[{"publication":"Pharmacogenomics Journal","date_created":"2026-03-29T22:07:08Z","OA_place":"publisher","acknowledgement":"This research has been conducted using the UK Biobank Resource under Application Number 62979. We are grateful to the UK Biobank and all its voluntary participants. This work used data provided by patients and collected by the NHS as part of their care and support.\r\n\r\nThis study was funded by the National Science Center, Poland: PRELUDIUM BIS-3 grant no. 2021/43/O/NZ7/01187 (development and benchmarking of variant scores) and SONATINA 5 grant 2021/40/C/NZ2/00218 (UKB analyses). Additional support came from the statutory funds of the Maj Institute of Pharmacology PAS. We gratefully acknowledge Poland’s high-performance Infrastructure PLGrid ACK Cyfronet AGH, for providing computer facilities and support within computational grant no PLG/2022/015861. DMF and GEB were funded by NIH grants NIH R35GM152106 and UM1HG011969.","OA_type":"hybrid","scopus_import":"1","day":"09","tmp":{"name":"Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)","short":"CC BY-NC-ND (4.0)","image":"/images/cc_by_nc_nd.png","legal_code_url":"https://creativecommons.org/licenses/by-nc-nd/4.0/legalcode"},"pmid":1,"article_processing_charge":"Yes (in subscription journal)","month":"03","file":[{"file_size":2618963,"content_type":"application/pdf","date_updated":"2026-03-30T07:04:08Z","file_name":"2026_PharmacogenomicsJour_Hajto.pdf","access_level":"open_access","success":1,"creator":"dernst","file_id":"21506","relation":"main_file","date_created":"2026-03-30T07:04:08Z","checksum":"2fd3d7e48b779ac24245f6c35449b89a"}],"volume":26,"intvolume":"        26","title":"Computational variant predictors for pharmacogenomics: From evaluation of single alleles to assessment of adverse drug reactions to antidepressants","quality_controlled":"1","status":"public","type":"journal_article","year":"2026","file_date_updated":"2026-03-30T07:04:08Z","publication_status":"published","has_accepted_license":"1","oa":1,"article_type":"original","doi":"10.1038/s41397-026-00399-0","publication_identifier":{"issn":[" 1470-269X"],"eissn":["1473-1150"]},"_id":"21503","article_number":"8","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","author":[{"full_name":"Hajto, Jacek","first_name":"Jacek","last_name":"Hajto"},{"last_name":"Piechota","first_name":"Marcin","full_name":"Piechota, Marcin"},{"last_name":"Krätschmer","first_name":"Ilse","id":"30d4014e-7753-11eb-b44b-db6d61112e73","full_name":"Krätschmer, Ilse","orcid":"0000-0002-5636-9259"},{"last_name":"Konowalska","first_name":"Paula","full_name":"Konowalska, Paula"},{"full_name":"Boyle, Gabriel E.","last_name":"Boyle","first_name":"Gabriel E."},{"first_name":"Douglas M.","last_name":"Fowler","full_name":"Fowler, Douglas M."},{"full_name":"Borczyk, Malgorzata","last_name":"Borczyk","first_name":"Malgorzata"},{"first_name":"Michal","last_name":"Korostynski","full_name":"Korostynski, Michal"}],"citation":{"ieee":"J. Hajto <i>et al.</i>, “Computational variant predictors for pharmacogenomics: From evaluation of single alleles to assessment of adverse drug reactions to antidepressants,” <i>Pharmacogenomics Journal</i>, vol. 26, no. 2. Springer Nature, 2026.","chicago":"Hajto, Jacek, Marcin Piechota, Ilse Krätschmer, Paula Konowalska, Gabriel E. Boyle, Douglas M. Fowler, Malgorzata Borczyk, and Michal Korostynski. “Computational Variant Predictors for Pharmacogenomics: From Evaluation of Single Alleles to Assessment of Adverse Drug Reactions to Antidepressants.” <i>Pharmacogenomics Journal</i>. Springer Nature, 2026. <a href=\"https://doi.org/10.1038/s41397-026-00399-0\">https://doi.org/10.1038/s41397-026-00399-0</a>.","ista":"Hajto J, Piechota M, Krätschmer I, Konowalska P, Boyle GE, Fowler DM, Borczyk M, Korostynski M. 2026. Computational variant predictors for pharmacogenomics: From evaluation of single alleles to assessment of adverse drug reactions to antidepressants. Pharmacogenomics Journal. 26(2), 8.","mla":"Hajto, Jacek, et al. “Computational Variant Predictors for Pharmacogenomics: From Evaluation of Single Alleles to Assessment of Adverse Drug Reactions to Antidepressants.” <i>Pharmacogenomics Journal</i>, vol. 26, no. 2, 8, Springer Nature, 2026, doi:<a href=\"https://doi.org/10.1038/s41397-026-00399-0\">10.1038/s41397-026-00399-0</a>.","short":"J. Hajto, M. Piechota, I. Krätschmer, P. Konowalska, G.E. Boyle, D.M. Fowler, M. Borczyk, M. Korostynski, Pharmacogenomics Journal 26 (2026).","ama":"Hajto J, Piechota M, Krätschmer I, et al. Computational variant predictors for pharmacogenomics: From evaluation of single alleles to assessment of adverse drug reactions to antidepressants. <i>Pharmacogenomics Journal</i>. 2026;26(2). doi:<a href=\"https://doi.org/10.1038/s41397-026-00399-0\">10.1038/s41397-026-00399-0</a>","apa":"Hajto, J., Piechota, M., Krätschmer, I., Konowalska, P., Boyle, G. E., Fowler, D. M., … Korostynski, M. (2026). Computational variant predictors for pharmacogenomics: From evaluation of single alleles to assessment of adverse drug reactions to antidepressants. <i>Pharmacogenomics Journal</i>. Springer Nature. <a href=\"https://doi.org/10.1038/s41397-026-00399-0\">https://doi.org/10.1038/s41397-026-00399-0</a>"},"oa_version":"Published Version","language":[{"iso":"eng"}],"issue":"2","publisher":"Springer Nature","date_published":"2026-03-09T00:00:00Z","department":[{"_id":"MaRo"}],"date_updated":"2026-03-30T07:10:50Z","abstract":[{"text":"Currently, pharmacogenetics relies on partially annotated star alleles, leaving novel variants and complex haplotypes uninterpretable. Computational scoring frameworks could overcome these limitations. Here, we comprehensively evaluated the ability of existing (CADD, FATHMM-XF, PROVEAN, MutationAssessor, SIFT, PhyloP100, APF, APF2) and novel (PharmGScore and PharmMLScore) variant effect predictors to assess pharmacogenetic alleles in multiple scenarios. Altogether we analyzed 541 PharmVar alleles, high‑throughput CYP2C9 and CYP2C19 mutational maps, and 200 642 UK Biobank exomes linked with health records containing antidepressant treatment outcomes. Many evaluated tools, especially ensemble frameworks, matched or exceeded star allele classifications (ROC‑AUC up to 0.85 for allele definitions, 0.95 in vitro; TPR up to 0.99 for exomes) and accurately predicted severe antidepressant adverse events for carriers of deleterious variants in CYP2C19 (OR 1.20–1.35). Our findings show that computational predictors deliver star allele accuracy while overcoming their limitations. With additional validation, computational tools could enhance clinical decision frameworks by enabling continuous scoring, incorporating previously unknown variants, and providing genome-wide applicability.","lang":"eng"}],"external_id":{"pmid":["41803106"]},"ddc":["570"]},{"external_id":{"pmid":["41677404"]},"ddc":["570"],"department":[{"_id":"MaRo"}],"date_updated":"2026-07-27T11:56:04Z","abstract":[{"text":"An individual's phenotype reflects a complex interplay of the direct effects of their DNA, epigenetic modifications of their DNA induced by their parents, and indirect effects of their parents' DNA. Here, we derive how the genetic variance within a population is changed under the influence of indirect maternal, paternal and parent-of-origin effects under random mating. We also consider indirect effects of a sibling, in particular how the genetic variance is altered when looking at the phenotypic difference between two siblings. The calculations are then extended to include assortative mating (AM), which alters the variance by inducing increased homozygosity and correlations within and across loci. AM likely leads to covariance of parental genetic effects, a measure of the similarity of parents in the indirect effects they have on their children. We propose that this assortment for parental characteristics, where biological parents create similar environments for their children, can create shared parental effects across traits and the appearance of cross-trait AM. Our theory shows how the resemblance among relatives increases under both AM, indirect and parent-of-origin effects. When our model is used to predict correlations among relatives in human height, we find that explaining the patterns observed in real data requires both indirect genetic effects and assortative mating. The degree to which direct, indirect and epigenetic effects shape the phenotypic variance of complex traits remains an open question that requires large-scale family data to be resolved.","lang":"eng"}],"date_published":"2026-04-01T00:00:00Z","PlanS_conform":"1","issue":"4","related_material":{"link":[{"url":"https://github.com/medical-genomics-group/familyMC","relation":"software"}]},"publisher":"Oxford University Press","oa_version":"Published Version","language":[{"iso":"eng"}],"citation":{"ista":"Krätschmer I, Robinson MR. 2026. A quantitative genetic model for indirect genetic effects and genomic imprinting under random and assortative mating. Genetics. 232(4), iyag042.","mla":"Krätschmer, Ilse, and Matthew Richard Robinson. “A Quantitative Genetic Model for Indirect Genetic Effects and Genomic Imprinting under Random and Assortative Mating.” <i>Genetics</i>, vol. 232, no. 4, iyag042, Oxford University Press, 2026, doi:<a href=\"https://doi.org/10.1093/genetics/iyag042\">10.1093/genetics/iyag042</a>.","ieee":"I. Krätschmer and M. R. Robinson, “A quantitative genetic model for indirect genetic effects and genomic imprinting under random and assortative mating,” <i>Genetics</i>, vol. 232, no. 4. Oxford University Press, 2026.","chicago":"Krätschmer, Ilse, and Matthew Richard Robinson. “A Quantitative Genetic Model for Indirect Genetic Effects and Genomic Imprinting under Random and Assortative Mating.” <i>Genetics</i>. Oxford University Press, 2026. <a href=\"https://doi.org/10.1093/genetics/iyag042\">https://doi.org/10.1093/genetics/iyag042</a>.","short":"I. Krätschmer, M.R. Robinson, Genetics 232 (2026).","ama":"Krätschmer I, Robinson MR. A quantitative genetic model for indirect genetic effects and genomic imprinting under random and assortative mating. <i>Genetics</i>. 2026;232(4). doi:<a href=\"https://doi.org/10.1093/genetics/iyag042\">10.1093/genetics/iyag042</a>","apa":"Krätschmer, I., &#38; Robinson, M. R. (2026). A quantitative genetic model for indirect genetic effects and genomic imprinting under random and assortative mating. <i>Genetics</i>. Oxford University Press. <a href=\"https://doi.org/10.1093/genetics/iyag042\">https://doi.org/10.1093/genetics/iyag042</a>"},"researchdata_availability":"no","corr_author":"1","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","dataavailabilitystatement":"The code used to simulate data can be found at https://github.\r\ncom/medical-genomics-group/familyMC. Simulations are based\r\non genotype data from 1000 Genomes Project downloaded from\r\nhttps://ftp.1000genomes.ebi. ac.uk/vol1/ftp/release/20130502/.\r\nSupplemental material available at GENETICS online.","author":[{"id":"30d4014e-7753-11eb-b44b-db6d61112e73","last_name":"Krätschmer","first_name":"Ilse","orcid":"0000-0002-5636-9259","full_name":"Krätschmer, Ilse"},{"full_name":"Robinson, Matthew Richard","orcid":"0000-0001-8982-8813","last_name":"Robinson","first_name":"Matthew Richard","id":"E5D42276-F5DA-11E9-8E24-6303E6697425"}],"publication_identifier":{"issn":["1943-2631"]},"doi":"10.1093/genetics/iyag042","_id":"21484","article_number":"iyag042","has_accepted_license":"1","oa":1,"article_type":"original","supplementarymaterial":"no","quality_controlled":"1","status":"public","type":"journal_article","year":"2026","publication_status":"published","file_date_updated":"2026-07-27T11:54:00Z","das_tickbox":"1","intvolume":"       232","title":"A quantitative genetic model for indirect genetic effects and genomic imprinting under random and assortative mating","article_processing_charge":"Yes (via OA deal)","month":"04","file":[{"content_type":"application/pdf","date_updated":"2026-07-27T11:54:00Z","file_size":734475,"file_name":"2026_Genetics_Kraetschmer.pdf","success":1,"access_level":"open_access","checksum":"926322c83b02522e96ab7a86e4011eec","date_created":"2026-07-27T11:54:00Z","relation":"main_file","file_id":"22425","creator":"dernst"}],"volume":232,"date_created":"2026-03-23T15:02:54Z","acknowledgement":"We thank members of the Medical Genomics group at ISTA for their comments, which improved this manuscript. This work was funded by an SNSF Eccellenza Grant to MRR (PCEGP3-181181), and by core funding from the Institute of Science and Technology Austria.","OA_place":"publisher","OA_type":"hybrid","scopus_import":"1","day":"01","tmp":{"short":"CC BY (4.0)","legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","image":"/images/cc_by.png","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)"},"pmid":1,"publication":"Genetics"},{"project":[{"_id":"059876FA-7A3F-11EA-A408-12923DDC885E","name":"Prix Lopez-Loretta 2019 - Marco Mondelli"},{"name":"Inference in High Dimensions: Light-speed Algorithms and Information Limits","grant_number":"101161364","_id":"911e6d1f-16d5-11f0-9cad-c5c68c6a1cdf"},{"_id":"9B8D11D6-BA93-11EA-9121-9846C619BF3A","name":"Improving estimation and prediction of common complex disease risk","grant_number":"PCEGP3_181181"}],"keyword":["Approximate Message Passing","GWAS","Genomics","Proteomics","Survival modeling"],"page":"169","title":"From sparse selection to risk prediction: Approximate message passing for proteomic survival models and large-scale genomics","article_processing_charge":"No","month":"07","file":[{"access_level":"open_access","checksum":"9ab386790515628d957a194f30a7ccb4","date_created":"2026-07-13T14:52:19Z","creator":"adepope","file_id":"22316","relation":"main_file","date_updated":"2026-07-13T14:52:19Z","content_type":"application/pdf","file_size":25109878,"file_name":"2026_Depope_Al_Thesis.pdf"},{"date_created":"2026-07-13T14:56:41Z","checksum":"8ed8fb63f76a695d5b6fec35343f4b90","creator":"adepope","file_id":"22317","relation":"source_file","access_level":"closed","file_name":"2026_Depope_Al_Thesis.zip","date_updated":"2026-07-13T14:56:41Z","content_type":"application/zip","file_size":1203199939}],"date_created":"2026-07-10T13:27:20Z","OA_place":"publisher","acknowledgement":"This work was supported in part by the Swiss National Science Foundation through the\r\nEccellenza Grant \"Improving estimation and prediction of common complex disease risk\"\r\n(grant number PCEGP3_181181); the European Research Council through the grant\r\n\"Inference in High Dimensions: Light-speed Algorithms and Information Limits\" (grant\r\nnumber 101161364); and the Fondation Jean-Jacques et Felicia Lopez-Loreta through the\r\nPrix Lopez-Loretta 2019.\r\n","day":"11","_id":"22258","doi":"10.15479/AT-ISTA-22258","publication_identifier":{"issn":["2663-337X"]},"has_accepted_license":"1","oa":1,"publication_status":"published","file_date_updated":"2026-07-13T14:56:41Z","year":"2026","type":"dissertation","status":"public","das_tickbox":"1","alternative_title":["ISTA Thesis"],"language":[{"iso":"eng"}],"oa_version":"Published Version","citation":{"ama":"Depope A. From sparse selection to risk prediction: Approximate message passing for proteomic survival models and large-scale genomics. 2026. doi:<a href=\"https://doi.org/10.15479/AT-ISTA-22258\">10.15479/AT-ISTA-22258</a>","short":"A. Depope, From Sparse Selection to Risk Prediction: Approximate Message Passing for Proteomic Survival Models and Large-Scale Genomics, Institute of Science and Technology Austria, 2026.","apa":"Depope, A. (2026). <i>From sparse selection to risk prediction: Approximate message passing for proteomic survival models and large-scale genomics</i>. Institute of Science and Technology Austria. <a href=\"https://doi.org/10.15479/AT-ISTA-22258\">https://doi.org/10.15479/AT-ISTA-22258</a>","ista":"Depope A. 2026. From sparse selection to risk prediction: Approximate message passing for proteomic survival models and large-scale genomics. Institute of Science and Technology Austria.","mla":"Depope, Al. <i>From Sparse Selection to Risk Prediction: Approximate Message Passing for Proteomic Survival Models and Large-Scale Genomics</i>. Institute of Science and Technology Austria, 2026, doi:<a href=\"https://doi.org/10.15479/AT-ISTA-22258\">10.15479/AT-ISTA-22258</a>.","ieee":"A. Depope, “From sparse selection to risk prediction: Approximate message passing for proteomic survival models and large-scale genomics,” Institute of Science and Technology Austria, 2026.","chicago":"Depope, Al. “From Sparse Selection to Risk Prediction: Approximate Message Passing for Proteomic Survival Models and Large-Scale Genomics.” Institute of Science and Technology Austria, 2026. <a href=\"https://doi.org/10.15479/AT-ISTA-22258\">https://doi.org/10.15479/AT-ISTA-22258</a>."},"corr_author":"1","acknowledged_ssus":[{"_id":"ScienComp"}],"user_id":"8b945eb4-e2f2-11eb-945a-df72226e66a9","author":[{"full_name":"Depope, Al","id":"0b77531d-dbcd-11ea-9d1d-a8eee0bf3830","first_name":"Al","last_name":"Depope"}],"ddc":["576","610","006"],"doi_confirm":"1","date_updated":"2026-07-28T07:08:15Z","department":[{"_id":"GradSch"},{"_id":"MaRo"},{"_id":"MaMo"}],"abstract":[{"text":"Uncovering the genetic architecture of complex traits and pinpointing causal molecular drivers require the ability to distinguish true signals from noise within massive, high-dimensional omics datasets. To extract meaningful biological insights from these datasets, such as identifying causal genetic variants and proteins, scalable and accurate inference methods are essential. To this end, this thesis develops novel Bayesian inference frameworks based on Vector Approximate Message Passing and demonstrates their effectiveness in the modeling of disease onset times and quantitative physical and clinical measures.\r\n\r\nFirst, we introduce gVAMP, a Bayesian framework tailored for Genome-Wide Association Studies that enables the joint modeling of quantitative complex traits across millions of genetic variants. gVAMP demonstrates superior accuracy in variable selection and out-of-sample polygenic risk prediction compared to state-of-the-art approaches. We model human height using 17 million whole-genome sequence variants from the UK Biobank, incorporating a vast number of rare variants and revealing novel associations. gVAMP achieves a prediction accuracy of approximately 46% for human height, representing the highest reported performance for this trait to date. \r\n\r\nSecond, we present vampW, a Bayesian framework for survival analysis applied to proteomic data. By effectively handling right-censoring and complex protein dependencies within the UK Biobank Pharma Proteomics Project dataset, vampW identifies 219 protein associations across 24 disease outcomes, the majority of which are not among the top marginal discoveries. We further adjust protein levels for exponential age effects, yielding 1,308 associations and highlighting the sensitivity of the analysis to the chosen age-correction methodology. Finally, vampW improves upon the variable selection capabilities of the commonly used (penalized) variants of the Cox proportional hazards model and delivers state-of-the-art out-of-sample prediction of disease onset times.\r\n\r\nCollectively, these methods provide powerful tools for dissecting the genetic architecture of complex traits and the proteomic drivers of disease onset. Furthermore, by delivering accurate polygenic risk scores and precise predictions of onset times, this work advances the capabilities of personalized medicine and clinical risk stratification.","lang":"eng"}],"supervisor":[{"id":"E5D42276-F5DA-11E9-8E24-6303E6697425","first_name":"Matthew Richard","last_name":"Robinson","orcid":"0000-0001-8982-8813","full_name":"Robinson, Matthew Richard"},{"full_name":"Mondelli, Marco","orcid":"0000-0002-3242-7020","id":"27EB676C-8706-11E9-9510-7717E6697425","first_name":"Marco","last_name":"Mondelli"}],"date_published":"2026-07-11T00:00:00Z","degree_awarded":"PhD","related_material":{"record":[{"id":"21488","status":"public","relation":"part_of_dissertation"}]},"publisher":"Institute of Science and Technology Austria"},{"abstract":[{"text":"Human height is a model for the genetic analysis of complex traits, and recent studies suggest the presence of thousands of common genetic variant associations and hundreds of low-frequency/rare variants. Here, we develop a new algorithmic paradigm based on approximate message passing (genomic vector approximate message passing [gVAMP]) for identifying DNA sequence variants associated with complex traits and common diseases in large-scale whole-genome sequencing (WGS) data. We show that gVAMP accurately localizes associations to variants with the correct frequency and position in the DNA, outperforming existing fine-mapping methods in selecting the appropriate genetic variants within WGS data. We then apply gVAMP to jointly model the relationship of tens of millions of WGS variants with human height in hundreds of thousands of UK Biobank individuals. We identify 59 rare variants and gene burden scores alongside many hundreds of DNA regions containing common variant associations and show that understanding the genetic basis of complex traits will require the joint analysis of hundreds of millions of variables measured on millions of people. The polygenic risk scores obtained from gVAMP have high accuracy (including a prediction accuracy of ∼46% for human height) and outperform current methods for downstream tasks such as mixed linear model association testing across 13 UK Biobank traits. In conclusion, gVAMP offers a scalable foundation for a wider range of analyses in WGS data.","lang":"eng"}],"date_updated":"2026-07-28T07:08:15Z","department":[{"_id":"MaMo"},{"_id":"MaRo"}],"ddc":["000","570"],"external_id":{"pmid":["41713425"]},"publisher":"Elsevier","related_material":{"record":[{"status":"public","relation":"dissertation_contains","id":"22258"}],"link":[{"relation":"press_release","url":"https://ista.ac.at/en/news/big-data-and-human-height/","description":"News on ISTA website"}]},"issue":"5","date_published":"2026-05-13T00:00:00Z","citation":{"short":"A. Depope, J. Bajzik, M. Mondelli, M.R. Robinson, Cell Genomics 6 (2026).","ama":"Depope A, Bajzik J, Mondelli M, Robinson MR. Joint modeling of whole-genome sequencing data for human height via approximate message passing. <i>Cell Genomics</i>. 2026;6(5). doi:<a href=\"https://doi.org/10.1016/j.xgen.2026.101162\">10.1016/j.xgen.2026.101162</a>","apa":"Depope, A., Bajzik, J., Mondelli, M., &#38; Robinson, M. R. (2026). Joint modeling of whole-genome sequencing data for human height via approximate message passing. <i>Cell Genomics</i>. Elsevier. <a href=\"https://doi.org/10.1016/j.xgen.2026.101162\">https://doi.org/10.1016/j.xgen.2026.101162</a>","ieee":"A. Depope, J. Bajzik, M. Mondelli, and M. R. Robinson, “Joint modeling of whole-genome sequencing data for human height via approximate message passing,” <i>Cell Genomics</i>, vol. 6, no. 5. Elsevier, 2026.","chicago":"Depope, Al, Jakub Bajzik, Marco Mondelli, and Matthew Richard Robinson. “Joint Modeling of Whole-Genome Sequencing Data for Human Height via Approximate Message Passing.” <i>Cell Genomics</i>. Elsevier, 2026. <a href=\"https://doi.org/10.1016/j.xgen.2026.101162\">https://doi.org/10.1016/j.xgen.2026.101162</a>.","ista":"Depope A, Bajzik J, Mondelli M, Robinson MR. 2026. Joint modeling of whole-genome sequencing data for human height via approximate message passing. Cell Genomics. 6(5), 101162.","mla":"Depope, Al, et al. “Joint Modeling of Whole-Genome Sequencing Data for Human Height via Approximate Message Passing.” <i>Cell Genomics</i>, vol. 6, no. 5, 101162, Elsevier, 2026, doi:<a href=\"https://doi.org/10.1016/j.xgen.2026.101162\">10.1016/j.xgen.2026.101162</a>."},"language":[{"iso":"eng"}],"oa_version":"Published Version","author":[{"full_name":"Depope, Al","id":"0b77531d-dbcd-11ea-9d1d-a8eee0bf3830","first_name":"Al","last_name":"Depope"},{"full_name":"Bajzik, Jakub","last_name":"Bajzik","first_name":"Jakub","id":"b995e25b-8c4b-11ed-a6d8-f71b7bcd6122"},{"orcid":"0000-0002-3242-7020","full_name":"Mondelli, Marco","last_name":"Mondelli","first_name":"Marco","id":"27EB676C-8706-11E9-9510-7717E6697425"},{"id":"E5D42276-F5DA-11E9-8E24-6303E6697425","last_name":"Robinson","first_name":"Matthew Richard","orcid":"0000-0001-8982-8813","full_name":"Robinson, Matthew Richard"}],"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","dataavailabilitystatement":"This project uses the UK Biobank data under project number 35520. UK Biobank genotypic and phenotypic data are available through a formal request at http://www.ukbiobank.ac.uk. It also uses genotypic and phenotypic data from the All of Us study, which are also available through a formal request at https://www.researchallofus.org/data-tools/data-access/. All summary statistic estimates are released publicly on Dryad: https://doi.org/10.5061/dryad.cz8w9gjjc.\r\n•\r\nThe gVAMP code developed in this work is open source and has been deposited on GitHub, where it is publicly available at https://github.com/medical-genomics-group/gVAMP, and the code used to generate the data in the manuscript are available from Zenodo https://doi.org/10.5281/zenodo.17935521. The URLs of other software used are listed in the key resources table.","corr_author":"1","researchdata_availability":"yes","article_type":"original","oa":1,"has_accepted_license":"1","article_number":"101162","_id":"21488","doi":"10.1016/j.xgen.2026.101162","publication_identifier":{"eissn":["2666-979X"]},"das_tickbox":"1","file_date_updated":"2026-07-28T07:06:26Z","publication_status":"published","year":"2026","type":"journal_article","status":"public","quality_controlled":"1","supplementarymaterial":"yes","volume":6,"file":[{"file_name":"2026_CellGenomics_Depope.pdf","file_size":3736705,"content_type":"application/pdf","date_updated":"2026-07-28T07:06:26Z","relation":"main_file","file_id":"22596","creator":"dernst","date_created":"2026-07-28T07:06:26Z","checksum":"6b59686f8d9733add4f23d23f3dd9df0","access_level":"open_access","success":1}],"article_processing_charge":"Yes","month":"05","title":"Joint modeling of whole-genome sequencing data for human height via approximate message passing","intvolume":"         6","project":[{"name":"Prix Lopez-Loretta 2019 - Marco Mondelli","_id":"059876FA-7A3F-11EA-A408-12923DDC885E"},{"_id":"911e6d1f-16d5-11f0-9cad-c5c68c6a1cdf","name":"Inference in High Dimensions: Light-speed Algorithms and Information Limits","grant_number":"101161364"},{"grant_number":"PCEGP3_181181","name":"Improving estimation and prediction of common complex disease risk","_id":"9B8D11D6-BA93-11EA-9121-9846C619BF3A"}],"publication":"Cell Genomics","pmid":1,"tmp":{"name":"Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)","short":"CC BY-NC-ND (4.0)","image":"/images/cc_by_nc_nd.png","legal_code_url":"https://creativecommons.org/licenses/by-nc-nd/4.0/legalcode"},"day":"13","DOAJ_listed":"1","scopus_import":"1","OA_type":"gold","acknowledgement":"We thank Malgorzata Borczyk for creating the gene burden scores. We thank Robin Beaumont, Amedeo Roberto Esposito, Gareth Hawkes, Philip Schniter, Matthew Stephens, Pragya Sur, Peter Visscher, Michael Weedon, and Harry Wright for providing valuable suggestions and comments on earlier versions of the work. This project was funded by a Lopez-Loreta Prize to M.M., an SNSF Eccellenza Grant to M.R.R. (PCEGP3-181181), an ERC Starting Grant to M.M. (INF2, project number 101161364), and core funding from ISTA. High-performance computing was supported by the Scientific Service Units (SSU) of ISTA through resources provided by Scientific Computing (SciComp). We would like to acknowledge the participants and investigators of the UK Biobank study. We gratefully acknowledge the All of Us participants for their contributions, without whom this research would not have been possible. We also thank the National Institutes of Health All of Us Research Program for making available the participant data (and/or samples and/or cohort) examined in this study.","OA_place":"publisher","date_created":"2026-03-23T15:10:03Z"},{"researchdata_availability":"upon request","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","dataavailabilitystatement":"Data will be made available on request.","author":[{"full_name":"Depope, Nika","last_name":"Depope","first_name":"Nika"},{"full_name":"Depope, Al","last_name":"Depope","first_name":"Al","id":"0b77531d-dbcd-11ea-9d1d-a8eee0bf3830"},{"full_name":"Archodoulaki, Vasiliki Maria","last_name":"Archodoulaki","first_name":"Vasiliki Maria"},{"full_name":"Dabrowska, Alicja","first_name":"Alicja","last_name":"Dabrowska"},{"last_name":"Lendl","first_name":"Bernhard","full_name":"Lendl, Bernhard"},{"last_name":"Mautner","first_name":"Andreas","full_name":"Mautner, Andreas"},{"first_name":"Wolfgang","last_name":"Ipsmiller","full_name":"Ipsmiller, Wolfgang"},{"first_name":"Andreas","last_name":"Bartl","full_name":"Bartl, Andreas"}],"oa_version":"Published Version","language":[{"iso":"eng"}],"citation":{"ama":"Depope N, Depope A, Archodoulaki VM, et al. DES formulation for recycling synthetic fibre textile waste containing elastane. <i>Waste Management</i>. 2026;224. doi:<a href=\"https://doi.org/10.1016/j.wasman.2026.115736\">10.1016/j.wasman.2026.115736</a>","short":"N. Depope, A. Depope, V.M. Archodoulaki, A. Dabrowska, B. Lendl, A. Mautner, W. Ipsmiller, A. Bartl, Waste Management 224 (2026).","apa":"Depope, N., Depope, A., Archodoulaki, V. M., Dabrowska, A., Lendl, B., Mautner, A., … Bartl, A. (2026). DES formulation for recycling synthetic fibre textile waste containing elastane. <i>Waste Management</i>. Elsevier. <a href=\"https://doi.org/10.1016/j.wasman.2026.115736\">https://doi.org/10.1016/j.wasman.2026.115736</a>","mla":"Depope, Nika, et al. “DES Formulation for Recycling Synthetic Fibre Textile Waste Containing Elastane.” <i>Waste Management</i>, vol. 224, 115736, Elsevier, 2026, doi:<a href=\"https://doi.org/10.1016/j.wasman.2026.115736\">10.1016/j.wasman.2026.115736</a>.","ista":"Depope N, Depope A, Archodoulaki VM, Dabrowska A, Lendl B, Mautner A, Ipsmiller W, Bartl A. 2026. DES formulation for recycling synthetic fibre textile waste containing elastane. Waste Management. 224, 115736.","ieee":"N. Depope <i>et al.</i>, “DES formulation for recycling synthetic fibre textile waste containing elastane,” <i>Waste Management</i>, vol. 224. Elsevier, 2026.","chicago":"Depope, Nika, Al Depope, Vasiliki Maria Archodoulaki, Alicja Dabrowska, Bernhard Lendl, Andreas Mautner, Wolfgang Ipsmiller, and Andreas Bartl. “DES Formulation for Recycling Synthetic Fibre Textile Waste Containing Elastane.” <i>Waste Management</i>. Elsevier, 2026. <a href=\"https://doi.org/10.1016/j.wasman.2026.115736\">https://doi.org/10.1016/j.wasman.2026.115736</a>."},"date_published":"2026-07-15T00:00:00Z","PlanS_conform":"1","publisher":"Elsevier","external_id":{"pmid":["42456595"]},"ddc":["540"],"department":[{"_id":"MaRo"}],"date_updated":"2026-08-03T06:35:45Z","abstract":[{"text":"This study presents a sustainable and non-destructive recycling strategy for synthetic fibres, specifically polyethylene terephthalate (PET), polyamide 6 (PA6), polyamide 66 (PA66), and elastane (EL). Two deep eutectic solvents (DESs) were synthesised, enabling selective dissolution of EL without degrading the surrounding polymer matrices. The dissolved EL phase was subsequently recovered by centrifugation, demonstrating its potential for material reuse. Comprehensive characterisation confirmed that the process preserves fibre integrity. SEM imaging revealed no detectable changes in fibre morphology, while DSC and TGA analyses indicated that the thermal properties of both EL and synthetic polymers were preserved. ATR-FTIR spectroscopy verified the absence of measurable chemical modifications. Mechanical testing showed that solvent treatment did not compromise the tensile performance of PET, PA6, or PA66 fibres, confirming their suitability for fibre-to-fibre recycling.\r\nThe recovered fibres were successfully re-extruded into continuous filaments, confirming their melt processability and enabling potential applications in textile yarns, technical fibres, nonwovens, and polymer components via melt-spinning or moulding. These results demonstrate that the proposed DES-based method offers a promising approach for the circular recycling of complex fibre blends and provides a viable pathway towards industrial implementation.","lang":"eng"}],"acknowledgement":"The financial support by the Austrian Federal Ministry of Economy, Energy and Tourism, the National Foundation for Research, Technology and Development and the Christian Doppler Research Association is gratefully acknowledged. The authors acknowledge “Open Access Funding by TU Wien” for financial support through its Open Access Funding Program.\r\nSpecial thanks are extended to EREMA Group GmbH, SALESIANER MIETTEX GmbH and Starlinger & Co GmbH for their material support and valuable input throughout the development of this study.","OA_place":"publisher","date_created":"2026-08-02T22:01:51Z","scopus_import":"1","OA_type":"hybrid","day":"15","pmid":1,"tmp":{"short":"CC BY (4.0)","legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","image":"/images/cc_by.png","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)"},"publication":"Waste Management","intvolume":"       224","title":"DES formulation for recycling synthetic fibre textile waste containing elastane","article_processing_charge":"Yes (via OA deal)","month":"07","volume":224,"supplementarymaterial":"yes","quality_controlled":"1","type":"journal_article","status":"public","publication_status":"epub_ahead","year":"2026","das_tickbox":"1","doi":"10.1016/j.wasman.2026.115736","publication_identifier":{"issn":["0956-053X"],"eissn":["1879-2456"]},"_id":"22614","main_file_link":[{"open_access":"1","url":"https://doi.org/10.1016/j.wasman.2026.115736"}],"article_number":"115736","has_accepted_license":"1","oa":1,"article_type":"original"},{"scopus_import":"1","OA_type":"gold","DOAJ_listed":"1","acknowledgement":"We thank Zoltan Kutalik, Peter Visscher, and members of the Robinson group at ISTA for their comments, which improved this manuscript. This work was funded by an SNSF Eccellenza Grant to M.R.R. (PCEGP3-181181) and by core funding from the Institute of Science and Technology Austria.\r\nThe Norwegian Mother, Father, and Child Cohort Study is supported by the Norwegian Ministry of Health and Care Services and the Ministry of Education and Research. We are grateful to all the participating families in Norway who take part in this on-going cohort study. We thank the Norwegian Institute of Public Health (NIPH) for generating high-quality genomic data. The research is part of the HARVEST collaboration, supported by the Research Council of Norway (#229624). We also thank the NORMENT Center for providing genotype data, funded by the Research Council of Norway (#223273), South East Norway Health Authorities, and Stiftelsen Kristian Gerhard Jebsen, and in collaboration with deCODE Genetics. We further thank the Center for Diabetes Research, the University of Bergen for providing genotype data funded by the ERC AdG project SELECTionPREDISPOSED, Stiftelsen Kristian Gerhard Jebsen, Trond Mohn Foundation, the Research Council of Norway, the Novo Nordisk Foundation, the University of Bergen, and the Western Norway Health Authorities. The MoBa work was performed on the TSD (Tjeneste for Sensitive Data) facilities, owned by the University of Oslo, operated and developed by the TSD service group at the University of Oslo, IT Department (USIT, tsd-drift@usit.uio.no). E.Y. is supported by the European Union (grant numbers 101045526 and 101073237) and the Research Council of Norway (grant numbers 336078, 288083, and 331640).\r\nWe would like to acknowledge the participants and investigators of the Generation Scotland Cohort study. Generation Scotland received core support from the Chief Scientist Office of the Scottish Government Health Directorates (CZD/16/6) and the Scottish Funding Council (HR03006). Genotyping and methylation typing of the GS:SFHS samples was carried out by the Genetics Core Laboratory at the Wellcome Trust Clinical Research Facility, Edinburgh, Scotland and was funded by the Medical Research Council UK and the Wellcome Trust (Wellcome Trust Strategic Award “STratifying Resilience and Depression Longitudinally” [STRADL] ref. 104036/Z/14/Z).\r\nWe would like to thank and acknowledge the participants and investigators of the Estonian Biobank (EstBB) study. The research was conducted using the Estonian Center of Genomics/Roadmap II funded by the Estonian Research Council (project number TT17).\r\nNorwegian analyses were performed on resources provided by Sigma2 - the National Infrastructure for High-Performance Computing and Data Storage in Norway. Estonian Data analysis was carried out in the High-Performance Computing Center cloud provided by University of Tartu. Analysis of the Generation Scotland data and the summary statistics obtained from the other analyses was conducted at IST Austria and is supported by the Scientific Service Units (SSU) of IST Austria through resources provided by Scientific Computing (SciComp).","date_created":"2026-06-10T07:39:08Z","OA_place":"publisher","pmid":1,"tmp":{"name":"Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)","short":"CC BY-NC-ND (4.0)","image":"/images/cc_by_nc_nd.png","legal_code_url":"https://creativecommons.org/licenses/by-nc-nd/4.0/legalcode"},"day":"08","publication":"Cell Genomics","intvolume":"         6","project":[{"name":"Improving estimation and prediction of common complex disease risk","grant_number":"PCEGP3_181181","_id":"9B8D11D6-BA93-11EA-9121-9846C619BF3A"}],"keyword":["direct genetic effects","DGE","indirect genetic effects","IGE","parent-of-origin effects","phenotypic variation","assortative mating","within-family GWAS","MoBa","EstBB"],"title":"Separating direct, indirect, and parent-of-origin genetic effects in the human population","month":"07","article_processing_charge":"Yes","volume":6,"file":[{"date_updated":"2026-07-28T07:24:50Z","content_type":"application/pdf","file_size":3679297,"file_name":"2026_CellGenomics_Kraetschmer.pdf","success":1,"access_level":"open_access","date_created":"2026-07-28T07:24:50Z","checksum":"f896b510480d2d4e4a7fd46c2e2761f4","relation":"main_file","file_id":"22597","creator":"dernst"}],"quality_controlled":"1","supplementarymaterial":"yes","file_date_updated":"2026-07-28T07:24:50Z","publication_status":"published","year":"2026","type":"journal_article","status":"public","das_tickbox":"1","_id":"21987","publication_identifier":{"eissn":["2666-979X"]},"doi":"10.1016/j.xgen.2026.101277","article_number":"101277","has_accepted_license":"1","article_type":"original","oa":1,"corr_author":"1","researchdata_availability":"yes","acknowledged_ssus":[{"_id":"ScienComp"}],"user_id":"ba8df636-2132-11f1-aed0-ed93e2281fdd","dataavailabilitystatement":"Information on how to access the MoBaPsychGen post-imputation QC data are available here: https://www.fhi.no/en/me/the-psychgen-centre-for-genetic-epidemiology-and-mental-health/access-to-genetic-data-after-quality-control-by-the-mobapsychgen-pipeline-v/.\r\nEstonian Biobank data (https://genomics.ut.ee/en/content/estonian-biobank) were used in this project. For access to be granted to the Estonian Biobank genotypic and corresponding phenotypic data, a preliminary application must be presented to the oversight committee, who must first approve the project. Ethics permission must then be obtained from the Estonian Committee on Bioethics and Human Research. Finally, a full project must be submitted and approved by the Estonian Biobank.\r\nAccess to the Generation Scotland data is available with appropriate permission from the Generation Scotland Access Committee. Applications should be made to access@generationscotland.org (https://genscot.ed.ac.uk/).\r\nThe code for JODIE developed in this work is open source and is publicly available on zenodo (https://doi.org/10.5281/zenodo.19593928) and GitHub (https://github.com/medical-genomics-group/JODIE).\r\nHaplotype Reference Consortium Release 1.1 data (https://ega-archive.org/datasets/EGAD00001002729) are available by application to a Data Access Committee (DAC) of the Wellcome Trust Sanger Institute.\r\nThe Common Metabolic Diseases Atlas can be accessed here: https://cmdga.org.","author":[{"last_name":"Krätschmer","first_name":"Ilse","id":"30d4014e-7753-11eb-b44b-db6d61112e73","orcid":"0000-0002-5636-9259","full_name":"Krätschmer, Ilse"},{"first_name":"Laura","last_name":"Hegemann","full_name":"Hegemann, Laura"},{"last_name":"Hofmeister","first_name":"Robin J.","full_name":"Hofmeister, Robin J."},{"last_name":"Corfield","first_name":"Elizabeth C.","full_name":"Corfield, Elizabeth C."},{"full_name":"Mahmoudi, Mahdi","first_name":"Mahdi","last_name":"Mahmoudi"},{"last_name":"Delaneau","first_name":"Olivier","full_name":"Delaneau, Olivier"},{"full_name":"Andreassen, Ole A.","first_name":"Ole A.","last_name":"Andreassen"},{"full_name":"Campbell, Archie","first_name":"Archie","last_name":"Campbell"},{"full_name":"Hayward, Caroline","first_name":"Caroline","last_name":"Hayward"},{"first_name":"Riccardo E.","last_name":"Marioni","full_name":"Marioni, Riccardo E."},{"full_name":"Ystrom, Eivind","first_name":"Eivind","last_name":"Ystrom"},{"last_name":"Havdahl","first_name":"Alexandra","full_name":"Havdahl, Alexandra"},{"full_name":"Robinson, Matthew Richard","orcid":"0000-0001-8982-8813","last_name":"Robinson","first_name":"Matthew Richard","id":"E5D42276-F5DA-11E9-8E24-6303E6697425"}],"language":[{"iso":"eng"}],"oa_version":"Published Version","citation":{"apa":"Krätschmer, I., Hegemann, L., Hofmeister, R. J., Corfield, E. C., Mahmoudi, M., Delaneau, O., … Robinson, M. R. (2026). Separating direct, indirect, and parent-of-origin genetic effects in the human population. <i>Cell Genomics</i>. Elsevier. <a href=\"https://doi.org/10.1016/j.xgen.2026.101277\">https://doi.org/10.1016/j.xgen.2026.101277</a>","short":"I. Krätschmer, L. Hegemann, R.J. Hofmeister, E.C. Corfield, M. Mahmoudi, O. Delaneau, O.A. Andreassen, A. Campbell, C. Hayward, R.E. Marioni, E. Ystrom, A. Havdahl, M.R. Robinson, Cell Genomics 6 (2026).","ama":"Krätschmer I, Hegemann L, Hofmeister RJ, et al. Separating direct, indirect, and parent-of-origin genetic effects in the human population. <i>Cell Genomics</i>. 2026;6(7). doi:<a href=\"https://doi.org/10.1016/j.xgen.2026.101277\">10.1016/j.xgen.2026.101277</a>","chicago":"Krätschmer, Ilse, Laura Hegemann, Robin J. Hofmeister, Elizabeth C. Corfield, Mahdi Mahmoudi, Olivier Delaneau, Ole A. Andreassen, et al. “Separating Direct, Indirect, and Parent-of-Origin Genetic Effects in the Human Population.” <i>Cell Genomics</i>. Elsevier, 2026. <a href=\"https://doi.org/10.1016/j.xgen.2026.101277\">https://doi.org/10.1016/j.xgen.2026.101277</a>.","ieee":"I. Krätschmer <i>et al.</i>, “Separating direct, indirect, and parent-of-origin genetic effects in the human population,” <i>Cell Genomics</i>, vol. 6, no. 7. Elsevier, 2026.","mla":"Krätschmer, Ilse, et al. “Separating Direct, Indirect, and Parent-of-Origin Genetic Effects in the Human Population.” <i>Cell Genomics</i>, vol. 6, no. 7, 101277, Elsevier, 2026, doi:<a href=\"https://doi.org/10.1016/j.xgen.2026.101277\">10.1016/j.xgen.2026.101277</a>.","ista":"Krätschmer I, Hegemann L, Hofmeister RJ, Corfield EC, Mahmoudi M, Delaneau O, Andreassen OA, Campbell A, Hayward C, Marioni RE, Ystrom E, Havdahl A, Robinson MR. 2026. Separating direct, indirect, and parent-of-origin genetic effects in the human population. Cell Genomics. 6(7), 101277."},"date_published":"2026-07-08T00:00:00Z","issue":"7","related_material":{"link":[{"relation":"press_release","url":"https://ista.ac.at/en/news/human-traits-beyond-inherited-genes/","description":"News on ISTA website"}]},"publisher":"Elsevier","ddc":["570"],"external_id":{"pmid":["40909755"]},"date_updated":"2026-08-04T09:34:08Z","department":[{"_id":"MaRo"}],"abstract":[{"text":"We introduce JODIE, a genetic joint modeling approach that estimates how DNA loci influence human traits by partitioning genetic effects into four components: direct effects (from a child’s alleles), indirect maternal and paternal effects (from parents’ alleles), and parent-of-origin (PofO) effects (dependent on parental transmission of alleles), while uniquely accounting for assortative mating. We analyze 30,000 child-mother-father trios from the Estonian Biobank and the Norwegian Mother, Father, and Child Cohort, focusing on height, body mass index, and childhood educational test scores. We find direct effects to be the largest contributor to trait variation, but combined, indirect parental and PofO effects are similarly substantial. We support our results by within-family genome-wide association testing and identify 276 independently associated DNA regions with a complex interplay between direct, indirect, and PofO effects. By joint modeling, we show that direct, indirect, and PofO effects collectively shape human phenotypic variation across loci genome-wide.","lang":"eng"}]},{"OA_place":"publisher","date_created":"2025-01-05T23:01:56Z","OA_type":"hybrid","scopus_import":"1","day":"02","pmid":1,"tmp":{"short":"CC BY (4.0)","legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","image":"/images/cc_by.png","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)"},"publication":"American Journal of Human Genetics","intvolume":"       112","title":"DNA methylation-based predictors of metabolic traits in Scottish and Singaporean cohorts","page":"106-115","month":"01","article_processing_charge":"No","file":[{"success":1,"access_level":"open_access","date_created":"2025-01-08T09:26:42Z","checksum":"891d120554f07da2c35d38388c29a690","file_id":"18776","creator":"dernst","relation":"main_file","content_type":"application/pdf","date_updated":"2025-01-08T09:26:42Z","file_size":2266488,"file_name":"2025_AJHG_Smith.pdf"}],"volume":112,"quality_controlled":"1","status":"public","type":"journal_article","publication_status":"published","file_date_updated":"2025-01-08T09:26:42Z","year":"2025","publication_identifier":{"issn":["0002-9297"],"eissn":["1537-6605"]},"doi":"10.1016/j.ajhg.2024.11.012","_id":"18754","has_accepted_license":"1","oa":1,"article_type":"original","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","author":[{"full_name":"Smith, Hannah M.","last_name":"Smith","first_name":"Hannah M."},{"full_name":"Ng, Hong Kiat","last_name":"Ng","first_name":"Hong Kiat"},{"last_name":"Moodie","first_name":"Joanna E.","full_name":"Moodie, Joanna E."},{"full_name":"Gadd, Danni A.","last_name":"Gadd","first_name":"Danni A."},{"full_name":"Mccartney, Daniel L.","first_name":"Daniel L.","last_name":"Mccartney"},{"full_name":"Bernabeu, Elena","first_name":"Elena","last_name":"Bernabeu"},{"full_name":"Campbell, Archie","last_name":"Campbell","first_name":"Archie"},{"first_name":"Paul","last_name":"Redmond","full_name":"Redmond, Paul"},{"last_name":"Taylor","first_name":"Adele","full_name":"Taylor, Adele"},{"full_name":"Page, Danielle","first_name":"Danielle","last_name":"Page"},{"last_name":"Corley","first_name":"Janie","full_name":"Corley, Janie"},{"full_name":"Harris, Sarah E.","last_name":"Harris","first_name":"Sarah E."},{"full_name":"Tay, Darwin","first_name":"Darwin","last_name":"Tay"},{"full_name":"Deary, Ian J.","first_name":"Ian J.","last_name":"Deary"},{"first_name":"Kathryn L.","last_name":"Evans","full_name":"Evans, Kathryn L."},{"first_name":"Matthew Richard","last_name":"Robinson","id":"E5D42276-F5DA-11E9-8E24-6303E6697425","full_name":"Robinson, Matthew Richard","orcid":"0000-0001-8982-8813"},{"full_name":"Chambers, John C.","last_name":"Chambers","first_name":"John C."},{"full_name":"Loh, Marie","first_name":"Marie","last_name":"Loh"},{"full_name":"Cox, Simon R.","last_name":"Cox","first_name":"Simon R."},{"first_name":"Riccardo E.","last_name":"Marioni","full_name":"Marioni, Riccardo E."},{"last_name":"Hillary","first_name":"Robert F.","full_name":"Hillary, Robert F."}],"oa_version":"Published Version","language":[{"iso":"eng"}],"citation":{"mla":"Smith, Hannah M., et al. “DNA Methylation-Based Predictors of Metabolic Traits in Scottish and Singaporean Cohorts.” <i>American Journal of Human Genetics</i>, vol. 112, no. 1, Elsevier, 2025, pp. 106–15, doi:<a href=\"https://doi.org/10.1016/j.ajhg.2024.11.012\">10.1016/j.ajhg.2024.11.012</a>.","ista":"Smith HM, Ng HK, Moodie JE, Gadd DA, Mccartney DL, Bernabeu E, Campbell A, Redmond P, Taylor A, Page D, Corley J, Harris SE, Tay D, Deary IJ, Evans KL, Robinson MR, Chambers JC, Loh M, Cox SR, Marioni RE, Hillary RF. 2025. DNA methylation-based predictors of metabolic traits in Scottish and Singaporean cohorts. American Journal of Human Genetics. 112(1), 106–115.","chicago":"Smith, Hannah M., Hong Kiat Ng, Joanna E. Moodie, Danni A. Gadd, Daniel L. Mccartney, Elena Bernabeu, Archie Campbell, et al. “DNA Methylation-Based Predictors of Metabolic Traits in Scottish and Singaporean Cohorts.” <i>American Journal of Human Genetics</i>. Elsevier, 2025. <a href=\"https://doi.org/10.1016/j.ajhg.2024.11.012\">https://doi.org/10.1016/j.ajhg.2024.11.012</a>.","ieee":"H. M. Smith <i>et al.</i>, “DNA methylation-based predictors of metabolic traits in Scottish and Singaporean cohorts,” <i>American Journal of Human Genetics</i>, vol. 112, no. 1. Elsevier, pp. 106–115, 2025.","apa":"Smith, H. M., Ng, H. K., Moodie, J. E., Gadd, D. A., Mccartney, D. L., Bernabeu, E., … Hillary, R. F. (2025). DNA methylation-based predictors of metabolic traits in Scottish and Singaporean cohorts. <i>American Journal of Human Genetics</i>. Elsevier. <a href=\"https://doi.org/10.1016/j.ajhg.2024.11.012\">https://doi.org/10.1016/j.ajhg.2024.11.012</a>","short":"H.M. Smith, H.K. Ng, J.E. Moodie, D.A. Gadd, D.L. Mccartney, E. Bernabeu, A. Campbell, P. Redmond, A. Taylor, D. Page, J. Corley, S.E. Harris, D. Tay, I.J. Deary, K.L. Evans, M.R. Robinson, J.C. Chambers, M. Loh, S.R. Cox, R.E. Marioni, R.F. Hillary, American Journal of Human Genetics 112 (2025) 106–115.","ama":"Smith HM, Ng HK, Moodie JE, et al. DNA methylation-based predictors of metabolic traits in Scottish and Singaporean cohorts. <i>American Journal of Human Genetics</i>. 2025;112(1):106-115. doi:<a href=\"https://doi.org/10.1016/j.ajhg.2024.11.012\">10.1016/j.ajhg.2024.11.012</a>"},"date_published":"2025-01-02T00:00:00Z","issue":"1","publisher":"Elsevier","related_material":{"link":[{"relation":"software","url":"https://github.com/marioni-group/Metabolic_trait"}]},"isi":1,"external_id":{"isi":["001412498600001"],"pmid":["39706196"]},"ddc":["570"],"department":[{"_id":"MaRo"}],"date_updated":"2025-02-27T12:38:23Z","abstract":[{"lang":"eng","text":"Exploring the molecular correlates of metabolic health measures may identify their shared and unique biological processes and pathways. Molecular proxies of these traits may also provide a more objective approach to their measurement. Here, DNA methylation (DNAm) data were used in epigenome-wide association studies (EWASs) and for training epigenetic scores (EpiScores) of six metabolic traits: body mass index (BMI), body fat percentage, waist-hip ratio, and blood-based measures of glucose, high-density lipoprotein cholesterol, and total cholesterol in >17,000 volunteers from the Generation Scotland (GS) cohort. We observed a maximum of 12,033 significant findings (p < 3.6 × 10−8) for BMI in a marginal linear regression EWAS. By contrast, a joint and conditional Bayesian penalized regression approach yielded 27 high-confidence associations with BMI. EpiScores trained in GS performed well in both Scottish and Singaporean test cohorts (Lothian Birth Cohort 1936 [LBC1936] and Health for Life in Singapore [HELIOS]). The EpiScores for BMI and total cholesterol performed best in HELIOS, explaining 20.8% and 7.1% of the variance in the measured traits, respectively. The corresponding results in LBC1936 were 14.4% and 3.2%, respectively. Differences were observed in HELIOS for body fat, where the EpiScore explained ∼9% of the variance in Chinese and Malay -subgroups but ∼3% in the Indian subgroup. The EpiScores also correlated with cognitive function in LBC1936 (standardized βrange: 0.08–0.12, false discovery rate p [pFDR] < 0.05). Accounting for the correlation structure across the methylome can vastly affect the number of lead findings in EWASs. The EpiScores of metabolic traits are broadly applicable across populations and can reflect differences in cognition."}]},{"has_accepted_license":"1","oa":1,"article_type":"original","doi":"10.1186/s13148-025-01818-y","publication_identifier":{"issn":["1868-7075"],"eissn":["1868-7083"]},"_id":"19023","article_number":"14","quality_controlled":"1","status":"public","type":"journal_article","publication_status":"published","year":"2025","file_date_updated":"2025-02-17T08:44:23Z","article_processing_charge":"Yes","month":"01","file":[{"creator":"dernst","file_id":"19030","relation":"main_file","checksum":"c32511f2d09e6c164116793e784944b8","date_created":"2025-02-17T08:44:23Z","access_level":"open_access","success":1,"file_name":"2025_ClinicalEpigenetics_Bernabeu.pdf","file_size":1170930,"content_type":"application/pdf","date_updated":"2025-02-17T08:44:23Z"}],"volume":17,"project":[{"_id":"9B8D11D6-BA93-11EA-9121-9846C619BF3A","name":"Improving estimation and prediction of common complex disease risk","grant_number":"PCEGP3_181181"}],"intvolume":"        17","title":"Blood-based epigenome-wide association study and prediction of alcohol consumption","publication":"Clinical Epigenetics","date_created":"2025-02-16T23:02:33Z","acknowledgement":"Generation Scotland: Generation Scotland received core support from the Chief Scientist Office of the Scottish Government Health Directorates (CZD/16/6) and the Scottish Funding Council (HR03006). Genotyping and DNA methylation profiling of the Generation Scotland samples were carried out by the Genetics Core Laboratory at the Edinburgh Clinical Research Facility, Edinburgh, Scotland, and were funded by the Medical Research Council UK and the Wellcome Trust (Wellcome Trust Strategic Award STratifying Resilience and Depression Longitudinally (STRADL; Reference 104036/Z/14/Z) and 220857/Z/20/Z. The DNA methylation data assayed for Generation Scotland were partially funded by a 2018 NARSAD Young Investigator Grant from the Brain & Behavior Research Foundation (Ref: 27404; awardee: Dr David M Howard) and by a JMAS SIM fellowship from the Royal College of Physicians of Edinburgh (Awardee: Dr Heather C Whalley). Lothian Birth Cohorts: We thank the LBC1921 and LBC1936 participants and team members who contributed to these studies. The LBC1921 was supported by the UK’s Biotechnology and Biological Sciences Research Council (BBSRC), The Royal Society, and The Chief Scientist Office of the Scottish Government. The LBC1936 is supported by the BBSRC, and the Economic and Social Research Council [BB/W008793/1] (which supports S.E.H.), Age UK (Disconnected Mind project), the Milton Damerel Trust, the Medical Research Council (MR/M01311/1), and the University of Edinburgh. Methylation typing of LBC1936 was supported by the Centre for Cognitive Ageing and Cognitive Epidemiology (Pilot Fund award), Age UK, The Wellcome Trust Institutional Strategic Support Fund, The University of Edinburgh, and The University of Queensland. Genotyping was funded by the BBSRC (BB/F019394/1). S.R.C. is supported by a Sir Henry Dale Fellowship jointly funded by the Wellcome Trust and the Royal Society (Grant Number 221890/Z/20/Z). ALSPAC: The UK Medical Research Council and Wellcome (Grant ref: 217065/Z/19/Z) and the University of Bristol provide core support for ALSPAC. This publication is the work of the authors and Matthew Suderman will serve as guarantors for the contents of this paper. A comprehensive list of grants funding is available on the ALSPAC website (http://www.bristol.ac.uk/alspac/external/documents/grant-acknowledgements.pdf). Funding for ALSPAC DNAm measurements was supported by the Wellcome (102215/2/13/2); the University of Bristol; the UK Economic and Social Research Council (ES/N000498/1); the UK Medical Research Council (MC_UU_12013/1, MC_UU_12013/2); and the John Templeton Foundation (60828). MS and PY work within the MRC Integrative Epidemiology Unit at the University of Bristol, which is supported by the Medical Research Council (MC_UU_00011/5). Sister Study: This research was supported by the Intramural Research Program of the National Institutes of Health (Z01-ES049033, Z01-ES049032, Z01-ES044005). A.D.C. was supported by a Medical Research Council PhD Studentship in Precision Medicine with funding from the Medical Research Council Doctoral Training Program and the University of Edinburgh College of Medicine and Veterinary Medicine. R.F.H is supported by an MRC IEU Fellowship. M.R.R. was funded by Swiss National Science Foundation Eccellenza Grant PCEGP3-181181 and by core funding from the Institute of Science and Technology Austria. E.B. and R.E.M. are supported by Alzheimer’s Society major project grant AS-PG-19b-010. This research was funded in whole, or in part, by the Wellcome Trust (104036/Z/14/Z, 220857/Z/20/Z, and 221890/Z/20/Z). For the purpose of open access, the author has applied a CC BY public copyright licence to any Author Accepted Manuscript version arising from this submission.","OA_place":"publisher","OA_type":"gold","scopus_import":"1","DOAJ_listed":"1","day":"25","tmp":{"short":"CC BY (4.0)","legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","image":"/images/cc_by.png","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)"},"pmid":1,"department":[{"_id":"MaRo"}],"date_updated":"2025-09-30T10:31:08Z","abstract":[{"text":"Alcohol consumption is an important risk factor for multiple diseases. It is typically assessed via self-report, which is open to measurement error through recall bias. Instead, molecular data such as blood-based DNA methylation (DNAm) could be used to derive a more objective measure of alcohol consumption by incorporating information from cytosine-phosphate-guanine (CpG) sites known to be linked to the trait. Here, we explore the epigenetic architecture of self-reported weekly units of alcohol consumption in the Generation Scotland study. We first create a blood-based epigenetic score (EpiScore) of alcohol consumption using elastic net penalized linear regression. We explore the effect of pre-filtering for CpG features ahead of elastic net, as well as differential patterns by sex and by units consumed in the last week relative to an average week. The final EpiScore was trained on 16,717 individuals and tested in four external cohorts: the Lothian Birth Cohorts (LBC) of 1921 and 1936, the Sister Study, and the Avon Longitudinal Study of Parents and Children (total N across studies > 10,000). The maximum Pearson correlation between the EpiScore and self-reported alcohol consumption within cohort ranged from 0.41 to 0.53. In LBC1936, higher EpiScore levels had significant associations with poorer global brain imaging metrics, whereas self-reported alcohol consumption did not. Finally, we identified two novel CpG loci via a Bayesian penalized regression epigenome-wide association study of alcohol consumption. Together, these findings show how DNAm can objectively characterize patterns of alcohol consumption that associate with brain health, unlike self-reported estimates.","lang":"eng"}],"isi":1,"external_id":{"pmid":["39863868"],"isi":["001406495600001"]},"ddc":["570"],"publisher":"Springer Nature","date_published":"2025-01-25T00:00:00Z","citation":{"ieee":"E. Bernabeu <i>et al.</i>, “Blood-based epigenome-wide association study and prediction of alcohol consumption,” <i>Clinical Epigenetics</i>, vol. 17. Springer Nature, 2025.","chicago":"Bernabeu, Elena, Aleksandra D. Chybowska, Jacob K. Kresovich, Matthew Suderman, Daniel L. Mccartney, Robert F. Hillary, Janie Corley, et al. “Blood-Based Epigenome-Wide Association Study and Prediction of Alcohol Consumption.” <i>Clinical Epigenetics</i>. Springer Nature, 2025. <a href=\"https://doi.org/10.1186/s13148-025-01818-y\">https://doi.org/10.1186/s13148-025-01818-y</a>.","mla":"Bernabeu, Elena, et al. “Blood-Based Epigenome-Wide Association Study and Prediction of Alcohol Consumption.” <i>Clinical Epigenetics</i>, vol. 17, 14, Springer Nature, 2025, doi:<a href=\"https://doi.org/10.1186/s13148-025-01818-y\">10.1186/s13148-025-01818-y</a>.","ista":"Bernabeu E, Chybowska AD, Kresovich JK, Suderman M, Mccartney DL, Hillary RF, Corley J, Valdés-Hernández MDC, Maniega SM, Bastin ME, Wardlaw JM, Xu Z, Sandler DP, Campbell A, Harris SE, Mcintosh AM, Taylor JA, Yousefi P, Cox SR, Evans KL, Robinson MR, Vallejos CA, Marioni RE. 2025. Blood-based epigenome-wide association study and prediction of alcohol consumption. Clinical Epigenetics. 17, 14.","short":"E. Bernabeu, A.D. Chybowska, J.K. Kresovich, M. Suderman, D.L. Mccartney, R.F. Hillary, J. Corley, M.D.C. Valdés-Hernández, S.M. Maniega, M.E. Bastin, J.M. Wardlaw, Z. Xu, D.P. Sandler, A. Campbell, S.E. Harris, A.M. Mcintosh, J.A. Taylor, P. Yousefi, S.R. Cox, K.L. Evans, M.R. Robinson, C.A. Vallejos, R.E. Marioni, Clinical Epigenetics 17 (2025).","ama":"Bernabeu E, Chybowska AD, Kresovich JK, et al. Blood-based epigenome-wide association study and prediction of alcohol consumption. <i>Clinical Epigenetics</i>. 2025;17. doi:<a href=\"https://doi.org/10.1186/s13148-025-01818-y\">10.1186/s13148-025-01818-y</a>","apa":"Bernabeu, E., Chybowska, A. D., Kresovich, J. K., Suderman, M., Mccartney, D. L., Hillary, R. F., … Marioni, R. E. (2025). Blood-based epigenome-wide association study and prediction of alcohol consumption. <i>Clinical Epigenetics</i>. Springer Nature. <a href=\"https://doi.org/10.1186/s13148-025-01818-y\">https://doi.org/10.1186/s13148-025-01818-y</a>"},"oa_version":"Published Version","language":[{"iso":"eng"}],"user_id":"317138e5-6ab7-11ef-aa6d-ffef3953e345","author":[{"last_name":"Bernabeu","first_name":"Elena","full_name":"Bernabeu, Elena"},{"last_name":"Chybowska","first_name":"Aleksandra D.","full_name":"Chybowska, Aleksandra D."},{"first_name":"Jacob K.","last_name":"Kresovich","full_name":"Kresovich, Jacob K."},{"full_name":"Suderman, Matthew","first_name":"Matthew","last_name":"Suderman"},{"last_name":"Mccartney","first_name":"Daniel L.","full_name":"Mccartney, Daniel L."},{"first_name":"Robert F.","last_name":"Hillary","full_name":"Hillary, Robert F."},{"last_name":"Corley","first_name":"Janie","full_name":"Corley, Janie"},{"full_name":"Valdés-Hernández, Maria Del C.","last_name":"Valdés-Hernández","first_name":"Maria Del C."},{"full_name":"Maniega, Susana Muñoz","first_name":"Susana Muñoz","last_name":"Maniega"},{"first_name":"Mark E.","last_name":"Bastin","full_name":"Bastin, Mark E."},{"full_name":"Wardlaw, Joanna M.","last_name":"Wardlaw","first_name":"Joanna M."},{"last_name":"Xu","first_name":"Zongli","full_name":"Xu, Zongli"},{"full_name":"Sandler, Dale P.","last_name":"Sandler","first_name":"Dale P."},{"last_name":"Campbell","first_name":"Archie","full_name":"Campbell, Archie"},{"full_name":"Harris, Sarah E.","last_name":"Harris","first_name":"Sarah E."},{"full_name":"Mcintosh, Andrew M.","first_name":"Andrew M.","last_name":"Mcintosh"},{"full_name":"Taylor, Jack A.","last_name":"Taylor","first_name":"Jack A."},{"full_name":"Yousefi, Paul","last_name":"Yousefi","first_name":"Paul"},{"full_name":"Cox, Simon R.","first_name":"Simon R.","last_name":"Cox"},{"full_name":"Evans, Kathryn L.","last_name":"Evans","first_name":"Kathryn L."},{"orcid":"0000-0001-8982-8813","full_name":"Robinson, Matthew Richard","id":"E5D42276-F5DA-11E9-8E24-6303E6697425","last_name":"Robinson","first_name":"Matthew Richard"},{"full_name":"Vallejos, Catalina A.","first_name":"Catalina A.","last_name":"Vallejos"},{"first_name":"Riccardo E.","last_name":"Marioni","full_name":"Marioni, Riccardo E."}]},{"date_published":"2025-09-12T00:00:00Z","PlanS_conform":"1","publisher":"Springer Nature","ddc":["580"],"isi":1,"external_id":{"pmid":["40940427"],"isi":["001570197600001"]},"date_updated":"2025-12-01T14:59:10Z","department":[{"_id":"MaRo"},{"_id":"DaZi"}],"abstract":[{"lang":"eng","text":"Genetic variation is generally regarded as a prerequisite for evolution. In principle, epigenetic information inherited independently of DNA sequence can also enable evolution, but whether this occurs in natural populations is unknown. Here we show that single-nucleotide and epigenetic gene body DNA methylation (gbM) polymorphisms explain comparable amounts of expression variance in <jats:italic>Arabidopsis thaliana</jats:italic> populations. We genetically demonstrate that gbM regulates transcription, and we identify and genetically validate many associations between gbM polymorphism and the variation of complex traits: fitness under heat and drought, flowering time and accumulation of diverse minerals. Epigenome-wide association studies pinpoint trait-relevant genes with greater precision than genetic association analyses, probably due to reduced linkage disequilibrium between gbM variants. Finally, we identify numerous associations between gbM epialleles and diverse environmental conditions in native habitats, suggesting that gbM facilitates adaptation. Overall, our results indicate that epigenetic methylation variation fundamentally shapes phenotypic diversity in a natural population."}],"corr_author":"1","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","author":[{"full_name":"Shahzad, Zaigham","last_name":"Shahzad","first_name":"Zaigham"},{"full_name":"Hollwey, Elizabeth","id":"b8c4f54b-e484-11eb-8fdc-a54df64ef6dd","first_name":"Elizabeth","last_name":"Hollwey"},{"first_name":"Jonathan D.","last_name":"Moore","full_name":"Moore, Jonathan D."},{"last_name":"Choi","first_name":"Jaemyung","full_name":"Choi, Jaemyung"},{"first_name":"Gaëlle","last_name":"Cassin-Ross","full_name":"Cassin-Ross, Gaëlle"},{"last_name":"Rouached","first_name":"Hatem","full_name":"Rouached, Hatem"},{"full_name":"Robinson, Matthew Richard","orcid":"0000-0001-8982-8813","first_name":"Matthew Richard","last_name":"Robinson","id":"E5D42276-F5DA-11E9-8E24-6303E6697425"},{"last_name":"Zilberman","first_name":"Daniel","id":"6973db13-dd5f-11ea-814e-b3e5455e9ed1","full_name":"Zilberman, Daniel","orcid":"0000-0002-0123-8649"}],"language":[{"iso":"eng"}],"oa_version":"Published Version","citation":{"apa":"Shahzad, Z., Hollwey, E., Moore, J. D., Choi, J., Cassin-Ross, G., Rouached, H., … Zilberman, D. (2025). Gene body methylation regulates gene expression and mediates phenotypic diversity in natural Arabidopsis populations. <i>Nature Plants</i>. Springer Nature. <a href=\"https://doi.org/10.1038/s41477-025-02108-4\">https://doi.org/10.1038/s41477-025-02108-4</a>","short":"Z. Shahzad, E. Hollwey, J.D. Moore, J. Choi, G. Cassin-Ross, H. Rouached, M.R. Robinson, D. Zilberman, Nature Plants 11 (2025) 2084–2099.","ama":"Shahzad Z, Hollwey E, Moore JD, et al. Gene body methylation regulates gene expression and mediates phenotypic diversity in natural Arabidopsis populations. <i>Nature Plants</i>. 2025;11:2084-2099. doi:<a href=\"https://doi.org/10.1038/s41477-025-02108-4\">10.1038/s41477-025-02108-4</a>","chicago":"Shahzad, Zaigham, Elizabeth Hollwey, Jonathan D. Moore, Jaemyung Choi, Gaëlle Cassin-Ross, Hatem Rouached, Matthew Richard Robinson, and Daniel Zilberman. “Gene Body Methylation Regulates Gene Expression and Mediates Phenotypic Diversity in Natural Arabidopsis Populations.” <i>Nature Plants</i>. Springer Nature, 2025. <a href=\"https://doi.org/10.1038/s41477-025-02108-4\">https://doi.org/10.1038/s41477-025-02108-4</a>.","ieee":"Z. Shahzad <i>et al.</i>, “Gene body methylation regulates gene expression and mediates phenotypic diversity in natural Arabidopsis populations,” <i>Nature Plants</i>, vol. 11. Springer Nature, pp. 2084–2099, 2025.","mla":"Shahzad, Zaigham, et al. “Gene Body Methylation Regulates Gene Expression and Mediates Phenotypic Diversity in Natural Arabidopsis Populations.” <i>Nature Plants</i>, vol. 11, Springer Nature, 2025, pp. 2084–99, doi:<a href=\"https://doi.org/10.1038/s41477-025-02108-4\">10.1038/s41477-025-02108-4</a>.","ista":"Shahzad Z, Hollwey E, Moore JD, Choi J, Cassin-Ross G, Rouached H, Robinson MR, Zilberman D. 2025. Gene body methylation regulates gene expression and mediates phenotypic diversity in natural Arabidopsis populations. Nature Plants. 11, 2084–2099."},"quality_controlled":"1","publication_status":"published","year":"2025","file_date_updated":"2025-10-23T11:13:58Z","status":"public","type":"journal_article","_id":"20479","publication_identifier":{"issn":["2055-0278"]},"doi":"10.1038/s41477-025-02108-4","has_accepted_license":"1","article_type":"original","oa":1,"OA_type":"hybrid","scopus_import":"1","date_created":"2025-10-16T13:11:21Z","acknowledgement":"We thank P. Baduel and V. Colot for sharing SV data, A. Muyle for gbM conservation data and X. Feng, C. Dean, E. Coen and Zilberman lab members for constructive comments on the paper. This work was supported by a European Research Council grant (725746) to D.Z., LUMS Startup grant (STG-188) to Z.S. and US National Science Foundation grant (MCB-2334561) to H.R. This study would not have been possible without Arabidopsis 1001 genome, methylome and transcriptome resources. Open access funding provided by Institute of Science and Technology (IST Austria).","OA_place":"publisher","ec_funded":1,"pmid":1,"tmp":{"short":"CC BY (4.0)","legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","image":"/images/cc_by.png","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)"},"day":"12","publication":"Nature Plants","intvolume":"        11","project":[{"grant_number":"725746","name":"Quantitative analysis of DNA methylation maintenance with chromatin","call_identifier":"H2020","_id":"62935a00-2b32-11ec-9570-eff30fa39068"}],"title":"Gene body methylation regulates gene expression and mediates phenotypic diversity in natural Arabidopsis populations","page":"2084-2099","article_processing_charge":"Yes (via OA deal)","month":"09","volume":11,"file":[{"checksum":"6a3f6cffdc934b8a2015c3c247f5a92a","date_created":"2025-10-23T11:13:58Z","file_id":"20524","creator":"dernst","relation":"main_file","success":1,"access_level":"open_access","file_name":"2025_NaturePlants_Shahzad.pdf","date_updated":"2025-10-23T11:13:58Z","content_type":"application/pdf","file_size":7746662}]},{"department":[{"_id":"MaRo"}],"date_updated":"2025-12-01T12:58:17Z","abstract":[{"lang":"eng","text":"Global fibre production has expanded rapidly, with polyester and cotton dominating, significantly contributing to textile waste and increasing demand for sustainable solutions. This study presents innovative method to recycle polyester/cotton (PET/CO) blends using hydrophobic deep eutectic solvents (DESs), eliminating the need for toxic chemicals while achieving high dissolution yields. PET was completely dissolved within 5 min, substantially outperforming state-of-the-art methods and facilitating the efficient and selective recovery of both components, PET (97%) and CO (100%). SEM imaging confirmed no morphological changes in cotton fibres after treatment. The thermal stability of the recovered materials was validated using DSC and TGA analyses, while ATR-FTIR spectroscopy indicated no chemical changes. Mechanical testing confirmed recovered cotton’s tenacity and elongation are within expected ranges despite showing a decrease of 28% in tenacity and 34% in elongation. Hence, the proposed process provides an efficient and sustainable recycling solution for PET/CO blends, retaining both polymers in a condition similar to virgin materials used in textile manufacturing with minimal processing time."}],"isi":1,"external_id":{"isi":["001594629200003"],"pmid":["41066876"]},"ddc":["572"],"publisher":"Elsevier","date_published":"2025-11-01T00:00:00Z","PlanS_conform":"1","citation":{"apa":"Depope, N., Depope, A., Archodoulaki, V. M., Ipsmiller, W., &#38; Bartl, A. (2025). Deep eutectic solvent as a solution for polyester/cotton textile recycling. <i>Waste Management</i>. Elsevier. <a href=\"https://doi.org/10.1016/j.wasman.2025.115177\">https://doi.org/10.1016/j.wasman.2025.115177</a>","ama":"Depope N, Depope A, Archodoulaki VM, Ipsmiller W, Bartl A. Deep eutectic solvent as a solution for polyester/cotton textile recycling. <i>Waste Management</i>. 2025;208. doi:<a href=\"https://doi.org/10.1016/j.wasman.2025.115177\">10.1016/j.wasman.2025.115177</a>","short":"N. Depope, A. Depope, V.M. Archodoulaki, W. Ipsmiller, A. Bartl, Waste Management 208 (2025).","mla":"Depope, Nika, et al. “Deep Eutectic Solvent as a Solution for Polyester/Cotton Textile Recycling.” <i>Waste Management</i>, vol. 208, 115177, Elsevier, 2025, doi:<a href=\"https://doi.org/10.1016/j.wasman.2025.115177\">10.1016/j.wasman.2025.115177</a>.","ista":"Depope N, Depope A, Archodoulaki VM, Ipsmiller W, Bartl A. 2025. Deep eutectic solvent as a solution for polyester/cotton textile recycling. Waste Management. 208, 115177.","chicago":"Depope, Nika, Al Depope, Vasiliki Maria Archodoulaki, Wolfgang Ipsmiller, and Andreas Bartl. “Deep Eutectic Solvent as a Solution for Polyester/Cotton Textile Recycling.” <i>Waste Management</i>. Elsevier, 2025. <a href=\"https://doi.org/10.1016/j.wasman.2025.115177\">https://doi.org/10.1016/j.wasman.2025.115177</a>.","ieee":"N. Depope, A. Depope, V. M. Archodoulaki, W. Ipsmiller, and A. Bartl, “Deep eutectic solvent as a solution for polyester/cotton textile recycling,” <i>Waste Management</i>, vol. 208. Elsevier, 2025."},"oa_version":"Published Version","language":[{"iso":"eng"}],"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","author":[{"last_name":"Depope","first_name":"Nika","full_name":"Depope, Nika"},{"full_name":"Depope, Al","last_name":"Depope","first_name":"Al","id":"0b77531d-dbcd-11ea-9d1d-a8eee0bf3830"},{"last_name":"Archodoulaki","first_name":"Vasiliki Maria","full_name":"Archodoulaki, Vasiliki Maria"},{"last_name":"Ipsmiller","first_name":"Wolfgang","full_name":"Ipsmiller, Wolfgang"},{"full_name":"Bartl, Andreas","last_name":"Bartl","first_name":"Andreas"}],"has_accepted_license":"1","oa":1,"article_type":"original","doi":"10.1016/j.wasman.2025.115177","publication_identifier":{"eissn":["1879-2456"],"issn":["0956-053X"]},"_id":"20491","article_number":"115177","quality_controlled":"1","status":"public","type":"journal_article","file_date_updated":"2025-10-20T10:57:36Z","publication_status":"published","year":"2025","month":"11","article_processing_charge":"Yes (via OA deal)","file":[{"file_name":"2025_WasteMgmt_Depope.pdf","content_type":"application/pdf","date_updated":"2025-10-20T10:57:36Z","file_size":4511527,"date_created":"2025-10-20T10:57:36Z","checksum":"c232aae0ef7ed653813a835013f25bae","file_id":"20501","creator":"dernst","relation":"main_file","success":1,"access_level":"open_access"}],"volume":208,"intvolume":"       208","title":"Deep eutectic solvent as a solution for polyester/cotton textile recycling","publication":"Waste Management","OA_place":"publisher","acknowledgement":"This study was conducted at the Josef Ressel Centre for Recovery Strategies of Textiles which is funded by the Christian Doppler Research Society on behalf of the Austrian Federal Ministry of Labor and Economic Affairs and the National Foundation for Research, Technology. The authors acknowledge “Open Access Funding by TU Wien” for financial support through its Open Access Funding Program.\r\nSpecial thanks are extended to EREMA Group GmbH, SALESIANER MIETTEX GmbH and Starlinger & Co GmbH for their material support and valuable input throughout the development of this study.","date_created":"2025-10-19T22:01:31Z","scopus_import":"1","OA_type":"hybrid","day":"01","tmp":{"short":"CC BY (4.0)","legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","image":"/images/cc_by.png","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)"},"pmid":1},{"year":"2025","publication_status":"published","file_date_updated":"2025-12-15T13:18:07Z","type":"journal_article","status":"public","quality_controlled":"1","article_type":"original","oa":1,"has_accepted_license":"1","article_number":"417","_id":"20816","doi":"10.1186/s13059-025-03892-0","publication_identifier":{"eissn":["1474-760X"],"issn":["1474-7596"]},"publication":"Genome Biology","tmp":{"short":"CC BY (4.0)","legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","image":"/images/cc_by.png","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)"},"pmid":1,"day":"08","OA_type":"gold","DOAJ_listed":"1","scopus_import":"1","OA_place":"publisher","date_created":"2025-12-14T23:02:04Z","acknowledgement":"Generation Scotland received core support from the Chief Scientist Office of the Scottish Government Health Directorates [CZD/16/6] and the Scottish Funding Council [HR03006] and is currently supported by the Wellcome Trust [216767/Z/19/Z]. Genotyping of the Generation Scotland samples was carried out by the Genetics Core Laboratory at the Edinburgh Clinical Research Facility, University of Edinburgh, Scotland and was funded by the Medical Research Council UK and the Wellcome Trust (Wellcome Trust Strategic Award “STratifying Resilience and Depression Longitudinally” (STRADL) Reference 104036/Z/14/Z). The DNA methylation profiling and analysis was supported by Wellcome Investigator Award 220857/Z/20/Z and Grant 104036/Z/14/Z (PI: Prof AM McIntosh) and through funding from NARSAD (Ref: 27404; awardee: Dr DM Howard) and the Royal College of Physicians of Edinburgh (Sim Fellowship; Awardee: Prof HC Whalley).\r\nJAR is a University of Edinburgh Clinical Academic Track PhD student, supported by the Wellcome Trust (319878/Z/24/Z). ADC was supported by a Medical Research Council PhD Studentship in Precision Medicine with funding from the Medical Research Council Doctoral Training Program and the University of Edinburgh College of Medicine and Veterinary Medicine. HMS is a student on the University of Edinburgh Translational Neuroscience PhD programme funded by the Wellcome Trust (218493/Z/19/Z). CH was funded by MRC Human Genetics Unit program (QTL in Health and Disease) (grant U.MC_UU_00007/10). S.R.C. is supported by a Sir Henry Dale Fellowship jointly funded by the Wellcome Trust and the Royal Society (221890/Z/20/Z). JM and REM were supported by Alzheimer’s Society project grant AS-PG-19b-010.","volume":26,"file":[{"checksum":"7c92919af1b5820d01e91e08906a411f","date_created":"2025-12-15T13:18:07Z","relation":"main_file","creator":"dernst","file_id":"20825","success":1,"access_level":"open_access","file_name":"2025_GenomeBiology_Robertson.pdf","content_type":"application/pdf","date_updated":"2025-12-15T13:18:07Z","file_size":2206991}],"article_processing_charge":"Yes","month":"12","title":"Methylome-wide association studies and epigenetic biomarker development for 133 mass spectrometry-assessed circulating proteins in 14,671 Generation Scotland participants","intvolume":"        26","publisher":"Springer Nature","date_published":"2025-12-08T00:00:00Z","abstract":[{"lang":"eng","text":"Background: DNA methylation (DNAm) can regulate gene expression, and its genome-wide patterns (epigenetic scores or EpiScores) can act as biomarkers for complex traits. The relative stability of methylation profiles may enable better assessment of chronic exposures compared to single time-point protein measures. We present the first large-scale epigenetic study of the highly-abundant serum proteome measured via ultra-high throughput mass spectrometry in 14,671 samples from the Generation Scotland cohort. We further demonstrate the first large-scale comparison of protein EpiScores and their respective proteins as predictors of incident cardiovascular disease.\r\n\r\nResults: Marginal epigenome-wide association models, adjusting for age, sex, measurement batch, estimated white cell proportions, BMI, smoking and methylation principal components, reveal 15,855 significant CpG – protein associations across 125 of 133 proteins PBonferroni < 2.71 × 10-10. Bayesian epigenome-wide association studies of the same 133 proteins reveal 697 CpG-Protein associations (posterior inclusion probability > 0.95). 112 protein EpiScores correlate significantly with their respective protein in a holdout test-set. Of these, sixteen associate significantly with incident all-cause cardiovascular disease (Nevents=191) compared to one measured protein.\r\n\r\nConclusions: We highlight a complex interplay between the blood-based methylome and proteome. Importantly, we show that protein EpiScores correlate with measured proteins and demonstrate that the, as-yet understudied, high-abundance proteome may yield clinically relevant biomarkers. The protein EpiScores demonstrate more significant associations with cardiovascular disease than directly measured proteins, suggesting their potential as clinical biomarkers for monitoring or predicting disease risk. We suggest that biomarker development could be enhanced by the consideration of protein EpiScores alongside measured proteins."}],"date_updated":"2025-12-15T13:19:41Z","department":[{"_id":"MaRo"}],"ddc":["570"],"external_id":{"pmid":["41361833"]},"author":[{"last_name":"Robertson","first_name":"Josephine A.","full_name":"Robertson, Josephine A."},{"last_name":"Bajzik","first_name":"Jakub","id":"b995e25b-8c4b-11ed-a6d8-f71b7bcd6122","full_name":"Bajzik, Jakub"},{"first_name":"Spyros","last_name":"Vernardis","full_name":"Vernardis, Spyros"},{"full_name":"Chybowska, Aleksandra D.","first_name":"Aleksandra D.","last_name":"Chybowska"},{"first_name":"Daniel L.","last_name":"Mccartney","full_name":"Mccartney, Daniel L."},{"full_name":"Grauslys, Arturas","first_name":"Arturas","last_name":"Grauslys"},{"first_name":"Jure","last_name":"Mur","full_name":"Mur, Jure"},{"last_name":"Smith","first_name":"Hannah M.","full_name":"Smith, Hannah M."},{"full_name":"Campbell, Archie","last_name":"Campbell","first_name":"Archie"},{"full_name":"Drake, Camilla","last_name":"Drake","first_name":"Camilla"},{"full_name":"Grant, Hannah","last_name":"Grant","first_name":"Hannah"},{"first_name":"Jamie","last_name":"Pearce","full_name":"Pearce, Jamie"},{"last_name":"Russ","first_name":"Tom C.","full_name":"Russ, Tom C."},{"full_name":"Adkin, Poppy","last_name":"Adkin","first_name":"Poppy"},{"full_name":"White, Matthew","last_name":"White","first_name":"Matthew"},{"first_name":"Charles","last_name":"Brigden","full_name":"Brigden, Charles"},{"last_name":"Messner","first_name":"Christoph B.","full_name":"Messner, Christoph B."},{"full_name":"Porteous, David J.","first_name":"David J.","last_name":"Porteous"},{"first_name":"Caroline","last_name":"Hayward","full_name":"Hayward, Caroline"},{"last_name":"Cox","first_name":"Simon R.","full_name":"Cox, Simon R."},{"full_name":"Zelezniak, Aleksej","last_name":"Zelezniak","first_name":"Aleksej"},{"first_name":"Markus","last_name":"Ralser","full_name":"Ralser, Markus"},{"full_name":"Robinson, Matthew Richard","orcid":"0000-0001-8982-8813","first_name":"Matthew Richard","last_name":"Robinson","id":"E5D42276-F5DA-11E9-8E24-6303E6697425"},{"last_name":"Marioni","first_name":"Riccardo E.","full_name":"Marioni, Riccardo E."}],"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","citation":{"ieee":"J. A. Robertson <i>et al.</i>, “Methylome-wide association studies and epigenetic biomarker development for 133 mass spectrometry-assessed circulating proteins in 14,671 Generation Scotland participants,” <i>Genome Biology</i>, vol. 26. Springer Nature, 2025.","chicago":"Robertson, Josephine A., Jakub Bajzik, Spyros Vernardis, Aleksandra D. Chybowska, Daniel L. Mccartney, Arturas Grauslys, Jure Mur, et al. “Methylome-Wide Association Studies and Epigenetic Biomarker Development for 133 Mass Spectrometry-Assessed Circulating Proteins in 14,671 Generation Scotland Participants.” <i>Genome Biology</i>. Springer Nature, 2025. <a href=\"https://doi.org/10.1186/s13059-025-03892-0\">https://doi.org/10.1186/s13059-025-03892-0</a>.","ista":"Robertson JA, Bajzik J, Vernardis S, Chybowska AD, Mccartney DL, Grauslys A, Mur J, Smith HM, Campbell A, Drake C, Grant H, Pearce J, Russ TC, Adkin P, White M, Brigden C, Messner CB, Porteous DJ, Hayward C, Cox SR, Zelezniak A, Ralser M, Robinson MR, Marioni RE. 2025. Methylome-wide association studies and epigenetic biomarker development for 133 mass spectrometry-assessed circulating proteins in 14,671 Generation Scotland participants. Genome Biology. 26, 417.","mla":"Robertson, Josephine A., et al. “Methylome-Wide Association Studies and Epigenetic Biomarker Development for 133 Mass Spectrometry-Assessed Circulating Proteins in 14,671 Generation Scotland Participants.” <i>Genome Biology</i>, vol. 26, 417, Springer Nature, 2025, doi:<a href=\"https://doi.org/10.1186/s13059-025-03892-0\">10.1186/s13059-025-03892-0</a>.","short":"J.A. Robertson, J. Bajzik, S. Vernardis, A.D. Chybowska, D.L. Mccartney, A. Grauslys, J. Mur, H.M. Smith, A. Campbell, C. Drake, H. Grant, J. Pearce, T.C. Russ, P. Adkin, M. White, C. Brigden, C.B. Messner, D.J. Porteous, C. Hayward, S.R. Cox, A. Zelezniak, M. Ralser, M.R. Robinson, R.E. Marioni, Genome Biology 26 (2025).","ama":"Robertson JA, Bajzik J, Vernardis S, et al. Methylome-wide association studies and epigenetic biomarker development for 133 mass spectrometry-assessed circulating proteins in 14,671 Generation Scotland participants. <i>Genome Biology</i>. 2025;26. doi:<a href=\"https://doi.org/10.1186/s13059-025-03892-0\">10.1186/s13059-025-03892-0</a>","apa":"Robertson, J. A., Bajzik, J., Vernardis, S., Chybowska, A. D., Mccartney, D. L., Grauslys, A., … Marioni, R. E. (2025). Methylome-wide association studies and epigenetic biomarker development for 133 mass spectrometry-assessed circulating proteins in 14,671 Generation Scotland participants. <i>Genome Biology</i>. Springer Nature. <a href=\"https://doi.org/10.1186/s13059-025-03892-0\">https://doi.org/10.1186/s13059-025-03892-0</a>"},"language":[{"iso":"eng"}],"oa_version":"Published Version"},{"oa":1,"article_type":"original","has_accepted_license":"1","publication_identifier":{"issn":["0071-3260"],"eissn":["1934-2845"]},"doi":"10.1007/s11692-023-09624-1","_id":"14932","status":"public","type":"journal_article","file_date_updated":"2024-07-22T11:53:43Z","publication_status":"published","year":"2024","quality_controlled":"1","file":[{"relation":"main_file","creator":"dernst","file_id":"17311","checksum":"416ab4dac751c443b9de51fb58278ff2","date_created":"2024-07-22T11:53:43Z","access_level":"open_access","success":1,"file_name":"2024_EvolutionaryBio_Tsuboi.pdf","file_size":1705974,"date_updated":"2024-07-22T11:53:43Z","content_type":"application/pdf"}],"volume":51,"month":"03","article_processing_charge":"Yes (via OA deal)","page":"149-165","title":"Antler allometry, the Irish elk and Gould revisited","intvolume":"        51","publication":"Evolutionary Biology","day":"01","tmp":{"short":"CC BY (4.0)","legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","image":"/images/cc_by.png","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)"},"date_created":"2024-02-04T23:00:53Z","acknowledgement":"Open access funding provided by University of Oslo (incl Oslo University Hospital).\r\nWe thank Adrian Lister, Louis Tomsett, Roberto Portela Miguez and Roula Pappa (NHMUK), Brian O'Toole and Eileen Westwig (AMNH), Daniela Kalthoff (NHRM), Alexander Bibl and Zachos Frank (NHMW), Darrin Lunde and John Ososky (NMNH), Matthew Parkes and Nigel Monaghan (NMI), Elizabetta Cioppi and Luca Bellucci (IGF), and Yoshihiro Tanaka and Hiroyuki Taruno (OMNH), who helped us in obtaining the museum data, and a special thanks to Jørgen Sikkeland (NTNU NHM) for assistance in obtaining the ontogenetic data for the red deer. We thank Olja Toljagic and Kjetil L. Voje for discussions, Ayumu Tsuboi for assistance with data collection, and Jean-Michel Gaillard and the anonymous reviewers for comments on the manuscript. We thank the Centre of Advanced Study (CAS) at the Norwegian Academy of Sciences and Letters for hosting us during the academic year of 2019/2020 when much of the analysis and writing were done. MT was funded by JSPS Research Fellowship for Young Scientists (201603238).","scopus_import":"1","abstract":[{"text":"The huge antlers of the extinct Irish elk have invited evolutionary speculation since Darwin. In the 1970s, Stephen Jay Gould presented the first extensive data on antler size in the Irish elk and combined these with comparative data from other deer to test the hypothesis that the gigantic antlers were the outcome of a positive allometry that constrained large-bodied deer to have proportionally even larger antlers. He concluded that the Irish elk had antlers as predicted for its size and interpreted this within his emerging framework of developmental constraints as an explanatory factor in evolution. Here we reanalyze antler allometry based on new morphometric data for 57 taxa of the family Cervidae. We also present a new phylogeny for the Cervidae, which we use for comparative analyses. In contrast to Gould, we find that the antlers of Irish elk were larger than predicted from the allometry within the true deer, Cervini, as analyzed by Gould, but follow the allometry across Cervidae as a whole. After dissecting the discrepancy, we reject the allometric-constraint hypothesis because, contrary to Gould, we find no similarity between static and evolutionary allometries, and because we document extensive non-allometric evolution of antler size across the Cervidae.","lang":"eng"}],"department":[{"_id":"MaRo"}],"date_updated":"2025-09-04T11:54:55Z","isi":1,"external_id":{"isi":["001151285200001"]},"ddc":["570"],"publisher":"Springer Nature","date_published":"2024-03-01T00:00:00Z","citation":{"ieee":"M. Tsuboi <i>et al.</i>, “Antler allometry, the Irish elk and Gould revisited,” <i>Evolutionary Biology</i>, vol. 51. Springer Nature, pp. 149–165, 2024.","chicago":"Tsuboi, Masahito, Bjørn Tore Kopperud, Michael Matschiner, Mark Grabowski, Chrsitine Syrowatka, Christophe Pélabon, and Thomas F. Hansen. “Antler Allometry, the Irish Elk and Gould Revisited.” <i>Evolutionary Biology</i>. Springer Nature, 2024. <a href=\"https://doi.org/10.1007/s11692-023-09624-1\">https://doi.org/10.1007/s11692-023-09624-1</a>.","mla":"Tsuboi, Masahito, et al. “Antler Allometry, the Irish Elk and Gould Revisited.” <i>Evolutionary Biology</i>, vol. 51, Springer Nature, 2024, pp. 149–65, doi:<a href=\"https://doi.org/10.1007/s11692-023-09624-1\">10.1007/s11692-023-09624-1</a>.","ista":"Tsuboi M, Kopperud BT, Matschiner M, Grabowski M, Syrowatka C, Pélabon C, Hansen TF. 2024. Antler allometry, the Irish elk and Gould revisited. Evolutionary Biology. 51, 149–165.","ama":"Tsuboi M, Kopperud BT, Matschiner M, et al. Antler allometry, the Irish elk and Gould revisited. <i>Evolutionary Biology</i>. 2024;51:149-165. doi:<a href=\"https://doi.org/10.1007/s11692-023-09624-1\">10.1007/s11692-023-09624-1</a>","short":"M. Tsuboi, B.T. Kopperud, M. Matschiner, M. Grabowski, C. Syrowatka, C. Pélabon, T.F. Hansen, Evolutionary Biology 51 (2024) 149–165.","apa":"Tsuboi, M., Kopperud, B. T., Matschiner, M., Grabowski, M., Syrowatka, C., Pélabon, C., &#38; Hansen, T. F. (2024). Antler allometry, the Irish elk and Gould revisited. <i>Evolutionary Biology</i>. Springer Nature. <a href=\"https://doi.org/10.1007/s11692-023-09624-1\">https://doi.org/10.1007/s11692-023-09624-1</a>"},"oa_version":"Published Version","language":[{"iso":"eng"}],"author":[{"full_name":"Tsuboi, Masahito","last_name":"Tsuboi","first_name":"Masahito"},{"full_name":"Kopperud, Bjørn Tore","last_name":"Kopperud","first_name":"Bjørn Tore"},{"full_name":"Matschiner, Michael","first_name":"Michael","last_name":"Matschiner"},{"last_name":"Grabowski","first_name":"Mark","full_name":"Grabowski, Mark"},{"last_name":"Syrowatka","first_name":"Chrsitine","id":"205ffb76-7fe7-11eb-aa17-958bd11b99ad","full_name":"Syrowatka, Chrsitine"},{"full_name":"Pélabon, Christophe","last_name":"Pélabon","first_name":"Christophe"},{"last_name":"Hansen","first_name":"Thomas F.","full_name":"Hansen, Thomas F."}],"user_id":"317138e5-6ab7-11ef-aa6d-ffef3953e345"},{"quality_controlled":"1","status":"public","type":"conference","publication_status":"published","year":"2024","publication_identifier":{"isbn":["9798350344851"],"issn":["1520-6149"]},"doi":"10.1109/ICASSP48485.2024.10447198","main_file_link":[{"url":"https://openreview.net/forum?id=aQYCDxfZV0","open_access":"1"}],"_id":"17147","oa":1,"acknowledgement":"This work was supported by a Lopez-Loreta Prize to MM, an SNSF Eccellenza Grant to MRR (PCEGP3-181181), and core funding from ISTA. The authors thank Philip Schniter, Matthew Stephens and Pragya Sur for valuable suggestions on an early version of the work. The authors acknowledge the participants and investigators of the UK Biobank study. High-performance\r\ncomputing was supported by the Scientific Service Units (SSU) of IST Austria through resources provided by Scientific Computing (SciComp).","date_created":"2024-06-16T22:01:07Z","OA_place":"repository","scopus_import":"1","OA_type":"green","day":"19","publication":"2024 IEEE International Conference on Acoustics, Speech, and Signal Processing","project":[{"name":"Prix Lopez-Loretta 2019 - Marco Mondelli","_id":"059876FA-7A3F-11EA-A408-12923DDC885E"},{"_id":"9B8D11D6-BA93-11EA-9121-9846C619BF3A","grant_number":"PCEGP3_181181","name":"Improving estimation and prediction of common complex disease risk"}],"title":"Inference of genetic effects via approximate message passing","page":"13151-13155","month":"04","article_processing_charge":"No","date_published":"2024-04-19T00:00:00Z","conference":{"name":"ICASSP: International Conference on Acoustics, Speech and Signal Processing","end_date":"2024-04-19","location":"Seoul, Korea","start_date":"2024-04-14"},"publisher":"IEEE","isi":1,"external_id":{"isi":["001396233806078"]},"department":[{"_id":"MaMo"},{"_id":"MaRo"}],"date_updated":"2026-07-13T14:57:55Z","abstract":[{"lang":"eng","text":"Efficient utilization of large-scale biobank data is crucial for inferring the genetic basis of disease and predicting health outcomes from the DNA. Yet we lack efficient, accurate methods that scale to data where electronic health records are linked to whole genome sequence information. To address this issue, our paper develops a new algorithmic paradigm based on Approximate Message Passing (AMP), which is specifically tailored for genomic prediction and association testing. Our method yields comparable out-of-sample prediction accuracy to the state of the art on UK Biobank traits, whilst dramatically improving computational complexity, with a 8x-speed up in the run time. In addition, AMP theory provides a joint association testing framework, which outperforms the currently used REGENIE method, in roughly a third of the compute time. This first, truly large-scale application of the AMP framework lays the foundations for a far wider range of statistical analyses for hundreds of millions of variables measured on millions of people."}],"corr_author":"1","acknowledged_ssus":[{"_id":"ScienComp"}],"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","author":[{"id":"0b77531d-dbcd-11ea-9d1d-a8eee0bf3830","last_name":"Depope","first_name":"Al","full_name":"Depope, Al"},{"first_name":"Marco","last_name":"Mondelli","id":"27EB676C-8706-11E9-9510-7717E6697425","orcid":"0000-0002-3242-7020","full_name":"Mondelli, Marco"},{"first_name":"Matthew Richard","last_name":"Robinson","id":"E5D42276-F5DA-11E9-8E24-6303E6697425","orcid":"0000-0001-8982-8813","full_name":"Robinson, Matthew Richard"}],"oa_version":"Submitted Version","language":[{"iso":"eng"}],"citation":{"ama":"Depope A, Mondelli M, Robinson MR. Inference of genetic effects via approximate message passing. In: <i>2024 IEEE International Conference on Acoustics, Speech, and Signal Processing</i>. IEEE; 2024:13151-13155. doi:<a href=\"https://doi.org/10.1109/ICASSP48485.2024.10447198\">10.1109/ICASSP48485.2024.10447198</a>","short":"A. Depope, M. Mondelli, M.R. Robinson, in:, 2024 IEEE International Conference on Acoustics, Speech, and Signal Processing, IEEE, 2024, pp. 13151–13155.","apa":"Depope, A., Mondelli, M., &#38; Robinson, M. R. (2024). Inference of genetic effects via approximate message passing. In <i>2024 IEEE International Conference on Acoustics, Speech, and Signal Processing</i> (pp. 13151–13155). Seoul, Korea: IEEE. <a href=\"https://doi.org/10.1109/ICASSP48485.2024.10447198\">https://doi.org/10.1109/ICASSP48485.2024.10447198</a>","ieee":"A. Depope, M. Mondelli, and M. R. Robinson, “Inference of genetic effects via approximate message passing,” in <i>2024 IEEE International Conference on Acoustics, Speech, and Signal Processing</i>, Seoul, Korea, 2024, pp. 13151–13155.","chicago":"Depope, Al, Marco Mondelli, and Matthew Richard Robinson. “Inference of Genetic Effects via Approximate Message Passing.” In <i>2024 IEEE International Conference on Acoustics, Speech, and Signal Processing</i>, 13151–55. IEEE, 2024. <a href=\"https://doi.org/10.1109/ICASSP48485.2024.10447198\">https://doi.org/10.1109/ICASSP48485.2024.10447198</a>.","ista":"Depope A, Mondelli M, Robinson MR. 2024. Inference of genetic effects via approximate message passing. 2024 IEEE International Conference on Acoustics, Speech, and Signal Processing. ICASSP: International Conference on Acoustics, Speech and Signal Processing, 13151–13155.","mla":"Depope, Al, et al. “Inference of Genetic Effects via Approximate Message Passing.” <i>2024 IEEE International Conference on Acoustics, Speech, and Signal Processing</i>, IEEE, 2024, pp. 13151–55, doi:<a href=\"https://doi.org/10.1109/ICASSP48485.2024.10447198\">10.1109/ICASSP48485.2024.10447198</a>."}},{"oa_version":"Published Version","language":[{"iso":"eng"}],"citation":{"ista":"Villanueva Marijuan A. 2024. Bayesian linear regression for analyzing general omics data with time-to-event phenotypes. Institute of Science and Technology Austria.","mla":"Villanueva Marijuan, Ariadna. <i>Bayesian Linear Regression for Analyzing General Omics Data with Time-to-Event Phenotypes</i>. Institute of Science and Technology Austria, 2024, doi:<a href=\"https://doi.org/10.15479/at:ista:17368\">10.15479/at:ista:17368</a>.","ieee":"A. Villanueva Marijuan, “Bayesian linear regression for analyzing general omics data with time-to-event phenotypes,” Institute of Science and Technology Austria, 2024.","chicago":"Villanueva Marijuan, Ariadna. “Bayesian Linear Regression for Analyzing General Omics Data with Time-to-Event Phenotypes.” Institute of Science and Technology Austria, 2024. <a href=\"https://doi.org/10.15479/at:ista:17368\">https://doi.org/10.15479/at:ista:17368</a>.","short":"A. Villanueva Marijuan, Bayesian Linear Regression for Analyzing General Omics Data with Time-to-Event Phenotypes, Institute of Science and Technology Austria, 2024.","ama":"Villanueva Marijuan A. Bayesian linear regression for analyzing general omics data with time-to-event phenotypes. 2024. doi:<a href=\"https://doi.org/10.15479/at:ista:17368\">10.15479/at:ista:17368</a>","apa":"Villanueva Marijuan, A. (2024). <i>Bayesian linear regression for analyzing general omics data with time-to-event phenotypes</i>. Institute of Science and Technology Austria. <a href=\"https://doi.org/10.15479/at:ista:17368\">https://doi.org/10.15479/at:ista:17368</a>"},"corr_author":"1","author":[{"full_name":"Villanueva Marijuan, Ariadna","last_name":"Villanueva Marijuan","first_name":"Ariadna","id":"e0ae4864-133f-11ed-8f02-adaa8dd27540"}],"user_id":"ba8df636-2132-11f1-aed0-ed93e2281fdd","ddc":["610"],"supervisor":[{"orcid":"0000-0001-8982-8813","full_name":"Robinson, Matthew Richard","id":"E5D42276-F5DA-11E9-8E24-6303E6697425","first_name":"Matthew Richard","last_name":"Robinson"}],"abstract":[{"text":"Recent advancements in molecular diagnostic techniques have enabled the collection of\r\nmultiple types of omics data from patients, including genomics, epigenomics, proteomics,\r\nand transcriptomics. However, we lack effective methods for integrating all these different\r\ndata types and combining them with clinical outcomes to study the molecular mechanisms\r\nthat govern pathological phenotypes. We present multi-omics BayesW, a penalized Bayesian\r\nregression method that can handle general omics data for survival analysis of time-to-event\r\nphenotypes. Our method can: (1) accommodate incomplete data by allowing censored\r\nindividuals, (2) use continuous time-to-event data to test associations of markers with a\r\nphenotype and (3) estimate effects jointly while allowing for independent groups of biological\r\nmarkers. Extensive simulations using planted signals on real data demonstrate that our model\r\naccurately retrieves the true parameters of the model while controlling for false discoveries\r\nand maintaining the expected prediction accuracy. We address data correlations by estimating\r\nthe effects jointly, even between omic groups, while also estimating the individual variance\r\nexplained by each group. We apply our model to two datasets. Using 18,000 individuals from\r\nthe Generation Scotland study we model the association of time at onset of Type 2 Diabetes,\r\nStroke, Ischemic Disease, and Osteoarthritis from baseline study entry, with 831,724 CpG\r\nmethylation probes. We find that large proportions of variation in disease onset times can\r\nbe attributed to methylation as measured in whole blood at baseline in individuals without\r\ndisease symptoms. We then apply our model to The Cancer Genome Atlas (TCGA) pan-cancer\r\ndataset, in which we use 5 types of omics: copy number variation, epigenetics, somatic\r\nmutations, miRNA, and gene expression. For cancer survival age-at-onset we find that, when\r\nfitting the 5 groups together, almost all variation attributable to \"omics\" data is explained by\r\nDNA methylation. When considering progression times, both methylation and gene expression\r\nexplain a large part of the variance. We found 2 genes that are significantly associated (95%\r\nposterior inclusion probability) with cancer survival time, conditional on all other genome-wide\r\nomics data variation. Owing to the vast variability of mechanisms characterizing different\r\ncancers, there are likely few specific genes with a strong signal in a pan-cancer setting. Taken\r\ntogether, we showed the applicability of our multi-omics BayesW model to a wide-range of\r\nbiological questions in multi-omics data.\r\n","lang":"eng"}],"department":[{"_id":"GradSch"},{"_id":"MaRo"}],"date_updated":"2026-04-07T13:03:41Z","date_published":"2024-08-13T00:00:00Z","publisher":"Institute of Science and Technology Austria","degree_awarded":"MS","page":"60","title":"Bayesian linear regression for analyzing general omics data with time-to-event phenotypes","keyword":["Epigenetics","Multi-omics","Bayesian regression"],"file":[{"file_name":"Masters_thesis_AriadnaVillanueva.pdf","file_size":13052436,"date_updated":"2025-02-14T23:30:03Z","content_type":"application/pdf","relation":"main_file","file_id":"17433","creator":"avillanu","date_created":"2024-08-14T11:51:24Z","checksum":"0c2daa174609f0c00919dccc5701d375","access_level":"open_access","embargo":"2025-02-14"},{"file_name":"Masters thesis-AriadnaVillanueva.zip","date_updated":"2025-02-14T23:30:03Z","content_type":"application/zip","file_size":45642547,"embargo_to":"open_access","date_created":"2024-08-14T11:51:57Z","checksum":"e9ed4465dfa539ac4c3a8d4d0b6271a1","relation":"source_file","creator":"avillanu","file_id":"17434","access_level":"closed"}],"month":"08","article_processing_charge":"No","day":"13","tmp":{"name":"Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)","short":"CC BY-NC-SA (4.0)","legal_code_url":"https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode","image":"/images/cc_by_nc_sa.png"},"date_created":"2024-08-02T10:52:40Z","OA_place":"publisher","publication_identifier":{"issn":["2791-4585"]},"doi":"10.15479/at:ista:17368","_id":"17368","oa":1,"has_accepted_license":"1","status":"public","type":"dissertation","file_date_updated":"2025-02-14T23:30:03Z","publication_status":"published","year":"2024","alternative_title":["ISTA Master's Thesis"]},{"author":[{"first_name":"Nick N","last_name":"Machnik","id":"3591A0AA-F248-11E8-B48F-1D18A9856A87","full_name":"Machnik, Nick N","orcid":"0000-0001-6617-9742"}],"user_id":"8b945eb4-e2f2-11eb-945a-df72226e66a9","corr_author":"1","citation":{"apa":"Machnik, N. N. (2024). <i>Algorithms for causal learning and comparative analysis for genomic data</i>. Institute of Science and Technology Austria. <a href=\"https://doi.org/10.15479/at:ista:18642\">https://doi.org/10.15479/at:ista:18642</a>","short":"N.N. Machnik, Algorithms for Causal Learning and Comparative Analysis for Genomic Data, Institute of Science and Technology Austria, 2024.","ama":"Machnik NN. Algorithms for causal learning and comparative analysis for genomic data. 2024. doi:<a href=\"https://doi.org/10.15479/at:ista:18642\">10.15479/at:ista:18642</a>","mla":"Machnik, Nick N. <i>Algorithms for Causal Learning and Comparative Analysis for Genomic Data</i>. Institute of Science and Technology Austria, 2024, doi:<a href=\"https://doi.org/10.15479/at:ista:18642\">10.15479/at:ista:18642</a>.","ista":"Machnik NN. 2024. Algorithms for causal learning and comparative analysis for genomic data. Institute of Science and Technology Austria.","chicago":"Machnik, Nick N. “Algorithms for Causal Learning and Comparative Analysis for Genomic Data.” Institute of Science and Technology Austria, 2024. <a href=\"https://doi.org/10.15479/at:ista:18642\">https://doi.org/10.15479/at:ista:18642</a>.","ieee":"N. N. Machnik, “Algorithms for causal learning and comparative analysis for genomic data,” Institute of Science and Technology Austria, 2024."},"oa_version":"Published Version","language":[{"iso":"eng"}],"related_material":{"record":[{"relation":"part_of_dissertation","status":"public","id":"18648"},{"id":"8707","relation":"part_of_dissertation","status":"public"}]},"publisher":"Institute of Science and Technology Austria","degree_awarded":"PhD","date_published":"2024-12-11T00:00:00Z","abstract":[{"text":"This thesis consists of two pieces of work in the broader field of computational biology,\r\nboth of which are methods for the analysis of large scale biological data, implemented in\r\nefficient software.\r\nChapter 2 introduces a statistical software for causal discovery and inference from observed\r\ngenetic marker and phenotypic trait data. We explore in simulation how well the method\r\ncan fine-map genetic effects, find the correct causal structure among tens of traits and\r\nmillions of genetic markers, and infer the causal effect size for the discovered causal\r\nrelations. We then apply the method to 8 million markers and 17 traits from the UK\r\nBiobank and show that many relationships found with other methods are likely due to\r\nthe effects of hidden confounders.\r\nChapter 3 describes how this method can be applied to longitudinal data. I show how one\r\ncan incorporate the background knowledge present in the known order of measurements to\r\nimprove the accuracy of the causal discovery process, and explore the method’s ability to\r\nidentify age specific genetic effects, and how the error rates of this recovery are influenced\r\nby missing data due to different censoring mechanisms.\r\nChapter 4 introduces a statistical software for the comparison of chromatin contact maps\r\nbased on the structural similarity index. We explore the robustness of the method to\r\nnoise and size differences of the compared maps, show how it can measure evolutionary\r\nconservation of topological features by providing a similarity ranking of syntenic regions,\r\nand finally how it can detect alterations in 3D genome structure due to genetic mutations\r\nin samples of medical relevance.\r\n","lang":"eng"}],"supervisor":[{"orcid":"0000-0001-8982-8813","full_name":"Robinson, Matthew Richard","id":"E5D42276-F5DA-11E9-8E24-6303E6697425","first_name":"Matthew Richard","last_name":"Robinson"}],"department":[{"_id":"GradSch"},{"_id":"MaRo"}],"date_updated":"2026-07-29T13:02:44Z","doi_confirm":"1","ddc":["576"],"day":"11","date_created":"2024-12-10T13:49:15Z","OA_place":"publisher","acknowledgement":"I would like to thank the Swiss National Science Foundation for funding parts of this work\r\nthrough the Eccellenza Grant \"Improving estimation and prediction of common complex\r\ndisease risk\" with grant number PCEGP3_181181.","file":[{"relation":"main_file","creator":"nmachnik","file_id":"18649","date_created":"2024-12-11T11:59:54Z","checksum":"d45e4d170f9a70a1f69b44b99bd058e4","access_level":"open_access","embargo":"2025-06-12","file_name":"NickMachnikThesisFinal_pdfa_conv.pdf","file_size":12845009,"date_updated":"2025-06-12T22:30:02Z","content_type":"application/pdf"},{"creator":"nmachnik","file_id":"18650","relation":"source_file","embargo_to":"open_access","date_created":"2024-12-11T11:59:34Z","checksum":"f88c9acc62002395ec4dcbdb5eea8b82","access_level":"closed","file_name":"thesis.zip","file_size":14189810,"content_type":"application/zip","date_updated":"2025-06-12T22:30:02Z"}],"month":"12","article_processing_charge":"No","page":"138","title":"Algorithms for causal learning and comparative analysis for genomic data","project":[{"_id":"9B8D11D6-BA93-11EA-9121-9846C619BF3A","grant_number":"PCEGP3_181181","name":"Improving estimation and prediction of common complex disease risk"}],"alternative_title":["ISTA Thesis"],"status":"public","type":"dissertation","publication_status":"published","year":"2024","file_date_updated":"2025-06-12T22:30:02Z","oa":1,"has_accepted_license":"1","publication_identifier":{"issn":["2663-337X"]},"doi":"10.15479/at:ista:18642","_id":"18642"},{"publication":"bioRxiv","OA_type":"free access","acknowledgement":"We thank Zoltan Kutalik and members of the Robinson group \r\nat ISTA for their comments, which improved this manuscript. This work was funded \r\nby a research collaboration agreement between Boehringer Ingelheim and the research \r\ngroup of MRR at the Institute of Science and Technology Austria. Additional funding \r\nwas also provided by an SNSF Eccellenza Grant to MRR (PCEGP3-181181), and by \r\ncore funding from the Institute of Science and Technology Austria. We would like \r\nto acknowledge the participants and investigators of the UK Biobank study. High- \r\nperformance computing was supported by the Scientific Service Units (SSU) of IST \r\nAustria through resources provided by Scientific Computing (SciComp). ","date_created":"2024-12-11T10:42:59Z","OA_place":"repository","tmp":{"legal_code_url":"https://creativecommons.org/licenses/by-nc/4.0/legalcode","image":"/images/cc_by_nc.png","short":"CC BY-NC (4.0)","name":"Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0)"},"day":"10","month":"08","article_processing_charge":"No","project":[{"_id":"9B8D11D6-BA93-11EA-9121-9846C619BF3A","name":"Improving estimation and prediction of common complex disease risk","grant_number":"PCEGP3_181181"},{"grant_number":"590359","name":"Advanced statistical modelling to facilitate more accurate characterisation of disease phenotypes, improved genetic mapping, and effective therapeutic hypothesis generation","_id":"bd936e6f-d553-11ed-ba76-a82299f63e8c"}],"title":"Causal inference for multiple risk factors and diseases from genomics data","publication_status":"published","year":"2024","type":"preprint","status":"public","oa":1,"main_file_link":[{"open_access":"1","url":"https://doi.org/10.1101/2023.12.06.570392"}],"_id":"18648","doi":"10.1101/2023.12.06.570392","user_id":"8b945eb4-e2f2-11eb-945a-df72226e66a9","author":[{"orcid":"0000-0001-6617-9742","full_name":"Machnik, Nick N","first_name":"Nick N","last_name":"Machnik","id":"3591A0AA-F248-11E8-B48F-1D18A9856A87"},{"id":"b9f6d5ef-7774-11eb-a47f-df2c75c02ee7","last_name":"Mahmoudi","first_name":"Seyed Mahdi","full_name":"Mahmoudi, Seyed Mahdi"},{"full_name":"Borczyk, Malgorzata","first_name":"Malgorzata","last_name":"Borczyk"},{"id":"30d4014e-7753-11eb-b44b-db6d61112e73","first_name":"Ilse","last_name":"Krätschmer","full_name":"Krätschmer, Ilse","orcid":"0000-0002-5636-9259"},{"full_name":"Bauer, Markus J.","last_name":"Bauer","first_name":"Markus J."},{"orcid":"0000-0001-8982-8813","full_name":"Robinson, Matthew Richard","id":"E5D42276-F5DA-11E9-8E24-6303E6697425","first_name":"Matthew Richard","last_name":"Robinson"}],"corr_author":"1","acknowledged_ssus":[{"_id":"ScienComp"}],"citation":{"ieee":"N. N. Machnik, S. M. Mahmoudi, M. Borczyk, I. Krätschmer, M. J. Bauer, and M. R. Robinson, “Causal inference for multiple risk factors and diseases from genomics data,” <i>bioRxiv</i>. 2024.","chicago":"Machnik, Nick N, Seyed Mahdi Mahmoudi, Malgorzata Borczyk, Ilse Krätschmer, Markus J. Bauer, and Matthew Richard Robinson. “Causal Inference for Multiple Risk Factors and Diseases from Genomics Data.” <i>BioRxiv</i>, 2024. <a href=\"https://doi.org/10.1101/2023.12.06.570392\">https://doi.org/10.1101/2023.12.06.570392</a>.","ista":"Machnik NN, Mahmoudi SM, Borczyk M, Krätschmer I, Bauer MJ, Robinson MR. 2024. Causal inference for multiple risk factors and diseases from genomics data. bioRxiv, <a href=\"https://doi.org/10.1101/2023.12.06.570392\">10.1101/2023.12.06.570392</a>.","mla":"Machnik, Nick N., et al. “Causal Inference for Multiple Risk Factors and Diseases from Genomics Data.” <i>BioRxiv</i>, 2024, doi:<a href=\"https://doi.org/10.1101/2023.12.06.570392\">10.1101/2023.12.06.570392</a>.","short":"N.N. Machnik, S.M. Mahmoudi, M. Borczyk, I. Krätschmer, M.J. Bauer, M.R. Robinson, BioRxiv (2024).","ama":"Machnik NN, Mahmoudi SM, Borczyk M, Krätschmer I, Bauer MJ, Robinson MR. Causal inference for multiple risk factors and diseases from genomics data. <i>bioRxiv</i>. 2024. doi:<a href=\"https://doi.org/10.1101/2023.12.06.570392\">10.1101/2023.12.06.570392</a>","apa":"Machnik, N. N., Mahmoudi, S. M., Borczyk, M., Krätschmer, I., Bauer, M. J., &#38; Robinson, M. R. (2024). Causal inference for multiple risk factors and diseases from genomics data. <i>bioRxiv</i>. <a href=\"https://doi.org/10.1101/2023.12.06.570392\">https://doi.org/10.1101/2023.12.06.570392</a>"},"language":[{"iso":"eng"}],"oa_version":"Preprint","license":"https://creativecommons.org/licenses/by-nc/4.0/","related_material":{"record":[{"relation":"dissertation_contains","status":"public","id":"18642"}]},"date_published":"2024-08-10T00:00:00Z","date_updated":"2026-08-16T22:31:07Z","department":[{"_id":"MaRo"}],"abstract":[{"lang":"eng","text":"Statistical causal learning in genomics relies on the instrumental variable method of\r\nMendelian Randomization (MR). Currently, an overwhelming number of MR studies\r\npurport to show causal relationships among a wide range of risk factors and outcomes.\r\nHere, we show that selecting instrument variables from genome-wide association study\r\nestimates leads to high false discovery rates for many MR approaches, which can be\r\ngreatly reduced by employing a graphical inference approach which: (i) explicitly tests\r\ninstrumental variable assumptions; (ii) distinguishes direct from indirect factors in very\r\nhigh-dimensional data; (iii) discriminates pleiotropic from trait-specific markers, controlling for LD genome-wide; (iv) accommodates rare variants and binary outcomes in a\r\nprincipled way; and (v) identifies potential unobserved latent confounding. For 17 traits\r\nand 8.4M variants recorded for 458,747 individuals in the UK Biobank, we show that\r\nstandard MR analysis gives an abundance of findings that disappear under stringent\r\nassumption checks, with many relationships reflecting potential unmeasured confounding. This implies that mixtures of temporal precedence and potential for reverse-causality\r\nprohibit understanding the underlying nature of phenotypic and genetic correlations in\r\nbiobank data. We propose that well-curated longitudinal records are likely needed and\r\nthat our approach provides a first-step toward robust principled screening for potential\r\ncausal links.\r\n"}]},{"scopus_import":"1","date_created":"2023-03-12T23:01:02Z","acknowledgement":"We are grateful to all the families who took part, the general practitioners, and the Scottish School of Primary Care for their help in recruiting them and the whole GS team that includes interviewers, computer and laboratory technicians, clerical workers, research scientists, volunteers, managers, receptionists, healthcare assistants, and nurses.","pmid":1,"tmp":{"short":"CC BY (4.0)","legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","image":"/images/cc_by.png","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)"},"day":"28","publication":"Genome Medicine","intvolume":"        15","title":"Refining epigenetic prediction of chronological and biological age","article_processing_charge":"No","month":"02","volume":15,"file":[{"file_name":"2023_GenomeMed_Bernabeu.pdf","file_size":4275987,"date_updated":"2023-03-14T10:29:47Z","content_type":"application/pdf","creator":"cchlebak","file_id":"12722","relation":"main_file","checksum":"833b837910c4db42fb5f0f34125f77a7","date_created":"2023-03-14T10:29:47Z","access_level":"open_access","success":1}],"quality_controlled":"1","publication_status":"published","file_date_updated":"2023-03-14T10:29:47Z","year":"2023","status":"public","type":"journal_article","_id":"12719","doi":"10.1186/s13073-023-01161-y","publication_identifier":{"eissn":["1756-994X"]},"article_number":"12","has_accepted_license":"1","article_type":"original","oa":1,"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","author":[{"full_name":"Bernabeu, Elena","first_name":"Elena","last_name":"Bernabeu"},{"first_name":"Daniel L.","last_name":"Mccartney","full_name":"Mccartney, Daniel L."},{"first_name":"Danni A.","last_name":"Gadd","full_name":"Gadd, Danni A."},{"first_name":"Robert F.","last_name":"Hillary","full_name":"Hillary, Robert F."},{"last_name":"Lu","first_name":"Ake T.","full_name":"Lu, Ake T."},{"last_name":"Murphy","first_name":"Lee","full_name":"Murphy, Lee"},{"full_name":"Wrobel, Nicola","last_name":"Wrobel","first_name":"Nicola"},{"full_name":"Campbell, Archie","last_name":"Campbell","first_name":"Archie"},{"last_name":"Harris","first_name":"Sarah E.","full_name":"Harris, Sarah E."},{"first_name":"David","last_name":"Liewald","full_name":"Liewald, David"},{"last_name":"Hayward","first_name":"Caroline","full_name":"Hayward, Caroline"},{"full_name":"Sudlow, Cathie","first_name":"Cathie","last_name":"Sudlow"},{"full_name":"Cox, Simon R.","first_name":"Simon R.","last_name":"Cox"},{"last_name":"Evans","first_name":"Kathryn L.","full_name":"Evans, Kathryn L."},{"full_name":"Horvath, Steve","last_name":"Horvath","first_name":"Steve"},{"last_name":"Mcintosh","first_name":"Andrew M.","full_name":"Mcintosh, Andrew M."},{"id":"E5D42276-F5DA-11E9-8E24-6303E6697425","last_name":"Robinson","first_name":"Matthew Richard","orcid":"0000-0001-8982-8813","full_name":"Robinson, Matthew Richard"},{"first_name":"Catalina A.","last_name":"Vallejos","full_name":"Vallejos, Catalina A."},{"full_name":"Marioni, Riccardo E.","first_name":"Riccardo E.","last_name":"Marioni"}],"language":[{"iso":"eng"}],"oa_version":"Published Version","citation":{"apa":"Bernabeu, E., Mccartney, D. L., Gadd, D. A., Hillary, R. F., Lu, A. T., Murphy, L., … Marioni, R. E. (2023). Refining epigenetic prediction of chronological and biological age. <i>Genome Medicine</i>. Springer Nature. <a href=\"https://doi.org/10.1186/s13073-023-01161-y\">https://doi.org/10.1186/s13073-023-01161-y</a>","ama":"Bernabeu E, Mccartney DL, Gadd DA, et al. Refining epigenetic prediction of chronological and biological age. <i>Genome Medicine</i>. 2023;15. doi:<a href=\"https://doi.org/10.1186/s13073-023-01161-y\">10.1186/s13073-023-01161-y</a>","short":"E. Bernabeu, D.L. Mccartney, D.A. Gadd, R.F. Hillary, A.T. Lu, L. Murphy, N. Wrobel, A. Campbell, S.E. Harris, D. Liewald, C. Hayward, C. Sudlow, S.R. Cox, K.L. Evans, S. Horvath, A.M. Mcintosh, M.R. Robinson, C.A. Vallejos, R.E. Marioni, Genome Medicine 15 (2023).","chicago":"Bernabeu, Elena, Daniel L. Mccartney, Danni A. Gadd, Robert F. Hillary, Ake T. Lu, Lee Murphy, Nicola Wrobel, et al. “Refining Epigenetic Prediction of Chronological and Biological Age.” <i>Genome Medicine</i>. Springer Nature, 2023. <a href=\"https://doi.org/10.1186/s13073-023-01161-y\">https://doi.org/10.1186/s13073-023-01161-y</a>.","ieee":"E. Bernabeu <i>et al.</i>, “Refining epigenetic prediction of chronological and biological age,” <i>Genome Medicine</i>, vol. 15. Springer Nature, 2023.","ista":"Bernabeu E, Mccartney DL, Gadd DA, Hillary RF, Lu AT, Murphy L, Wrobel N, Campbell A, Harris SE, Liewald D, Hayward C, Sudlow C, Cox SR, Evans KL, Horvath S, Mcintosh AM, Robinson MR, Vallejos CA, Marioni RE. 2023. Refining epigenetic prediction of chronological and biological age. Genome Medicine. 15, 12.","mla":"Bernabeu, Elena, et al. “Refining Epigenetic Prediction of Chronological and Biological Age.” <i>Genome Medicine</i>, vol. 15, 12, Springer Nature, 2023, doi:<a href=\"https://doi.org/10.1186/s13073-023-01161-y\">10.1186/s13073-023-01161-y</a>."},"date_published":"2023-02-28T00:00:00Z","publisher":"Springer Nature","ddc":["570"],"isi":1,"external_id":{"pmid":["36855161"],"isi":["000940286600001"]},"date_updated":"2025-04-23T08:49:38Z","department":[{"_id":"MaRo"}],"abstract":[{"lang":"eng","text":"Background\r\nEpigenetic clocks can track both chronological age (cAge) and biological age (bAge). The latter is typically defined by physiological biomarkers and risk of adverse health outcomes, including all-cause mortality. As cohort sample sizes increase, estimates of cAge and bAge become more precise. Here, we aim to develop accurate epigenetic predictors of cAge and bAge, whilst improving our understanding of their epigenomic architecture.\r\n\r\nMethods\r\nFirst, we perform large-scale (N = 18,413) epigenome-wide association studies (EWAS) of chronological age and all-cause mortality. Next, to create a cAge predictor, we use methylation data from 24,674 participants from the Generation Scotland study, the Lothian Birth Cohorts (LBC) of 1921 and 1936, and 8 other cohorts with publicly available data. In addition, we train a predictor of time to all-cause mortality as a proxy for bAge using the Generation Scotland cohort (1214 observed deaths). For this purpose, we use epigenetic surrogates (EpiScores) for 109 plasma proteins and the 8 component parts of GrimAge, one of the current best epigenetic predictors of survival. We test this bAge predictor in four external cohorts (LBC1921, LBC1936, the Framingham Heart Study and the Women’s Health Initiative study).\r\n\r\nResults\r\nThrough the inclusion of linear and non-linear age-CpG associations from the EWAS, feature pre-selection in advance of elastic net regression, and a leave-one-cohort-out (LOCO) cross-validation framework, we obtain cAge prediction with a median absolute error equal to 2.3 years. Our bAge predictor was found to slightly outperform GrimAge in terms of the strength of its association to survival (HRGrimAge = 1.47 [1.40, 1.54] with p = 1.08 × 10−52, and HRbAge = 1.52 [1.44, 1.59] with p = 2.20 × 10−60). Finally, we introduce MethylBrowsR, an online tool to visualise epigenome-wide CpG-age associations.\r\n\r\nConclusions\r\nThe integration of multiple large datasets, EpiScores, non-linear DNAm effects, and new approaches to feature selection has facilitated improvements to the blood-based epigenetic prediction of biological and chronological age."}]},{"publisher":"Public Library of Science","issue":"3","date_published":"2023-03-16T00:00:00Z","abstract":[{"lang":"eng","text":"AlphaFold changed the field of structural biology by achieving three-dimensional (3D) structure prediction from protein sequence at experimental quality. The astounding success even led to claims that the protein folding problem is “solved”. However, protein folding problem is more than just structure prediction from sequence. Presently, it is unknown if the AlphaFold-triggered revolution could help to solve other problems related to protein folding. Here we assay the ability of AlphaFold to predict the impact of single mutations on protein stability (ΔΔG) and function. To study the question we extracted the pLDDT and <pLDDT> metrics from AlphaFold predictions before and after single mutation in a protein and correlated the predicted change with the experimentally known ΔΔG values. Additionally, we correlated the same AlphaFold pLDDT metrics with the impact of a single mutation on structure using a large scale dataset of single mutations in GFP with the experimentally assayed levels of fluorescence. We found a very weak or no correlation between AlphaFold output metrics and change of protein stability or fluorescence. Our results imply that AlphaFold may not be immediately applied to other problems or applications in protein folding."}],"date_updated":"2025-04-23T08:50:30Z","department":[{"_id":"FyKo"},{"_id":"MaRo"}],"ddc":["570"],"external_id":{"isi":["000985134400106"],"pmid":["36928239"]},"isi":1,"author":[{"full_name":"Pak, Marina A.","last_name":"Pak","first_name":"Marina A."},{"last_name":"Markhieva","first_name":"Karina A.","full_name":"Markhieva, Karina A."},{"full_name":"Novikova, Mariia S.","last_name":"Novikova","first_name":"Mariia S."},{"last_name":"Petrov","first_name":"Dmitry S.","full_name":"Petrov, Dmitry S."},{"first_name":"Ilya S.","last_name":"Vorobyev","full_name":"Vorobyev, Ilya S."},{"full_name":"Maksimova, Ekaterina","last_name":"Maksimova","first_name":"Ekaterina","id":"2FBE0DE4-F248-11E8-B48F-1D18A9856A87"},{"full_name":"Kondrashov, Fyodor","orcid":"0000-0001-8243-4694","id":"44FDEF62-F248-11E8-B48F-1D18A9856A87","first_name":"Fyodor","last_name":"Kondrashov"},{"last_name":"Ivankov","first_name":"Dmitry N.","full_name":"Ivankov, Dmitry N."}],"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","citation":{"short":"M.A. Pak, K.A. Markhieva, M.S. Novikova, D.S. Petrov, I.S. Vorobyev, E. Maksimova, F. Kondrashov, D.N. Ivankov, PLoS ONE 18 (2023).","ama":"Pak MA, Markhieva KA, Novikova MS, et al. Using AlphaFold to predict the impact of single mutations on protein stability and function. <i>PLoS ONE</i>. 2023;18(3). doi:<a href=\"https://doi.org/10.1371/journal.pone.0282689\">10.1371/journal.pone.0282689</a>","apa":"Pak, M. A., Markhieva, K. A., Novikova, M. S., Petrov, D. S., Vorobyev, I. S., Maksimova, E., … Ivankov, D. N. (2023). Using AlphaFold to predict the impact of single mutations on protein stability and function. <i>PLoS ONE</i>. Public Library of Science. <a href=\"https://doi.org/10.1371/journal.pone.0282689\">https://doi.org/10.1371/journal.pone.0282689</a>","mla":"Pak, Marina A., et al. “Using AlphaFold to Predict the Impact of Single Mutations on Protein Stability and Function.” <i>PLoS ONE</i>, vol. 18, no. 3, e0282689, Public Library of Science, 2023, doi:<a href=\"https://doi.org/10.1371/journal.pone.0282689\">10.1371/journal.pone.0282689</a>.","ista":"Pak MA, Markhieva KA, Novikova MS, Petrov DS, Vorobyev IS, Maksimova E, Kondrashov F, Ivankov DN. 2023. Using AlphaFold to predict the impact of single mutations on protein stability and function. PLoS ONE. 18(3), e0282689.","ieee":"M. A. Pak <i>et al.</i>, “Using AlphaFold to predict the impact of single mutations on protein stability and function,” <i>PLoS ONE</i>, vol. 18, no. 3. Public Library of Science, 2023.","chicago":"Pak, Marina A., Karina A. Markhieva, Mariia S. Novikova, Dmitry S. Petrov, Ilya S. Vorobyev, Ekaterina Maksimova, Fyodor Kondrashov, and Dmitry N. Ivankov. “Using AlphaFold to Predict the Impact of Single Mutations on Protein Stability and Function.” <i>PLoS ONE</i>. Public Library of Science, 2023. <a href=\"https://doi.org/10.1371/journal.pone.0282689\">https://doi.org/10.1371/journal.pone.0282689</a>."},"language":[{"iso":"eng"}],"oa_version":"Published Version","year":"2023","publication_status":"published","file_date_updated":"2023-03-27T07:09:08Z","type":"journal_article","status":"public","quality_controlled":"1","article_type":"original","oa":1,"has_accepted_license":"1","article_number":"e0282689","_id":"12758","publication_identifier":{"eissn":["1932-6203"]},"doi":"10.1371/journal.pone.0282689","publication":"PLoS ONE","pmid":1,"tmp":{"short":"CC BY (4.0)","legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","image":"/images/cc_by.png","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)"},"day":"16","scopus_import":"1","date_created":"2023-03-26T22:01:07Z","acknowledgement":"The authors acknowledge the use of Zhores supercomputer [28] for obtaining the results presented in this paper.The authors thank Zimin Foundation and Petrovax for support of the presented study at the School of Molecular and Theoretical Biology 2021.","volume":18,"file":[{"date_created":"2023-03-27T07:09:08Z","checksum":"0281bdfccf8d76c4e08dd011c603f6b6","relation":"main_file","file_id":"12771","creator":"dernst","success":1,"access_level":"open_access","file_name":"2023_PLoSOne_Pak.pdf","content_type":"application/pdf","date_updated":"2023-03-27T07:09:08Z","file_size":856625}],"article_processing_charge":"No","month":"03","title":"Using AlphaFold to predict the impact of single mutations on protein stability and function","intvolume":"        18"},{"date_published":"2023-09-07T00:00:00Z","publisher":"Elsevier","issue":"9","isi":1,"external_id":{"isi":["001074842500001"],"pmid":["37543033"]},"ddc":["570"],"abstract":[{"lang":"eng","text":"There is currently little evidence that the genetic basis of human phenotype varies significantly across the lifespan. However, time-to-event phenotypes are understudied and can be thought of as reflecting an underlying hazard, which is unlikely to be constant through life when values take a broad range. Here, we find that 74% of 245 genome-wide significant genetic associations with age at natural menopause (ANM) in the UK Biobank show a form of age-specific effect. Nineteen of these replicated discoveries are identified only by our modeling framework, which determines the time dependency of DNA-variant age-at-onset associations without a significant multiple-testing burden. Across the range of early to late menopause, we find evidence for significantly different underlying biological pathways, changes in the signs of genetic correlations of ANM to health indicators and outcomes, and differences in inferred causal relationships. We find that DNA damage response processes only act to shape ovarian reserve and depletion for women of early ANM. Genetically mediated delays in ANM were associated with increased relative risk of breast cancer and leiomyoma at all ages and with high cholesterol and heart failure for late-ANM women. These findings suggest that a better understanding of the age dependency of genetic risk factor relationships among health indicators and outcomes is achievable through appropriate statistical modeling of large-scale biobank data."}],"department":[{"_id":"MaRo"}],"date_updated":"2025-09-09T12:51:20Z","corr_author":"1","author":[{"full_name":"Ojavee, Sven E.","last_name":"Ojavee","first_name":"Sven E."},{"full_name":"Darrous, Liza","first_name":"Liza","last_name":"Darrous"},{"first_name":"Marion","last_name":"Patxot","full_name":"Patxot, Marion"},{"full_name":"Läll, Kristi","last_name":"Läll","first_name":"Kristi"},{"first_name":"Krista","last_name":"Fischer","full_name":"Fischer, Krista"},{"first_name":"Reedik","last_name":"Mägi","full_name":"Mägi, Reedik"},{"last_name":"Kutalik","first_name":"Zoltan","full_name":"Kutalik, Zoltan"},{"orcid":"0000-0001-8982-8813","full_name":"Robinson, Matthew Richard","id":"E5D42276-F5DA-11E9-8E24-6303E6697425","first_name":"Matthew Richard","last_name":"Robinson"}],"user_id":"317138e5-6ab7-11ef-aa6d-ffef3953e345","oa_version":"Published Version","language":[{"iso":"eng"}],"citation":{"short":"S.E. Ojavee, L. Darrous, M. Patxot, K. Läll, K. Fischer, R. Mägi, Z. Kutalik, M.R. Robinson, American Journal of Human Genetics 110 (2023) 1549–1563.","ama":"Ojavee SE, Darrous L, Patxot M, et al. Genetic insights into the age-specific biological mechanisms governing human ovarian aging. <i>American Journal of Human Genetics</i>. 2023;110(9):1549-1563. doi:<a href=\"https://doi.org/10.1016/j.ajhg.2023.07.006\">10.1016/j.ajhg.2023.07.006</a>","apa":"Ojavee, S. E., Darrous, L., Patxot, M., Läll, K., Fischer, K., Mägi, R., … Robinson, M. R. (2023). Genetic insights into the age-specific biological mechanisms governing human ovarian aging. <i>American Journal of Human Genetics</i>. Elsevier. <a href=\"https://doi.org/10.1016/j.ajhg.2023.07.006\">https://doi.org/10.1016/j.ajhg.2023.07.006</a>","ieee":"S. E. Ojavee <i>et al.</i>, “Genetic insights into the age-specific biological mechanisms governing human ovarian aging,” <i>American Journal of Human Genetics</i>, vol. 110, no. 9. Elsevier, pp. 1549–1563, 2023.","chicago":"Ojavee, Sven E., Liza Darrous, Marion Patxot, Kristi Läll, Krista Fischer, Reedik Mägi, Zoltan Kutalik, and Matthew Richard Robinson. “Genetic Insights into the Age-Specific Biological Mechanisms Governing Human Ovarian Aging.” <i>American Journal of Human Genetics</i>. Elsevier, 2023. <a href=\"https://doi.org/10.1016/j.ajhg.2023.07.006\">https://doi.org/10.1016/j.ajhg.2023.07.006</a>.","mla":"Ojavee, Sven E., et al. “Genetic Insights into the Age-Specific Biological Mechanisms Governing Human Ovarian Aging.” <i>American Journal of Human Genetics</i>, vol. 110, no. 9, Elsevier, 2023, pp. 1549–63, doi:<a href=\"https://doi.org/10.1016/j.ajhg.2023.07.006\">10.1016/j.ajhg.2023.07.006</a>.","ista":"Ojavee SE, Darrous L, Patxot M, Läll K, Fischer K, Mägi R, Kutalik Z, Robinson MR. 2023. Genetic insights into the age-specific biological mechanisms governing human ovarian aging. American Journal of Human Genetics. 110(9), 1549–1563."},"type":"journal_article","status":"public","file_date_updated":"2024-01-30T13:20:35Z","year":"2023","publication_status":"published","quality_controlled":"1","publication_identifier":{"eissn":["1537-6605"],"issn":["0002-9297"]},"doi":"10.1016/j.ajhg.2023.07.006","_id":"14258","oa":1,"article_type":"original","has_accepted_license":"1","day":"07","pmid":1,"tmp":{"short":"CC BY (4.0)","legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","image":"/images/cc_by.png","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)"},"date_created":"2023-09-03T22:01:15Z","acknowledgement":"This project was funded by an SNSF Eccellenza grant to M.R.R. (PCEGP3-181181) and by core funding from the Institute of Science and Technology Austria. K.L. and R.M. were supported by the Estonian Research Council grant 1911. Estonian Biobank computations were performed in the High-Performance Computing Center, University of Tartu. We thank Triin Laisk for her valuable insights and comments that helped greatly. We would like to acknowledge the participants and investigators of UK Biobank and Estonian Biobank studies. This project uses UK Biobank data under project number 35520.","scopus_import":"1","publication":"American Journal of Human Genetics","page":"1549-1563","title":"Genetic insights into the age-specific biological mechanisms governing human ovarian aging","intvolume":"       110","file":[{"file_size":2551276,"content_type":"application/pdf","date_updated":"2024-01-30T13:20:35Z","file_name":"2023_AJHG_Ojavee.pdf","access_level":"open_access","success":1,"file_id":"14912","creator":"dernst","relation":"main_file","checksum":"4108b031dc726ae6b4a5ae7e021ba188","date_created":"2024-01-30T13:20:35Z"}],"volume":110,"article_processing_charge":"Yes (via OA deal)","month":"09"},{"external_id":{"pmid":["38052961"],"isi":["001169777400004"]},"isi":1,"date_updated":"2025-09-09T14:00:46Z","department":[{"_id":"MaRo"}],"date_published":"2023-12-01T00:00:00Z","issue":"12","publisher":"Springer Nature","language":[{"iso":"eng"}],"oa_version":"None","citation":{"ista":"Ing-Simmons E, Machnik NN, Vaquerizas JM. 2023. Reply to: Revisiting the use of structural similarity index in Hi-C. Nature Genetics. 55(12), 2053–2055.","mla":"Ing-Simmons, Elizabeth, et al. “Reply to: Revisiting the Use of Structural Similarity Index in Hi-C.” <i>Nature Genetics</i>, vol. 55, no. 12, Springer Nature, 2023, pp. 2053–55, doi:<a href=\"https://doi.org/10.1038/s41588-023-01595-5\">10.1038/s41588-023-01595-5</a>.","chicago":"Ing-Simmons, Elizabeth, Nick N Machnik, and Juan M. Vaquerizas. “Reply to: Revisiting the Use of Structural Similarity Index in Hi-C.” <i>Nature Genetics</i>. Springer Nature, 2023. <a href=\"https://doi.org/10.1038/s41588-023-01595-5\">https://doi.org/10.1038/s41588-023-01595-5</a>.","ieee":"E. Ing-Simmons, N. N. Machnik, and J. M. Vaquerizas, “Reply to: Revisiting the use of structural similarity index in Hi-C,” <i>Nature Genetics</i>, vol. 55, no. 12. Springer Nature, pp. 2053–2055, 2023.","apa":"Ing-Simmons, E., Machnik, N. N., &#38; Vaquerizas, J. M. (2023). Reply to: Revisiting the use of structural similarity index in Hi-C. <i>Nature Genetics</i>. Springer Nature. <a href=\"https://doi.org/10.1038/s41588-023-01595-5\">https://doi.org/10.1038/s41588-023-01595-5</a>","ama":"Ing-Simmons E, Machnik NN, Vaquerizas JM. Reply to: Revisiting the use of structural similarity index in Hi-C. <i>Nature Genetics</i>. 2023;55(12):2053-2055. doi:<a href=\"https://doi.org/10.1038/s41588-023-01595-5\">10.1038/s41588-023-01595-5</a>","short":"E. Ing-Simmons, N.N. Machnik, J.M. Vaquerizas, Nature Genetics 55 (2023) 2053–2055."},"user_id":"317138e5-6ab7-11ef-aa6d-ffef3953e345","author":[{"first_name":"Elizabeth","last_name":"Ing-Simmons","full_name":"Ing-Simmons, Elizabeth"},{"full_name":"Machnik, Nick N","orcid":"0000-0001-6617-9742","last_name":"Machnik","first_name":"Nick N","id":"3591A0AA-F248-11E8-B48F-1D18A9856A87"},{"first_name":"Juan M.","last_name":"Vaquerizas","full_name":"Vaquerizas, Juan M."}],"_id":"14689","publication_identifier":{"eissn":["1546-1718"],"issn":["1061-4036"]},"doi":"10.1038/s41588-023-01595-5","article_type":"letter_note","quality_controlled":"1","publication_status":"published","year":"2023","type":"journal_article","status":"public","intvolume":"        55","page":"2053-2055","title":"Reply to: Revisiting the use of structural similarity index in Hi-C","article_processing_charge":"No","month":"12","volume":55,"scopus_import":"1","date_created":"2023-12-17T23:00:53Z","pmid":1,"day":"01","publication":"Nature Genetics"}]