = 95% CI p = and Intrinsic HorvathAge IVW OR = 95% CI p = (Figure Appendix 1, Appendix Appendix Appendix Appendix Appendix Appendix Appendix Supplementary file 1 — Table Supplementary file 1 — Table Supplementary file 1 — Table Supplementary file 1 — Table Figure 2 Download asset Open asset Fixed effect meta-analysis of weighted Mendelian randomization estimates for genetically predicted effects of epigenetic age acceleration on multiple cancers. and 95% confidence intervals are reported per 1 year increase in GrimAge acceleration, PhenoAge acceleration, HannumAge acceleration and Intrinsic HorvathAge HannumAge and Intrinsic HorvathAge acceleration were by and 24 genetic respectively. All meta-analysis estimates were calculated using data from UK Biobank, FinnGen and international for colorectal cancer estimates, which UK Biobank data to Ovarian cancer was also evidence of causality between
Source: eLife
Authors: Fernanda Morales Berstein, Daniel L McCartney, Ake T Lu, Konstantinos K Tsilidis, Emmanouil Bouras, Philip Haycock, Kimberley Burrows, Amanda I Phipps, Daniel D Buchanan, Iona Cheng, Richard M Martin, George Davey Smith, Caroline L Relton, Steve Horvath, Riccardo E Marioni, Tom G Richardson, Rebecca C Richmond
Institutions: University of Bristol, MRC Integrative Epidemiology Unit, Institute of Genetics and Cancer, University of Edinburgh, University of California, Los Angeles, University of Ioannina, Imperial College London, University of Washington, Fred Hutch Cancer Center, The University of Melbourne, Cancer Prevention Institute of California, NIHR Bristol Biomedical Research Centre, University Hospitals Bristol and Weston NHS Foundation Trust
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: gold
Estimated APC paid: $2,500
OpenAlex ID: W4293228381