Author guidelines for journals could help to promote transparency, openness, and reproducibility

DNA methylation (DNAm) has been revealed to play a role in various diseases. Here we performed epigenome-wide screening and validation to identify mortality-related DNAm signatures in a general population-based cohort with up to 14 years follow-up. In the discovery panel in a case-cohort approach, 11,063 CpGs reach genome-wide significance (FDR<0.05). 58 CpGs, mapping to 38 well-known disease-related genes and 14 intergenic regions, are confirmed in a validation panel. A mortality risk score based on ten selected CpGs exhibits strong association with all-cause mortality, showing hazard ratios (95% CI) of 2.16 (1.10–4.24), 3.42 (1.81–6.46) and 7.36 (3.69–14.68), respectively, for participants with scores of 1, 2–5 and 5+ compared with a score of 0. These associations are confirmed in an independent cohort and are independent from the ‘epigenetic clock'. In conclusion, DNAm of multiple disease-related genes are strongly linked to mortality outcomes. The DNAm-based risk score might be informative for risk assessment and stratification.

We carried out a trans-ancestry genome-wide association and replication study of blood pressure phenotypes among up to 320,251 individuals of East Asian, European and South Asian ancestry. We find genetic variants at 12 new loci to be associated with blood pressure (P = 3.9 × 10−11 to 5.0 × 10−21). The sentinel blood pressure SNPs are enriched for association with DNA methylation at multiple nearby CpG sites, suggesting that, at some of the loci identified, DNA methylation may lie on the regulatory pathway linking sequence variation to blood pressure. The sentinel SNPs at the 12 new loci point to genes involved in vascular smooth muscle (IGFBP3, KCNK3, PDE3A and PRDM6) and renal (ARHGAP24, OSR1, SLC22A7 and TBX2) function. The new and known genetic variants predict increased left ventricular mass, circulating levels of NT-proBNP, and cardiovascular and all-cause mortality (P = 0.04 to 8.6 × 10−6). Our results provide new evidence for the role of DNA methylation in blood pressure regulation.

In the sentence beginning 'The meQTL are enriched for functionally relevant characteristics', please provide a de inition for Hi-C.2. In the author list, there are two authors with the name Panos Deloukas with different af iliations. Please con irm whether this is the same individual and whether the two authors can be merged. 3.Please check your article carefully, coordinate with any co-authors and enter all inal edits clearly in the eproof, remembering to save frequently. Once corrections are submitted, we cannot routinely make further changes to the article. 4. Note that the eproof should be amended in only one browser window at any one time; otherwise changes will be overwritten. 5. Author surnames have been highlighted. Please check these carefully and adjust if the irst name or surname is marked up incorrectly. Note that changes here will affect indexing of your article in public repositories such as PubMed. Also, carefully check the spelling and numbering of all author names and af iliations, and the corresponding email address(es).6. You cannot alter accepted Supplementary Information iles except for critical changes to scienti ic content. If you do resupply any iles, please also provide a brief (but complete) list of changes. If these are not considered scienti ic changes, any altered Supplementary iles will not be used, only the originally accepted version will be published. If applicable, please ensure that any accession codes and datasets whoseDOIs or other identi iers are mentioned in the paper are scheduled for public release as soon as possible, we recommend within a few days of submitting your proof, and update the database record with publication details from this article once available. 8. Your paper has been copy edited. Please review every sentence to ensure that it conveys your intended meaning; if changes are required, please provide further clari ication rather than reverting to the original text. Please note that formatting (including hyphenation, Latin words,and any reference citations that might be mistaken for exponents) has been made consistent with our house style. 9. Please con irm or correct the city name inserted in af iliations 5, 10, 12, 13, 21, 22, 39 and 44. 10. In the sentence beginning 'Our meQTL replicate in data generated by the Illumina', please provide a de inition for EPIC and con irm whether the de inition provided for MeDIP-seq is correct. 11. In the legend for Fig. 2g, please provide a de inition for SAT. 12. In the sentence beginning 'We used summary data-based Mendelian', please con irm whether it is correct to say '0.05 ÷ n tests'. 13. In the sentence beginning 'We then tested the 4,811 eQTL genes', please provide a de inition for coloc PP4. 14. Please note, we reserve ‘signi icant' and its derivatives for statistical signi icance. This has been reworded where this was not the intended meaning (for example to important, notable, ...

Advances in the “omics” field bring about the need for a high number of good quality samples. Many omics studies take advantage of biobanked samples to meet this need. Most of the laboratory errors occur in the pre-analytical phase. Therefore evidence-based standard operating procedures for the pre-analytical phase as well as markers to distinguish between ‘good’ and ‘bad’ quality samples taking into account the desired downstream analysis are urgently needed. We studied concentration changes of metabolites in serum samples due to pre-storage handling conditions as well as due to repeated freeze-thaw cycles. We collected fasting serum samples and subjected aliquots to up to four freeze-thaw cycles and to pre-storage handling delays of 12, 24 and 36 hours at room temperature (RT) and on wet and dry ice. For each treated aliquot, we quantified 127 metabolites through a targeted metabolomics approach. We found a clear signature of degradation in samples kept at RT. Storage on wet ice led to less pronounced concentration changes. 24 metabolites showed significant concentration changes at RT. In 22 of these, changes were already visible after only 12 hours of storage delay. Especially pronounced were increases in lysophosphatidylcholines and decreases in phosphatidylcholines. We showed that the ratio between the concentrations of these molecule classes could serve as a measure to distinguish between ‘good’ and ‘bad’ quality samples in our study. In contrast, we found quite stable metabolite concentrations during up to four freeze-thaw cycles. We concluded that pre-analytical RT handling of serum samples should be strictly avoided and serum samples should always be handled on wet ice or in cooling devices after centrifugation. Moreover, serum samples should be frozen at or below -80°C as soon as possible after centrifugation.