[{"language":[{"iso":"eng"}],"month":"04","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","scopus_import":"1","oa_version":"Published Version","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.","article_number":"iyag042","corr_author":"1","department":[{"_id":"MaRo"}],"has_accepted_license":"1","article_processing_charge":"Yes (via OA deal)","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"}],"publication_identifier":{"issn":["1943-2631"]},"citation":{"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>","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).","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.","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.","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>","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>."},"file_date_updated":"2026-07-27T11:54:00Z","doi":"10.1093/genetics/iyag042","title":"A quantitative genetic model for indirect genetic effects and genomic imprinting under random and assortative mating","ddc":["570"],"date_updated":"2026-07-27T11:56:04Z","_id":"21484","publisher":"Oxford University Press","external_id":{"pmid":["41677404"]},"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.","author":[{"id":"30d4014e-7753-11eb-b44b-db6d61112e73","orcid":"0000-0002-5636-9259","last_name":"Krätschmer","first_name":"Ilse","full_name":"Krätschmer, Ilse"},{"full_name":"Robinson, Matthew Richard","first_name":"Matthew Richard","orcid":"0000-0001-8982-8813","last_name":"Robinson","id":"E5D42276-F5DA-11E9-8E24-6303E6697425"}],"day":"01","OA_place":"publisher","das_tickbox":"1","supplementarymaterial":"no","volume":232,"pmid":1,"OA_type":"hybrid","article_type":"original","file":[{"file_name":"2026_Genetics_Kraetschmer.pdf","date_created":"2026-07-27T11:54:00Z","access_level":"open_access","relation":"main_file","date_updated":"2026-07-27T11:54:00Z","checksum":"926322c83b02522e96ab7a86e4011eec","success":1,"file_id":"22425","content_type":"application/pdf","creator":"dernst","file_size":734475}],"quality_controlled":"1","fulldoi":"https://doi.org/10.1093/genetics/iyag042","publication_status":"published","tmp":{"image":"/images/cc_by.png","short":"CC BY (4.0)","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode"},"publication":"Genetics","date_created":"2026-03-23T15:02:54Z","date_published":"2026-04-01T00:00:00Z","oa":1,"status":"public","year":"2026","PlanS_conform":"1","researchdata_availability":"no","intvolume":"       232","related_material":{"link":[{"url":"https://github.com/medical-genomics-group/familyMC","relation":"software"}]},"type":"journal_article","issue":"4"},{"dataavailabilitystatement":"Access to the data is available with appropriate permission from the Generation Scotland Access Committee. Applications should be made to access@generationscotland.org. https://www.ed.ac.uk/lothian-birth-cohorts/data-access-collaboration gives information on the Lothian Birth Cohort data, which are available on request from the Lothian Birth Cohort Study, University of Edinburgh. Data from both cohorts are not publicly available as they contain information that could compromise participant consent and confidentiality.\r\n\r\nSource code is available at https://github.com/medical-genomics-group/MAJA.","article_number":"vbag231","oa_version":"Published Version","language":[{"iso":"eng"}],"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","month":"09","scopus_import":"1","date_updated":"2026-10-06T12:50:50Z","ddc":["570"],"publisher":"Oxford University Press","_id":"22972","external_id":{"pmid":["42761413"]},"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. We would like to acknowledge the participants and investigators of the Generation Scotland and Lothian Birth Cohort studies. 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) Reference 104036/Z/14/Z]. DNA methylation data for Generation Scotland was also funded by a 2018 NARSAD Young Investigator Grant from the Brain and Behavior Research Foundation [Ref: 27404; awardee: Dr David M Howard] and by a John, Margaret, Alfred and Stewart Sim Fellowship from the Royal College of Physicians of Edinburgh (Awardee: Dr Heather C Whalley). The LBC1936 is supported by the Biotechnology and Biological Sciences Research Council, and the Economic and Social Research Council [BB/W008793/1] (which supports SEH, and JC), Age UK (Disconnected Mind project), the Milton Damerel Trust, the Medical Research Council [G0701120, G1001245, MR/M013111/1, MR/R024065/1] 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. HS is supported by funding from the Wellcome Trust 4-year PhD in Translational Neuroscience [218493/Z/19/Z]. SRC is also supported by a Sir Henry Dale Fellowship jointly funded by Wellcome and the Royal Society [221890/Z/20/Z]. High-performance computing was supported by the Scientific Service Units (SSU) of IST Austria through resources provided by Scientific Computing (SciComp).","title":"MAJA: Multivariate Bayesian model for discovery of shared epigenetic pathways across human phenotypes","DOAJ_listed":"1","article_processing_charge":"Yes","abstract":[{"lang":"eng","text":"Genomic measurements of DNA methylation, gene expression or protein levels are becoming more prevalent and are increasingly used to study health outcomes. However, most proposed association testing methods consider only marginal effects of each feature on a single outcome variable and are not set up to handle highly correlated, continuous data. Here, we introduce MAJA, a method to learn shared and outcome-specific effects for multiple traits in multi-omics data. MAJA determines the unique contribution of individual loci, genes, or molecular pathways to variation in one or more traits, conditional on all other measured “omics” data genome-wide. Simulations show MAJA accurately finds shared and distinct associations between omics-data and multiple traits and estimates omics-specific (co)variances, allowing for sparsity and correlations within the data. Applying MAJA to 12 outcome traits in Generation Scotland methylation data (n = 18 264), we find novel shared epigenetic probes among cholesterol metabolism, osteoarthritis, blood pressure and asthma. In contrast to marginal testing, we find only 10 CpG probes with significant effects above the genome-wide background. This highlights the need for joint association testing in highly correlated methylation data from whole blood and for studies of increased sample size in order to refine epigenomic associations in observational data."}],"publication_identifier":{"eissn":["2635-0041"]},"citation":{"chicago":"Krätschmer, Ilse, Hannah M Smith, Daniel L McCartney, Elena Bernabeu, Mahdi Mahmoudi, Archie Campbell, Janie Corley, et al. “MAJA: Multivariate Bayesian Model for Discovery of Shared Epigenetic Pathways across Human Phenotypes.” <i>Bioinformatics Advances</i>. Oxford University Press, 2026. <a href=\"https://doi.org/10.1093/bioadv/vbag231\">https://doi.org/10.1093/bioadv/vbag231</a>.","short":"I. Krätschmer, H.M. Smith, D.L. McCartney, E. Bernabeu, M. Mahmoudi, A. Campbell, J. Corley, S.E. Harris, S.R. Cox, R.E. Marioni, M.R. Robinson, Bioinformatics Advances 6 (2026).","ama":"Krätschmer I, Smith HM, McCartney DL, et al. MAJA: Multivariate Bayesian model for discovery of shared epigenetic pathways across human phenotypes. <i>Bioinformatics Advances</i>. 2026;6(1). doi:<a href=\"https://doi.org/10.1093/bioadv/vbag231\">10.1093/bioadv/vbag231</a>","ieee":"I. Krätschmer <i>et al.</i>, “MAJA: Multivariate Bayesian model for discovery of shared epigenetic pathways across human phenotypes,” <i>Bioinformatics Advances</i>, vol. 6, no. 1. Oxford University Press, 2026.","ista":"Krätschmer I, Smith HM, McCartney DL, Bernabeu E, Mahmoudi M, Campbell A, Corley J, Harris SE, Cox SR, Marioni RE, Robinson MR. 2026. MAJA: Multivariate Bayesian model for discovery of shared epigenetic pathways across human phenotypes. Bioinformatics Advances. 6(1), vbag231.","mla":"Krätschmer, Ilse, et al. “MAJA: Multivariate Bayesian Model for Discovery of Shared Epigenetic Pathways across Human Phenotypes.” <i>Bioinformatics Advances</i>, vol. 6, no. 1, vbag231, Oxford University Press, 2026, doi:<a href=\"https://doi.org/10.1093/bioadv/vbag231\">10.1093/bioadv/vbag231</a>.","apa":"Krätschmer, I., Smith, H. M., McCartney, D. L., Bernabeu, E., Mahmoudi, M., Campbell, A., … Robinson, M. R. (2026). MAJA: Multivariate Bayesian model for discovery of shared epigenetic pathways across human phenotypes. <i>Bioinformatics Advances</i>. Oxford University Press. <a href=\"https://doi.org/10.1093/bioadv/vbag231\">https://doi.org/10.1093/bioadv/vbag231</a>"},"file_date_updated":"2026-10-06T12:49:18Z","doi":"10.1093/bioadv/vbag231","corr_author":"1","department":[{"_id":"MaRo"}],"has_accepted_license":"1","pmid":1,"article_type":"original","OA_type":"gold","file":[{"file_id":"23064","content_type":"application/pdf","success":1,"file_size":1370368,"creator":"dernst","date_created":"2026-10-06T12:49:18Z","access_level":"open_access","file_name":"2026_BioinformaticsAdvances_Kraetschmer.pdf","relation":"main_file","date_updated":"2026-10-06T12:49:18Z","checksum":"86849f1b88c47d050be4aa0a40105a97"}],"quality_controlled":"1","supplementarymaterial":"yes","volume":6,"OA_place":"publisher","das_tickbox":"1","author":[{"last_name":"Krätschmer","id":"30d4014e-7753-11eb-b44b-db6d61112e73","orcid":"0000-0002-5636-9259","full_name":"Krätschmer, Ilse","first_name":"Ilse"},{"first_name":"Hannah M","full_name":"Smith, Hannah M","last_name":"Smith"},{"first_name":"Daniel L","full_name":"McCartney, Daniel L","last_name":"McCartney"},{"first_name":"Elena","full_name":"Bernabeu, Elena","last_name":"Bernabeu"},{"last_name":"Mahmoudi","first_name":"Mahdi","full_name":"Mahmoudi, Mahdi"},{"full_name":"Campbell, Archie","first_name":"Archie","last_name":"Campbell"},{"last_name":"Corley","first_name":"Janie","full_name":"Corley, Janie"},{"first_name":"Sarah E","full_name":"Harris, Sarah E","last_name":"Harris"},{"full_name":"Cox, Simon R","first_name":"Simon R","last_name":"Cox"},{"full_name":"Marioni, Riccardo E","first_name":"Riccardo E","last_name":"Marioni"},{"full_name":"Robinson, Matthew Richard","first_name":"Matthew Richard","id":"E5D42276-F5DA-11E9-8E24-6303E6697425","orcid":"0000-0001-8982-8813","last_name":"Robinson"}],"day":"01","acknowledged_ssus":[{"_id":"ScienComp"}],"researchdata_availability":"no","status":"public","PlanS_conform":"1","year":"2026","intvolume":"         6","type":"journal_article","issue":"1","date_created":"2026-09-22T05:41:01Z","date_published":"2026-09-01T00:00:00Z","oa":1,"fulldoi":"https://doi.org/10.1093/bioadv/vbag231","publication_status":"published","tmp":{"image":"/images/cc_by.png","short":"CC BY (4.0)","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode"},"publication":"Bioinformatics Advances"},{"supplementarymaterial":"no","volume":17,"OA_type":"gold","article_type":"original","file":[{"content_type":"application/pdf","file_id":"23070","success":1,"file_size":2345578,"creator":"dernst","date_created":"2026-10-07T09:50:18Z","access_level":"open_access","file_name":"2026_NatureComm_Machnik.pdf","relation":"main_file","date_updated":"2026-10-07T09:50:18Z","checksum":"05517674168a275c70f18e964ce33d8b"}],"quality_controlled":"1","author":[{"orcid":"0000-0001-6617-9742","id":"3591A0AA-F248-11E8-B48F-1D18A9856A87","last_name":"Machnik","full_name":"Machnik, Nick N","first_name":"Nick N"},{"first_name":"Seyed Mahdi","full_name":"Mahmoudi, Seyed Mahdi","id":"b9f6d5ef-7774-11eb-a47f-df2c75c02ee7","last_name":"Mahmoudi"},{"last_name":"Borczyk","first_name":"Malgorzata","full_name":"Borczyk, Malgorzata"},{"first_name":"Ilse","full_name":"Krätschmer, Ilse","last_name":"Krätschmer","id":"30d4014e-7753-11eb-b44b-db6d61112e73","orcid":"0000-0002-5636-9259"},{"last_name":"Bauer","full_name":"Bauer, Markus J.","first_name":"Markus J."},{"orcid":"0000-0001-8982-8813","last_name":"Robinson","id":"E5D42276-F5DA-11E9-8E24-6303E6697425","full_name":"Robinson, Matthew Richard","first_name":"Matthew Richard"}],"day":"25","acknowledged_ssus":[{"_id":"ScienComp"}],"OA_place":"publisher","das_tickbox":"1","date_created":"2026-10-04T22:01:53Z","date_published":"2026-08-25T00:00:00Z","oa":1,"year":"2026","status":"public","researchdata_availability":"yes","related_material":{"record":[{"id":"18648","status":"public","relation":"earlier_version"}]},"intvolume":"        17","type":"journal_article","fulldoi":"https://doi.org/10.1038/s41467-026-76877-7","publication_status":"published","tmp":{"image":"/images/cc_by_nc_nd.png","short":"CC BY-NC-ND (4.0)","legal_code_url":"https://creativecommons.org/licenses/by-nc-nd/4.0/legalcode","name":"Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)"},"publication":"Nature Communications","dataavailabilitystatement":"This project uses the UK Biobank data under project number 35520.\r\nUK Biobank genotypic and phenotypic data is available through a\r\nformal request at (http://www.ukbiobank.ac.uk). All of the data generated\r\nin this study are provided in the Source Data file. Source data\r\nare provided with this paper. The CI-GWAS code is fully open source and available at https://github.\r\ncom/medical-genomics-group/ci-gwasand has been archived on\r\nZenodo52. The scripts used to execute the model are available at\r\nhttps://github.com/medical-genomics-group/ci-gwas. R version 4.2.1 is\r\navailable at https://www.r-project.org/. Plink version 1.9 is available at\r\nhttps://www.cog-genomics.org/plink/1.9/.","article_number":"10153 ","month":"08","language":[{"iso":"eng"}],"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","scopus_import":"1","oa_version":"Published Version","title":"Causal inference for multiple risk factors and diseases from genomics data","date_updated":"2026-10-16T11:01:16Z","ddc":["570"],"_id":"23043","publisher":"Springer Nature","acknowledgement":"We thank Zoltan Kutalik andmembers of the Robinson group at ISTA for\r\ntheir comments, which improved this manuscript. We would like to\r\nacknowledge the participants and investigators of the UK Biobank study.\r\nHigh-performance computing was supported by the Scientific Service\r\nUnits (SSU) of IST Austria through resources provided by Scientific\r\nComputing (SciComp). This work was funded by a research collaboration agreement between\r\nBoehringer Ingelheim and the research group of MRR at the Institute of\r\nScience and Technology Austria. Additional funding was also provided\r\nby an SNSF Eccellenza Grant to MRR (PCEGP3-181181), and by core\r\nfunding from the Institute of Science and Technology Austria. MB was\r\nfunded by the National Science Center Poland grant SONATINA UMO-\r\n2021/40/C/NZ2/00218.","corr_author":"1","department":[{"_id":"MaRo"}],"has_accepted_license":"1","article_processing_charge":"Yes","DOAJ_listed":"1","abstract":[{"text":"Mendelian randomization, the standard instrument variable causal-inference approach for genomics, can be severely biased by inadequate selection of appropriate instrument variables and horizontal pleiotropy. We introduce Causal Inference GWAS, a graphical approach that selects genetic instruments from summary statistics controlling for linkage and pleiotropy, accommodates rare variants and binary outcomes, distinguishes direct from indirect risk factors in high dimensions, and flags latent confounding. Applied to 9 risk factors, 4 metabolic disease outcomes, and 8.4M variants across 458,747 UK Biobank individuals, our method runs in 20 minutes, identifying only 696 genome-wide valid instruments. We replicate nearly all previously reported risk factor-outcome paths, but find that most are indistinguishable from unmeasured confounding. This suggests robust biobank causal inference will require longitudinal and family data to resolve temporal precedence from reverse causation. Our approach offers a principled first step toward screening genuine causal signal from phenotypic correlation in biobank data.","lang":"eng"}],"publication_identifier":{"eissn":["2041-1723"]},"doi":"10.1038/s41467-026-76877-7","file_date_updated":"2026-10-07T09:50:18Z","citation":{"ista":"Machnik NN, Mahmoudi SM, Borczyk M, Krätschmer I, Bauer MJ, Robinson MR. 2026. Causal inference for multiple risk factors and diseases from genomics data. Nature Communications. 17, 10153.","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>Nature Communications</i>, vol. 17. Springer Nature, 2026.","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>Nature Communications</i>. 2026;17. doi:<a href=\"https://doi.org/10.1038/s41467-026-76877-7\">10.1038/s41467-026-76877-7</a>","short":"N.N. Machnik, S.M. Mahmoudi, M. Borczyk, I. Krätschmer, M.J. Bauer, M.R. Robinson, Nature Communications 17 (2026).","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>Nature Communications</i>. Springer Nature, 2026. <a href=\"https://doi.org/10.1038/s41467-026-76877-7\">https://doi.org/10.1038/s41467-026-76877-7</a>.","apa":"Machnik, N. N., Mahmoudi, S. M., Borczyk, M., Krätschmer, I., Bauer, M. J., &#38; Robinson, M. R. (2026). Causal inference for multiple risk factors and diseases from genomics data. <i>Nature Communications</i>. Springer Nature. <a href=\"https://doi.org/10.1038/s41467-026-76877-7\">https://doi.org/10.1038/s41467-026-76877-7</a>","mla":"Machnik, Nick N., et al. “Causal Inference for Multiple Risk Factors and Diseases from Genomics Data.” <i>Nature Communications</i>, vol. 17, 10153, Springer Nature, 2026, doi:<a href=\"https://doi.org/10.1038/s41467-026-76877-7\">10.1038/s41467-026-76877-7</a>."}},{"project":[{"grant_number":"PCEGP3_181181","name":"Improving estimation and prediction of common complex disease risk","_id":"9B8D11D6-BA93-11EA-9121-9846C619BF3A"}],"article_number":"101277","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.","oa_version":"Published Version","month":"07","language":[{"iso":"eng"}],"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","scopus_import":"1","publisher":"Cell Press","_id":"21987","ddc":["570"],"date_updated":"2026-10-20T09:03:50Z","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).","external_id":{"pmid":["40909755"]},"title":"Separating direct, indirect, and parent-of-origin genetic effects in the human population","DOAJ_listed":"1","article_processing_charge":"Yes","citation":{"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, Cell Press, 2026, doi:<a href=\"https://doi.org/10.1016/j.xgen.2026.101277\">10.1016/j.xgen.2026.101277</a>.","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>. Cell Press. <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. Cell Press, 2026.","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.","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>. Cell Press, 2026. <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>"},"doi":"10.1016/j.xgen.2026.101277","file_date_updated":"2026-07-28T07:24:50Z","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"}],"publication_identifier":{"eissn":["2666-979X"]},"corr_author":"1","keyword":["direct genetic effects","DGE","indirect genetic effects","IGE","parent-of-origin effects","phenotypic variation","assortative mating","within-family GWAS","MoBa","EstBB"],"has_accepted_license":"1","department":[{"_id":"MaRo"}],"article_type":"original","OA_type":"gold","pmid":1,"quality_controlled":"1","file":[{"file_size":3679297,"creator":"dernst","file_id":"22597","content_type":"application/pdf","success":1,"date_updated":"2026-07-28T07:24:50Z","relation":"main_file","checksum":"f896b510480d2d4e4a7fd46c2e2761f4","date_created":"2026-07-28T07:24:50Z","access_level":"open_access","file_name":"2026_CellGenomics_Kraetschmer.pdf"}],"volume":6,"supplementarymaterial":"yes","OA_place":"publisher","das_tickbox":"1","author":[{"first_name":"Ilse","full_name":"Krätschmer, Ilse","orcid":"0000-0002-5636-9259","last_name":"Krätschmer","id":"30d4014e-7753-11eb-b44b-db6d61112e73"},{"last_name":"Hegemann","first_name":"Laura","full_name":"Hegemann, Laura"},{"full_name":"Hofmeister, Robin J.","first_name":"Robin J.","last_name":"Hofmeister"},{"last_name":"Corfield","full_name":"Corfield, Elizabeth C.","first_name":"Elizabeth C."},{"last_name":"Mahmoudi","first_name":"Mahdi","full_name":"Mahmoudi, Mahdi"},{"first_name":"Olivier","full_name":"Delaneau, Olivier","last_name":"Delaneau"},{"first_name":"Ole A.","full_name":"Andreassen, Ole A.","last_name":"Andreassen"},{"last_name":"Campbell","first_name":"Archie","full_name":"Campbell, Archie"},{"first_name":"Caroline","full_name":"Hayward, Caroline","last_name":"Hayward"},{"last_name":"Marioni","first_name":"Riccardo E.","full_name":"Marioni, Riccardo E."},{"first_name":"Eivind","full_name":"Ystrom, Eivind","last_name":"Ystrom"},{"last_name":"Havdahl","first_name":"Alexandra","full_name":"Havdahl, Alexandra"},{"first_name":"Matthew Richard","full_name":"Robinson, Matthew Richard","orcid":"0000-0001-8982-8813","id":"E5D42276-F5DA-11E9-8E24-6303E6697425","last_name":"Robinson"}],"acknowledged_ssus":[{"_id":"ScienComp"}],"day":"08","intvolume":"         6","related_material":{"link":[{"url":"https://ista.ac.at/en/news/human-traits-beyond-inherited-genes/","relation":"press_release","description":"News on ISTA website"}]},"year":"2026","researchdata_availability":"yes","status":"public","issue":"7","type":"journal_article","date_created":"2026-06-10T07:39:08Z","oa":1,"date_published":"2026-07-08T00:00:00Z","publication_status":"published","fulldoi":"https://doi.org/10.1016/j.xgen.2026.101277","publication":"Cell Genomics","tmp":{"image":"/images/cc_by_nc_nd.png","short":"CC BY-NC-ND (4.0)","legal_code_url":"https://creativecommons.org/licenses/by-nc-nd/4.0/legalcode","name":"Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)"}},{"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"}],"publication_identifier":{"eissn":["2666-979X"]},"doi":"10.1016/j.xgen.2026.101162","file_date_updated":"2026-07-28T07:06:26Z","citation":{"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, Cell Press, 2026, 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>. Cell Press. <a href=\"https://doi.org/10.1016/j.xgen.2026.101162\">https://doi.org/10.1016/j.xgen.2026.101162</a>","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>. Cell Press, 2026. <a href=\"https://doi.org/10.1016/j.xgen.2026.101162\">https://doi.org/10.1016/j.xgen.2026.101162</a>.","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>","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. Cell Press, 2026.","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."},"DOAJ_listed":"1","article_processing_charge":"Yes","department":[{"_id":"MaMo"},{"_id":"MaRo"}],"has_accepted_license":"1","corr_author":"1","external_id":{"pmid":["41713425"]},"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.","ddc":["000","570"],"date_updated":"2026-10-20T09:03:22Z","publisher":"Cell Press","_id":"21488","title":"Joint modeling of whole-genome sequencing data for human height via approximate message passing","oa_version":"Published Version","scopus_import":"1","month":"05","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","language":[{"iso":"eng"}],"project":[{"name":"Prix Lopez-Loretta 2019 - Marco Mondelli","_id":"059876FA-7A3F-11EA-A408-12923DDC885E"},{"grant_number":"101161364","_id":"911e6d1f-16d5-11f0-9cad-c5c68c6a1cdf","name":"Inference in High Dimensions: Light-speed Algorithms and Information Limits"},{"_id":"9B8D11D6-BA93-11EA-9121-9846C619BF3A","name":"Improving estimation and prediction of common complex disease risk","grant_number":"PCEGP3_181181"}],"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.","article_number":"101162","tmp":{"image":"/images/cc_by_nc_nd.png","short":"CC BY-NC-ND (4.0)","legal_code_url":"https://creativecommons.org/licenses/by-nc-nd/4.0/legalcode","name":"Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)"},"publication":"Cell Genomics","fulldoi":"https://doi.org/10.1016/j.xgen.2026.101162","publication_status":"published","type":"journal_article","issue":"5","status":"public","year":"2026","researchdata_availability":"yes","related_material":{"record":[{"status":"public","relation":"dissertation_contains","id":"22258"}],"link":[{"url":"https://ista.ac.at/en/news/big-data-and-human-height/","relation":"press_release","description":"News on ISTA website"}]},"intvolume":"         6","date_published":"2026-05-13T00:00:00Z","oa":1,"date_created":"2026-03-23T15:10:03Z","das_tickbox":"1","OA_place":"publisher","day":"13","author":[{"id":"0b77531d-dbcd-11ea-9d1d-a8eee0bf3830","last_name":"Depope","first_name":"Al","full_name":"Depope, Al"},{"last_name":"Bajzik","id":"b995e25b-8c4b-11ed-a6d8-f71b7bcd6122","full_name":"Bajzik, Jakub","first_name":"Jakub"},{"id":"27EB676C-8706-11E9-9510-7717E6697425","last_name":"Mondelli","orcid":"0000-0002-3242-7020","first_name":"Marco","full_name":"Mondelli, Marco"},{"id":"E5D42276-F5DA-11E9-8E24-6303E6697425","last_name":"Robinson","orcid":"0000-0001-8982-8813","full_name":"Robinson, Matthew Richard","first_name":"Matthew Richard"}],"file":[{"date_created":"2026-07-28T07:06:26Z","access_level":"open_access","file_name":"2026_CellGenomics_Depope.pdf","relation":"main_file","date_updated":"2026-07-28T07:06:26Z","checksum":"6b59686f8d9733add4f23d23f3dd9df0","file_id":"22596","content_type":"application/pdf","success":1,"file_size":3736705,"creator":"dernst"}],"quality_controlled":"1","pmid":1,"article_type":"original","OA_type":"gold","supplementarymaterial":"yes","volume":6},{"publication":"Clinical Epigenetics","tmp":{"image":"/images/cc_by.png","short":"CC BY (4.0)","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode"},"publication_status":"published","fulldoi":"https://doi.org/10.1186/s13148-025-01818-y","oa":1,"date_published":"2025-01-25T00:00:00Z","date_created":"2025-02-16T23:02:33Z","type":"journal_article","intvolume":"        17","year":"2025","status":"public","day":"25","author":[{"last_name":"Bernabeu","full_name":"Bernabeu, Elena","first_name":"Elena"},{"last_name":"Chybowska","first_name":"Aleksandra D.","full_name":"Chybowska, Aleksandra D."},{"full_name":"Kresovich, Jacob K.","first_name":"Jacob K.","last_name":"Kresovich"},{"last_name":"Suderman","full_name":"Suderman, Matthew","first_name":"Matthew"},{"last_name":"Mccartney","first_name":"Daniel L.","full_name":"Mccartney, Daniel L."},{"full_name":"Hillary, Robert F.","first_name":"Robert F.","last_name":"Hillary"},{"last_name":"Corley","first_name":"Janie","full_name":"Corley, Janie"},{"last_name":"Valdés-Hernández","first_name":"Maria Del C.","full_name":"Valdés-Hernández, Maria Del C."},{"last_name":"Maniega","full_name":"Maniega, Susana Muñoz","first_name":"Susana Muñoz"},{"first_name":"Mark E.","full_name":"Bastin, Mark E.","last_name":"Bastin"},{"last_name":"Wardlaw","first_name":"Joanna M.","full_name":"Wardlaw, Joanna M."},{"full_name":"Xu, Zongli","first_name":"Zongli","last_name":"Xu"},{"last_name":"Sandler","first_name":"Dale P.","full_name":"Sandler, Dale P."},{"first_name":"Archie","full_name":"Campbell, Archie","last_name":"Campbell"},{"first_name":"Sarah E.","full_name":"Harris, Sarah E.","last_name":"Harris"},{"last_name":"Mcintosh","first_name":"Andrew M.","full_name":"Mcintosh, Andrew M."},{"last_name":"Taylor","first_name":"Jack A.","full_name":"Taylor, Jack A."},{"first_name":"Paul","full_name":"Yousefi, Paul","last_name":"Yousefi"},{"last_name":"Cox","first_name":"Simon R.","full_name":"Cox, Simon R."},{"last_name":"Evans","full_name":"Evans, Kathryn L.","first_name":"Kathryn L."},{"full_name":"Robinson, Matthew Richard","first_name":"Matthew Richard","id":"E5D42276-F5DA-11E9-8E24-6303E6697425","orcid":"0000-0001-8982-8813","last_name":"Robinson"},{"last_name":"Vallejos","first_name":"Catalina A.","full_name":"Vallejos, Catalina A."},{"first_name":"Riccardo E.","full_name":"Marioni, Riccardo E.","last_name":"Marioni"}],"isi":1,"OA_place":"publisher","volume":17,"quality_controlled":"1","file":[{"content_type":"application/pdf","file_id":"19030","success":1,"file_size":1170930,"creator":"dernst","date_created":"2025-02-17T08:44:23Z","access_level":"open_access","file_name":"2025_ClinicalEpigenetics_Bernabeu.pdf","date_updated":"2025-02-17T08:44:23Z","relation":"main_file","checksum":"c32511f2d09e6c164116793e784944b8"}],"article_type":"original","OA_type":"gold","pmid":1,"has_accepted_license":"1","department":[{"_id":"MaRo"}],"doi":"10.1186/s13148-025-01818-y","citation":{"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>.","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>","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.","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.","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>.","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>"},"file_date_updated":"2025-02-17T08:44:23Z","publication_identifier":{"issn":["1868-7075"],"eissn":["1868-7083"]},"abstract":[{"lang":"eng","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."}],"article_processing_charge":"Yes","DOAJ_listed":"1","title":"Blood-based epigenome-wide association study and prediction of alcohol consumption","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.","external_id":{"pmid":["39863868"],"isi":["001406495600001"]},"_id":"19023","publisher":"Springer Nature","date_updated":"2025-09-30T10:31:08Z","ddc":["570"],"scopus_import":"1","language":[{"iso":"eng"}],"month":"01","user_id":"317138e5-6ab7-11ef-aa6d-ffef3953e345","oa_version":"Published Version","article_number":"14","project":[{"_id":"9B8D11D6-BA93-11EA-9121-9846C619BF3A","name":"Improving estimation and prediction of common complex disease risk","grant_number":"PCEGP3_181181"}]},{"project":[{"grant_number":"725746","name":"Quantitative analysis of DNA methylation maintenance with chromatin","_id":"62935a00-2b32-11ec-9570-eff30fa39068","call_identifier":"H2020"}],"scopus_import":"1","month":"09","language":[{"iso":"eng"}],"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","oa_version":"Published Version","title":"Gene body methylation regulates gene expression and mediates phenotypic diversity in natural Arabidopsis populations","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).","external_id":{"pmid":["40940427"],"isi":["001570197600001"]},"_id":"20479","publisher":"Springer Nature","date_updated":"2025-12-01T14:59:10Z","ddc":["580"],"has_accepted_license":"1","department":[{"_id":"MaRo"},{"_id":"DaZi"}],"corr_author":"1","doi":"10.1038/s41477-025-02108-4","citation":{"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.","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.","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>.","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>","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>.","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>"},"file_date_updated":"2025-10-23T11:13:58Z","publication_identifier":{"issn":["2055-0278"]},"abstract":[{"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.","lang":"eng"}],"article_processing_charge":"Yes (via OA deal)","volume":11,"quality_controlled":"1","file":[{"relation":"main_file","date_updated":"2025-10-23T11:13:58Z","checksum":"6a3f6cffdc934b8a2015c3c247f5a92a","file_name":"2025_NaturePlants_Shahzad.pdf","date_created":"2025-10-23T11:13:58Z","access_level":"open_access","creator":"dernst","file_size":7746662,"success":1,"content_type":"application/pdf","file_id":"20524"}],"OA_type":"hybrid","article_type":"original","pmid":1,"day":"12","author":[{"last_name":"Shahzad","full_name":"Shahzad, Zaigham","first_name":"Zaigham"},{"last_name":"Hollwey","id":"b8c4f54b-e484-11eb-8fdc-a54df64ef6dd","full_name":"Hollwey, Elizabeth","first_name":"Elizabeth"},{"last_name":"Moore","first_name":"Jonathan D.","full_name":"Moore, Jonathan D."},{"last_name":"Choi","first_name":"Jaemyung","full_name":"Choi, Jaemyung"},{"first_name":"Gaëlle","full_name":"Cassin-Ross, Gaëlle","last_name":"Cassin-Ross"},{"full_name":"Rouached, Hatem","first_name":"Hatem","last_name":"Rouached"},{"full_name":"Robinson, Matthew Richard","first_name":"Matthew Richard","orcid":"0000-0001-8982-8813","last_name":"Robinson","id":"E5D42276-F5DA-11E9-8E24-6303E6697425"},{"last_name":"Zilberman","id":"6973db13-dd5f-11ea-814e-b3e5455e9ed1","orcid":"0000-0002-0123-8649","first_name":"Daniel","full_name":"Zilberman, Daniel"}],"isi":1,"page":"2084-2099","ec_funded":1,"OA_place":"publisher","oa":1,"date_published":"2025-09-12T00:00:00Z","date_created":"2025-10-16T13:11:21Z","type":"journal_article","intvolume":"        11","PlanS_conform":"1","year":"2025","status":"public","publication":"Nature Plants","tmp":{"image":"/images/cc_by.png","short":"CC BY (4.0)","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode"},"publication_status":"published","fulldoi":"https://doi.org/10.1038/s41477-025-02108-4"},{"oa":1,"date_published":"2025-12-08T00:00:00Z","date_created":"2025-12-14T23:02:04Z","type":"journal_article","intvolume":"        26","status":"public","year":"2025","publication":"Genome Biology","tmp":{"image":"/images/cc_by.png","short":"CC BY (4.0)","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode"},"publication_status":"published","fulldoi":"https://doi.org/10.1186/s13059-025-03892-0","volume":26,"quality_controlled":"1","file":[{"success":1,"content_type":"application/pdf","file_id":"20825","creator":"dernst","file_size":2206991,"file_name":"2025_GenomeBiology_Robertson.pdf","date_created":"2025-12-15T13:18:07Z","access_level":"open_access","date_updated":"2025-12-15T13:18:07Z","relation":"main_file","checksum":"7c92919af1b5820d01e91e08906a411f"}],"article_type":"original","OA_type":"gold","pmid":1,"day":"08","author":[{"full_name":"Robertson, Josephine A.","first_name":"Josephine A.","last_name":"Robertson"},{"first_name":"Jakub","full_name":"Bajzik, Jakub","id":"b995e25b-8c4b-11ed-a6d8-f71b7bcd6122","last_name":"Bajzik"},{"full_name":"Vernardis, Spyros","first_name":"Spyros","last_name":"Vernardis"},{"first_name":"Aleksandra D.","full_name":"Chybowska, Aleksandra D.","last_name":"Chybowska"},{"last_name":"Mccartney","first_name":"Daniel L.","full_name":"Mccartney, Daniel L."},{"full_name":"Grauslys, Arturas","first_name":"Arturas","last_name":"Grauslys"},{"full_name":"Mur, Jure","first_name":"Jure","last_name":"Mur"},{"first_name":"Hannah M.","full_name":"Smith, Hannah M.","last_name":"Smith"},{"last_name":"Campbell","first_name":"Archie","full_name":"Campbell, Archie"},{"first_name":"Camilla","full_name":"Drake, Camilla","last_name":"Drake"},{"last_name":"Grant","full_name":"Grant, Hannah","first_name":"Hannah"},{"full_name":"Pearce, Jamie","first_name":"Jamie","last_name":"Pearce"},{"full_name":"Russ, Tom C.","first_name":"Tom C.","last_name":"Russ"},{"first_name":"Poppy","full_name":"Adkin, Poppy","last_name":"Adkin"},{"first_name":"Matthew","full_name":"White, Matthew","last_name":"White"},{"first_name":"Charles","full_name":"Brigden, Charles","last_name":"Brigden"},{"last_name":"Messner","full_name":"Messner, Christoph B.","first_name":"Christoph B."},{"last_name":"Porteous","first_name":"David J.","full_name":"Porteous, David J."},{"last_name":"Hayward","first_name":"Caroline","full_name":"Hayward, Caroline"},{"first_name":"Simon R.","full_name":"Cox, Simon R.","last_name":"Cox"},{"first_name":"Aleksej","full_name":"Zelezniak, Aleksej","last_name":"Zelezniak"},{"full_name":"Ralser, Markus","first_name":"Markus","last_name":"Ralser"},{"last_name":"Robinson","orcid":"0000-0001-8982-8813","id":"E5D42276-F5DA-11E9-8E24-6303E6697425","first_name":"Matthew Richard","full_name":"Robinson, Matthew Richard"},{"full_name":"Marioni, Riccardo E.","first_name":"Riccardo E.","last_name":"Marioni"}],"OA_place":"publisher","title":"Methylome-wide association studies and epigenetic biomarker development for 133 mass spectrometry-assessed circulating proteins in 14,671 Generation Scotland participants","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.","external_id":{"pmid":["41361833"]},"publisher":"Springer Nature","_id":"20816","date_updated":"2025-12-15T13:19:41Z","ddc":["570"],"has_accepted_license":"1","department":[{"_id":"MaRo"}],"doi":"10.1186/s13059-025-03892-0","citation":{"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>","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>.","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>","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>.","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).","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.","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."},"file_date_updated":"2025-12-15T13:18:07Z","publication_identifier":{"issn":["1474-7596"],"eissn":["1474-760X"]},"abstract":[{"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.","lang":"eng"}],"DOAJ_listed":"1","article_processing_charge":"Yes","article_number":"417","scopus_import":"1","language":[{"iso":"eng"}],"month":"12","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","oa_version":"Published Version"},{"volume":112,"pmid":1,"article_type":"original","OA_type":"hybrid","file":[{"checksum":"891d120554f07da2c35d38388c29a690","date_updated":"2025-01-08T09:26:42Z","relation":"main_file","file_name":"2025_AJHG_Smith.pdf","access_level":"open_access","date_created":"2025-01-08T09:26:42Z","creator":"dernst","file_size":2266488,"success":1,"file_id":"18776","content_type":"application/pdf"}],"quality_controlled":"1","isi":1,"author":[{"full_name":"Smith, Hannah M.","first_name":"Hannah M.","last_name":"Smith"},{"full_name":"Ng, Hong Kiat","first_name":"Hong Kiat","last_name":"Ng"},{"last_name":"Moodie","first_name":"Joanna E.","full_name":"Moodie, Joanna E."},{"last_name":"Gadd","first_name":"Danni A.","full_name":"Gadd, Danni A."},{"first_name":"Daniel L.","full_name":"Mccartney, Daniel L.","last_name":"Mccartney"},{"first_name":"Elena","full_name":"Bernabeu, Elena","last_name":"Bernabeu"},{"first_name":"Archie","full_name":"Campbell, Archie","last_name":"Campbell"},{"first_name":"Paul","full_name":"Redmond, Paul","last_name":"Redmond"},{"last_name":"Taylor","full_name":"Taylor, Adele","first_name":"Adele"},{"full_name":"Page, Danielle","first_name":"Danielle","last_name":"Page"},{"full_name":"Corley, Janie","first_name":"Janie","last_name":"Corley"},{"first_name":"Sarah E.","full_name":"Harris, Sarah E.","last_name":"Harris"},{"full_name":"Tay, Darwin","first_name":"Darwin","last_name":"Tay"},{"first_name":"Ian J.","full_name":"Deary, Ian J.","last_name":"Deary"},{"full_name":"Evans, Kathryn L.","first_name":"Kathryn L.","last_name":"Evans"},{"first_name":"Matthew Richard","full_name":"Robinson, Matthew Richard","id":"E5D42276-F5DA-11E9-8E24-6303E6697425","orcid":"0000-0001-8982-8813","last_name":"Robinson"},{"last_name":"Chambers","first_name":"John C.","full_name":"Chambers, John C."},{"last_name":"Loh","full_name":"Loh, Marie","first_name":"Marie"},{"last_name":"Cox","first_name":"Simon R.","full_name":"Cox, Simon R."},{"last_name":"Marioni","first_name":"Riccardo E.","full_name":"Marioni, Riccardo E."},{"last_name":"Hillary","first_name":"Robert F.","full_name":"Hillary, Robert F."}],"day":"02","OA_place":"publisher","page":"106-115","date_created":"2025-01-05T23:01:56Z","date_published":"2025-01-02T00:00:00Z","oa":1,"status":"public","year":"2025","intvolume":"       112","related_material":{"link":[{"url":"https://github.com/marioni-group/Metabolic_trait","relation":"software"}]},"type":"journal_article","issue":"1","fulldoi":"https://doi.org/10.1016/j.ajhg.2024.11.012","publication_status":"published","tmp":{"image":"/images/cc_by.png","short":"CC BY (4.0)","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode"},"publication":"American Journal of Human Genetics","month":"01","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","language":[{"iso":"eng"}],"scopus_import":"1","oa_version":"Published Version","title":"DNA methylation-based predictors of metabolic traits in Scottish and Singaporean cohorts","date_updated":"2026-10-20T08:53:52Z","ddc":["570"],"publisher":"Cell Press","_id":"18754","external_id":{"pmid":["39706196"],"isi":["001412498600001"]},"department":[{"_id":"MaRo"}],"has_accepted_license":"1","article_processing_charge":"No","publication_identifier":{"issn":["0002-9297"],"eissn":["1537-6605"]},"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."}],"doi":"10.1016/j.ajhg.2024.11.012","file_date_updated":"2025-01-08T09:26:42Z","citation":{"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>","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>. Cell Press, 2025. <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.","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.","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. Cell Press, 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>. Cell Press. <a href=\"https://doi.org/10.1016/j.ajhg.2024.11.012\">https://doi.org/10.1016/j.ajhg.2024.11.012</a>","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, Cell Press, 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>."}},{"main_file_link":[{"open_access":"1","url":"https://openreview.net/forum?id=aQYCDxfZV0"}],"oa_version":"Submitted Version","user_id":"317138e5-6ab7-11ef-aa6d-ffef3953e345","language":[{"iso":"eng"}],"month":"04","scopus_import":"1","project":[{"name":"Prix Lopez-Loretta 2019 - Marco Mondelli","_id":"059876FA-7A3F-11EA-A408-12923DDC885E"},{"name":"Improving estimation and prediction of common complex disease risk","_id":"9B8D11D6-BA93-11EA-9121-9846C619BF3A","grant_number":"PCEGP3_181181"}],"article_processing_charge":"No","publication_identifier":{"isbn":["9798350344851"],"issn":["1520-6149"]},"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."}],"doi":"10.1109/ICASSP48485.2024.10447198","citation":{"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.","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.","short":"A. Depope, M. Mondelli, M.R. Robinson, in:, 2024 IEEE International Conference on Acoustics, Speech, and Signal Processing, IEEE, 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>.","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>","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>.","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>"},"corr_author":"1","department":[{"_id":"MaMo"},{"_id":"MaRo"}],"date_updated":"2026-10-16T10:42:55Z","_id":"17147","publisher":"IEEE","external_id":{"isi":["001396233806078"]},"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).","title":"Inference of genetic effects via approximate message passing","OA_place":"repository","page":"13151-13155","das_tickbox":"0","isi":1,"author":[{"first_name":"Al","full_name":"Depope, Al","id":"0b77531d-dbcd-11ea-9d1d-a8eee0bf3830","last_name":"Depope"},{"orcid":"0000-0002-3242-7020","last_name":"Mondelli","id":"27EB676C-8706-11E9-9510-7717E6697425","first_name":"Marco","full_name":"Mondelli, Marco"},{"id":"E5D42276-F5DA-11E9-8E24-6303E6697425","orcid":"0000-0001-8982-8813","last_name":"Robinson","full_name":"Robinson, Matthew Richard","first_name":"Matthew Richard"}],"day":"19","acknowledged_ssus":[{"_id":"ScienComp"}],"OA_type":"green","quality_controlled":"1","supplementarymaterial":"no","fulldoi":"https://doi.org/10.1109/ICASSP48485.2024.10447198","publication_status":"published","publication":"2024 IEEE International Conference on Acoustics, Speech, and Signal Processing","conference":{"location":"Seoul, Korea","start_date":"2024-04-14","end_date":"2024-04-19","name":"ICASSP: International Conference on Acoustics, Speech and Signal Processing"},"researchdata_availability":"no","year":"2024","status":"public","type":"conference","date_created":"2024-06-16T22:01:07Z","date_published":"2024-04-19T00:00:00Z","oa":1},{"publication_status":"published","fulldoi":"https://doi.org/10.1101/2023.12.06.570392","publication":"bioRxiv","tmp":{"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)","legal_code_url":"https://creativecommons.org/licenses/by-nc/4.0/legalcode"},"date_created":"2024-12-11T10:42:59Z","oa":1,"date_published":"2024-08-10T00:00:00Z","related_material":{"record":[{"id":"23043","status":"public","relation":"later_version"},{"relation":"dissertation_contains","status":"public","id":"18642"}]},"researchdata_availability":"not applicable","status":"public","year":"2024","type":"preprint","author":[{"orcid":"0000-0001-6617-9742","last_name":"Machnik","id":"3591A0AA-F248-11E8-B48F-1D18A9856A87","full_name":"Machnik, Nick N","first_name":"Nick N"},{"first_name":"Seyed Mahdi","full_name":"Mahmoudi, Seyed Mahdi","id":"b9f6d5ef-7774-11eb-a47f-df2c75c02ee7","last_name":"Mahmoudi"},{"last_name":"Borczyk","full_name":"Borczyk, Malgorzata","first_name":"Malgorzata"},{"orcid":"0000-0002-5636-9259","id":"30d4014e-7753-11eb-b44b-db6d61112e73","last_name":"Krätschmer","first_name":"Ilse","full_name":"Krätschmer, Ilse"},{"first_name":"Markus J.","full_name":"Bauer, Markus J.","last_name":"Bauer"},{"id":"E5D42276-F5DA-11E9-8E24-6303E6697425","orcid":"0000-0001-8982-8813","last_name":"Robinson","first_name":"Matthew Richard","full_name":"Robinson, Matthew Richard"}],"acknowledged_ssus":[{"_id":"ScienComp"}],"day":"10","OA_place":"repository","das_tickbox":"0","license":"https://creativecommons.org/licenses/by-nc/4.0/","supplementarymaterial":"yes","OA_type":"free access","corr_author":"1","department":[{"_id":"MaRo"}],"article_processing_charge":"No","doi":"10.1101/2023.12.06.570392","citation":{"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>.","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>","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.","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>.","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>"},"abstract":[{"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","lang":"eng"}],"title":"Causal inference for multiple risk factors and diseases from genomics data","_id":"18648","date_updated":"2026-10-23T22:31:31Z","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). ","language":[{"iso":"eng"}],"user_id":"317138e5-6ab7-11ef-aa6d-ffef3953e345","month":"08","main_file_link":[{"open_access":"1","url":"https://doi.org/10.1101/2023.12.06.570392"}],"oa_version":"Preprint","project":[{"_id":"9B8D11D6-BA93-11EA-9121-9846C619BF3A","name":"Improving estimation and prediction of common complex disease risk","grant_number":"PCEGP3_181181"},{"_id":"bd936e6f-d553-11ed-ba76-a82299f63e8c","name":"Advanced statistical modelling to facilitate more accurate characterisation of disease phenotypes, improved genetic mapping, and effective therapeutic hypothesis generation","grant_number":"590359"}]},{"oa_version":"Published Version","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","language":[{"iso":"eng"}],"month":"02","scopus_import":"1","article_number":"12","article_processing_charge":"No","citation":{"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.","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>.","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>","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>.","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>"},"file_date_updated":"2023-03-14T10:29:47Z","doi":"10.1186/s13073-023-01161-y","publication_identifier":{"eissn":["1756-994X"]},"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."}],"has_accepted_license":"1","department":[{"_id":"MaRo"}],"_id":"12719","publisher":"Springer Nature","ddc":["570"],"date_updated":"2025-04-23T08:49:38Z","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.","external_id":{"pmid":["36855161"],"isi":["000940286600001"]},"title":"Refining epigenetic prediction of chronological and biological age","author":[{"first_name":"Elena","full_name":"Bernabeu, Elena","last_name":"Bernabeu"},{"last_name":"Mccartney","full_name":"Mccartney, Daniel L.","first_name":"Daniel L."},{"last_name":"Gadd","full_name":"Gadd, Danni A.","first_name":"Danni A."},{"full_name":"Hillary, Robert F.","first_name":"Robert F.","last_name":"Hillary"},{"full_name":"Lu, Ake T.","first_name":"Ake T.","last_name":"Lu"},{"first_name":"Lee","full_name":"Murphy, Lee","last_name":"Murphy"},{"full_name":"Wrobel, Nicola","first_name":"Nicola","last_name":"Wrobel"},{"first_name":"Archie","full_name":"Campbell, Archie","last_name":"Campbell"},{"last_name":"Harris","full_name":"Harris, Sarah E.","first_name":"Sarah E."},{"full_name":"Liewald, David","first_name":"David","last_name":"Liewald"},{"first_name":"Caroline","full_name":"Hayward, Caroline","last_name":"Hayward"},{"first_name":"Cathie","full_name":"Sudlow, Cathie","last_name":"Sudlow"},{"last_name":"Cox","first_name":"Simon R.","full_name":"Cox, Simon R."},{"last_name":"Evans","full_name":"Evans, Kathryn L.","first_name":"Kathryn L."},{"first_name":"Steve","full_name":"Horvath, Steve","last_name":"Horvath"},{"first_name":"Andrew M.","full_name":"Mcintosh, Andrew M.","last_name":"Mcintosh"},{"first_name":"Matthew Richard","full_name":"Robinson, Matthew Richard","id":"E5D42276-F5DA-11E9-8E24-6303E6697425","last_name":"Robinson","orcid":"0000-0001-8982-8813"},{"last_name":"Vallejos","first_name":"Catalina A.","full_name":"Vallejos, Catalina A."},{"last_name":"Marioni","full_name":"Marioni, Riccardo E.","first_name":"Riccardo E."}],"isi":1,"day":"28","article_type":"original","pmid":1,"quality_controlled":"1","file":[{"success":1,"content_type":"application/pdf","file_id":"12722","creator":"cchlebak","file_size":4275987,"file_name":"2023_GenomeMed_Bernabeu.pdf","date_created":"2023-03-14T10:29:47Z","access_level":"open_access","relation":"main_file","date_updated":"2023-03-14T10:29:47Z","checksum":"833b837910c4db42fb5f0f34125f77a7"}],"volume":15,"publication_status":"published","fulldoi":"https://doi.org/10.1186/s13073-023-01161-y","publication":"Genome Medicine","tmp":{"image":"/images/cc_by.png","short":"CC BY (4.0)","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode"},"intvolume":"        15","status":"public","year":"2023","type":"journal_article","date_created":"2023-03-12T23:01:02Z","oa":1,"date_published":"2023-02-28T00:00:00Z"},{"oa_version":"Published Version","language":[{"iso":"eng"}],"user_id":"317138e5-6ab7-11ef-aa6d-ffef3953e345","month":"09","scopus_import":"1","article_processing_charge":"Yes (via OA deal)","publication_identifier":{"eissn":["1537-6605"],"issn":["0002-9297"]},"abstract":[{"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.","lang":"eng"}],"file_date_updated":"2024-01-30T13:20:35Z","citation":{"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>","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>.","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>","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.","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>.","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.","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."},"doi":"10.1016/j.ajhg.2023.07.006","corr_author":"1","department":[{"_id":"MaRo"}],"has_accepted_license":"1","ddc":["570"],"date_updated":"2025-09-09T12:51:20Z","publisher":"Elsevier","_id":"14258","external_id":{"pmid":["37543033"],"isi":["001074842500001"]},"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.","title":"Genetic insights into the age-specific biological mechanisms governing human ovarian aging","page":"1549-1563","isi":1,"author":[{"last_name":"Ojavee","full_name":"Ojavee, Sven E.","first_name":"Sven E."},{"last_name":"Darrous","first_name":"Liza","full_name":"Darrous, Liza"},{"last_name":"Patxot","first_name":"Marion","full_name":"Patxot, Marion"},{"last_name":"Läll","first_name":"Kristi","full_name":"Läll, Kristi"},{"full_name":"Fischer, Krista","first_name":"Krista","last_name":"Fischer"},{"last_name":"Mägi","first_name":"Reedik","full_name":"Mägi, Reedik"},{"first_name":"Zoltan","full_name":"Kutalik, Zoltan","last_name":"Kutalik"},{"last_name":"Robinson","id":"E5D42276-F5DA-11E9-8E24-6303E6697425","orcid":"0000-0001-8982-8813","full_name":"Robinson, Matthew Richard","first_name":"Matthew Richard"}],"day":"07","pmid":1,"article_type":"original","file":[{"file_name":"2023_AJHG_Ojavee.pdf","date_created":"2024-01-30T13:20:35Z","access_level":"open_access","date_updated":"2024-01-30T13:20:35Z","relation":"main_file","checksum":"4108b031dc726ae6b4a5ae7e021ba188","success":1,"file_id":"14912","content_type":"application/pdf","creator":"dernst","file_size":2551276}],"quality_controlled":"1","volume":110,"fulldoi":"https://doi.org/10.1016/j.ajhg.2023.07.006","publication_status":"published","tmp":{"image":"/images/cc_by.png","short":"CC BY (4.0)","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode"},"publication":"American Journal of Human Genetics","year":"2023","status":"public","intvolume":"       110","type":"journal_article","issue":"9","date_created":"2023-09-03T22:01:15Z","date_published":"2023-09-07T00:00:00Z","oa":1},{"article_number":"e2121279119","scopus_import":"1","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","language":[{"iso":"eng"}],"month":"07","oa_version":"Published Version","title":"Improving GWAS discovery and genomic prediction accuracy in biobank data","acknowledgement":"This project was funded by Swiss National Science Foundation Eccellenza Grant PCEGP3-181181(toM.R.R.) and by core funding from the Institute of Science and Technology Austria. P.M.V. acknowledges funding from the Australian National Health and Medical Research Council (1113400) and the Australian Research Council (FL180100072). K.L. and R.M. were supported by the Estonian Research Council Grant PRG687. Estonian Biobank computations were performed in the High-Performance Computing Centre, University of Tartu.","external_id":{"isi":["000881496900003"],"pmid":["35905320"]},"_id":"11733","publisher":"National Academy of Sciences","date_updated":"2025-06-12T06:22:37Z","ddc":["570"],"has_accepted_license":"1","department":[{"_id":"MaRo"}],"corr_author":"1","file_date_updated":"2022-08-08T07:31:19Z","citation":{"ieee":"E. J. Orliac <i>et al.</i>, “Improving GWAS discovery and genomic prediction accuracy in biobank data,” <i>Proceedings of the National Academy of Sciences of the United States of America</i>, vol. 119, no. 31. National Academy of Sciences, 2022.","ista":"Orliac EJ, Trejo Banos D, Ojavee SE, Läll K, Mägi R, Visscher PM, Robinson MR. 2022. Improving GWAS discovery and genomic prediction accuracy in biobank data. Proceedings of the National Academy of Sciences of the United States of America. 119(31), e2121279119.","short":"E.J. Orliac, D. Trejo Banos, S.E. Ojavee, K. Läll, R. Mägi, P.M. Visscher, M.R. Robinson, Proceedings of the National Academy of Sciences of the United States of America 119 (2022).","chicago":"Orliac, Etienne J., Daniel Trejo Banos, Sven E. Ojavee, Kristi Läll, Reedik Mägi, Peter M. Visscher, and Matthew Richard Robinson. “Improving GWAS Discovery and Genomic Prediction Accuracy in Biobank Data.” <i>Proceedings of the National Academy of Sciences of the United States of America</i>. National Academy of Sciences, 2022. <a href=\"https://doi.org/10.1073/pnas.2121279119\">https://doi.org/10.1073/pnas.2121279119</a>.","ama":"Orliac EJ, Trejo Banos D, Ojavee SE, et al. Improving GWAS discovery and genomic prediction accuracy in biobank data. <i>Proceedings of the National Academy of Sciences of the United States of America</i>. 2022;119(31). doi:<a href=\"https://doi.org/10.1073/pnas.2121279119\">10.1073/pnas.2121279119</a>","mla":"Orliac, Etienne J., et al. “Improving GWAS Discovery and Genomic Prediction Accuracy in Biobank Data.” <i>Proceedings of the National Academy of Sciences of the United States of America</i>, vol. 119, no. 31, e2121279119, National Academy of Sciences, 2022, doi:<a href=\"https://doi.org/10.1073/pnas.2121279119\">10.1073/pnas.2121279119</a>.","apa":"Orliac, E. J., Trejo Banos, D., Ojavee, S. E., Läll, K., Mägi, R., Visscher, P. M., &#38; Robinson, M. R. (2022). Improving GWAS discovery and genomic prediction accuracy in biobank data. <i>Proceedings of the National Academy of Sciences of the United States of America</i>. National Academy of Sciences. <a href=\"https://doi.org/10.1073/pnas.2121279119\">https://doi.org/10.1073/pnas.2121279119</a>"},"doi":"10.1073/pnas.2121279119","publication_identifier":{"eissn":["1091-6490"]},"abstract":[{"text":"Genetically informed, deep-phenotyped biobanks are an important research resource and it is imperative that the most powerful, versatile, and efficient analysis approaches are used. Here, we apply our recently developed Bayesian grouped mixture of regressions model (GMRM) in the UK and Estonian Biobanks and obtain the highest genomic prediction accuracy reported to date across 21 heritable traits. When compared to other approaches, GMRM accuracy was greater than annotation prediction models run in the LDAK or LDPred-funct software by 15% (SE 7%) and 14% (SE 2%), respectively, and was 18% (SE 3%) greater than a baseline BayesR model without single-nucleotide polymorphism (SNP) markers grouped into minor allele frequency–linkage disequilibrium (MAF-LD) annotation categories. For height, the prediction accuracy R2 was 47% in a UK Biobank holdout sample, which was 76% of the estimated h2SNP. We then extend our GMRM prediction model to provide mixed-linear model association (MLMA) SNP marker estimates for genome-wide association (GWAS) discovery, which increased the independent loci detected to 16,162 in unrelated UK Biobank individuals, compared to 10,550 from BoltLMM and 10,095 from Regenie, a 62 and 65% increase, respectively. The average χ2 value of the leading markers increased by 15.24 (SE 0.41) for every 1% increase in prediction accuracy gained over a baseline BayesR model across the traits. Thus, we show that modeling genetic associations accounting for MAF and LD differences among SNP markers, and incorporating prior knowledge of genomic function, is important for both genomic prediction and discovery in large-scale individual-level studies.","lang":"eng"}],"article_processing_charge":"No","volume":119,"quality_controlled":"1","file":[{"creator":"dernst","file_size":1001164,"success":1,"file_id":"11745","content_type":"application/pdf","relation":"main_file","date_updated":"2022-08-08T07:31:19Z","checksum":"b5d2024e19fbad6f85a5e384e44d0f3b","file_name":"2022_PNAS_Orliac.pdf","date_created":"2022-08-08T07:31:19Z","access_level":"open_access"}],"article_type":"original","pmid":1,"day":"29","author":[{"full_name":"Orliac, Etienne J.","first_name":"Etienne J.","last_name":"Orliac"},{"last_name":"Trejo Banos","full_name":"Trejo Banos, Daniel","first_name":"Daniel"},{"full_name":"Ojavee, Sven E.","first_name":"Sven E.","last_name":"Ojavee"},{"last_name":"Läll","first_name":"Kristi","full_name":"Läll, Kristi"},{"last_name":"Mägi","full_name":"Mägi, Reedik","first_name":"Reedik"},{"full_name":"Visscher, Peter M.","first_name":"Peter M.","last_name":"Visscher"},{"id":"E5D42276-F5DA-11E9-8E24-6303E6697425","orcid":"0000-0001-8982-8813","last_name":"Robinson","first_name":"Matthew Richard","full_name":"Robinson, Matthew Richard"}],"isi":1,"oa":1,"date_published":"2022-07-29T00:00:00Z","date_created":"2022-08-07T22:01:56Z","issue":"31","type":"journal_article","related_material":{"record":[{"status":"public","id":"13064","relation":"research_data"}]},"intvolume":"       119","status":"public","year":"2022","publication":"Proceedings of the National Academy of Sciences of the United States of America","tmp":{"image":"/images/cc_by_nc_nd.png","short":"CC BY-NC-ND (4.0)","legal_code_url":"https://creativecommons.org/licenses/by-nc-nd/4.0/legalcode","name":"Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)"},"publication_status":"published","fulldoi":"https://doi.org/10.1073/pnas.2121279119"},{"date_published":"2022-11-03T00:00:00Z","oa":1,"date_created":"2023-01-12T12:05:28Z","type":"journal_article","issue":"11","status":"public","year":"2022","intvolume":"       109","tmp":{"image":"/images/cc_by_nc_nd.png","short":"CC BY-NC-ND (4.0)","legal_code_url":"https://creativecommons.org/licenses/by-nc-nd/4.0/legalcode","name":"Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)"},"publication":"The American Journal of Human Genetics","fulldoi":"https://doi.org/10.1016/j.ajhg.2022.09.011","publication_status":"published","volume":109,"file":[{"success":1,"content_type":"application/pdf","file_id":"12353","creator":"dernst","file_size":705195,"file_name":"2022_AJHG_Ojavee.pdf","access_level":"open_access","date_created":"2023-01-24T09:23:01Z","checksum":"4cd7f12bfe21a8237bb095eedfa26361","relation":"main_file","date_updated":"2023-01-24T09:23:01Z"}],"quality_controlled":"1","pmid":1,"article_type":"original","day":"03","acknowledged_ssus":[{"_id":"ScienComp"}],"isi":1,"author":[{"last_name":"Ojavee","first_name":"Sven E.","full_name":"Ojavee, Sven E."},{"last_name":"Kutalik","full_name":"Kutalik, Zoltan","first_name":"Zoltan"},{"last_name":"Robinson","id":"E5D42276-F5DA-11E9-8E24-6303E6697425","orcid":"0000-0001-8982-8813","first_name":"Matthew Richard","full_name":"Robinson, Matthew Richard"}],"page":"2009-2017","title":"Liability-scale heritability estimation for biobank studies of low-prevalence disease","external_id":{"pmid":["36265482"],"isi":["000898683500006"]},"acknowledgement":"This project was funded by an SNSF Eccellenza grant to M.R.R. (PCEGP3-181181), core funding from the Institute of Science and Technology Austria, and core funding from the Department of Computational Biology of the University of Lausanne. Z.K. was funded by the Swiss National Science Foundation (310030-189147). This research was supported by the Scientific Service Units (SSUs) of IST Austria through resources provided by Scientific Computing (SciComp). We would like to thank the participants of the UK Biobank.","ddc":["570"],"date_updated":"2025-06-11T13:55:19Z","publisher":"Elsevier","_id":"12142","department":[{"_id":"MaRo"}],"has_accepted_license":"1","keyword":["Genetics (clinical)","Genetics"],"corr_author":"1","publication_identifier":{"issn":["0002-9297"]},"abstract":[{"lang":"eng","text":"Theory for liability-scale models of the underlying genetic basis of complex disease provides an important way to interpret, compare, and understand results generated from biological studies. In particular, through estimation of the liability-scale heritability (LSH), liability models facilitate an understanding and comparison of the relative importance of genetic and environmental risk factors that shape different clinically important disease outcomes. Increasingly, large-scale biobank studies that link genetic information to electronic health records, containing hundreds of disease diagnosis indicators that mostly occur infrequently within the sample, are becoming available. Here, we propose an extension of the existing liability-scale model theory suitable for estimating LSH in biobank studies of low-prevalence disease. In a simulation study, we find that our derived expression yields lower mean square error (MSE) and is less sensitive to prevalence misspecification as compared to previous transformations for diseases with  =< 2% population prevalence and LSH of =< 0.45, especially if the biobank sample prevalence is less than that of the wider population. Applying our expression to 13 diagnostic outcomes of  =< 3% prevalence in the UK Biobank study revealed important differences in LSH obtained from the different theoretical expressions that impact the conclusions made when comparing LSH across disease outcomes. This demonstrates the importance of careful consideration for estimation and prediction of low-prevalence disease outcomes and facilitates improved inference of the underlying genetic basis of  =< 2% population prevalence diseases, especially where biobank sample ascertainment results in a healthier sample population."}],"doi":"10.1016/j.ajhg.2022.09.011","file_date_updated":"2023-01-24T09:23:01Z","citation":{"mla":"Ojavee, Sven E., et al. “Liability-Scale Heritability Estimation for Biobank Studies of Low-Prevalence Disease.” <i>The American Journal of Human Genetics</i>, vol. 109, no. 11, Elsevier, 2022, pp. 2009–17, doi:<a href=\"https://doi.org/10.1016/j.ajhg.2022.09.011\">10.1016/j.ajhg.2022.09.011</a>.","apa":"Ojavee, S. E., Kutalik, Z., &#38; Robinson, M. R. (2022). Liability-scale heritability estimation for biobank studies of low-prevalence disease. <i>The American Journal of Human Genetics</i>. Elsevier. <a href=\"https://doi.org/10.1016/j.ajhg.2022.09.011\">https://doi.org/10.1016/j.ajhg.2022.09.011</a>","short":"S.E. Ojavee, Z. Kutalik, M.R. Robinson, The American Journal of Human Genetics 109 (2022) 2009–2017.","chicago":"Ojavee, Sven E., Zoltan Kutalik, and Matthew Richard Robinson. “Liability-Scale Heritability Estimation for Biobank Studies of Low-Prevalence Disease.” <i>The American Journal of Human Genetics</i>. Elsevier, 2022. <a href=\"https://doi.org/10.1016/j.ajhg.2022.09.011\">https://doi.org/10.1016/j.ajhg.2022.09.011</a>.","ama":"Ojavee SE, Kutalik Z, Robinson MR. Liability-scale heritability estimation for biobank studies of low-prevalence disease. <i>The American Journal of Human Genetics</i>. 2022;109(11):2009-2017. doi:<a href=\"https://doi.org/10.1016/j.ajhg.2022.09.011\">10.1016/j.ajhg.2022.09.011</a>","ieee":"S. E. Ojavee, Z. Kutalik, and M. R. Robinson, “Liability-scale heritability estimation for biobank studies of low-prevalence disease,” <i>The American Journal of Human Genetics</i>, vol. 109, no. 11. Elsevier, pp. 2009–2017, 2022.","ista":"Ojavee SE, Kutalik Z, Robinson MR. 2022. Liability-scale heritability estimation for biobank studies of low-prevalence disease. The American Journal of Human Genetics. 109(11), 2009–2017."},"article_processing_charge":"Yes (via OA deal)","project":[{"_id":"9B8D11D6-BA93-11EA-9121-9846C619BF3A","name":"Improving estimation and prediction of common complex disease risk","grant_number":"PCEGP3_181181"}],"scopus_import":"1","month":"11","language":[{"iso":"eng"}],"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","oa_version":"Published Version"},{"oa_version":"Published Version","month":"11","language":[{"iso":"eng"}],"user_id":"4359f0d1-fa6c-11eb-b949-802e58b17ae8","scopus_import":"1","publisher":"Wiley","_id":"12235","ddc":["570","610"],"date_updated":"2024-10-09T21:03:49Z","acknowledgement":"This project was funded by an SNSF Eccellenza Grant to MRR (PCEGP3-181181), and by core funding from the Institute of Science and Technology Austria. We would like to thank the participants of the study and all the midwives and doctors involved for the computerized obstetrical data from the CHUV Maternity Hospital. Open access funding provided by Universite de Lausanne.","external_id":{"pmid":["36059200"],"isi":["000849690500001"]},"title":"Haematological changes from conception to childbirth: An indicator of major pregnancy complications","article_processing_charge":"No","doi":"10.1111/ejh.13844","citation":{"apa":"Patxot, M., Stojanov, M., Ojavee, S. E., Gobert, R. P., Kutalik, Z., Gavillet, M., … Robinson, M. R. (2022). Haematological changes from conception to childbirth: An indicator of major pregnancy complications. <i>European Journal of Haematology</i>. Wiley. <a href=\"https://doi.org/10.1111/ejh.13844\">https://doi.org/10.1111/ejh.13844</a>","mla":"Patxot, Marion, et al. “Haematological Changes from Conception to Childbirth: An Indicator of Major Pregnancy Complications.” <i>European Journal of Haematology</i>, vol. 109, no. 5, Wiley, 2022, pp. 566–75, doi:<a href=\"https://doi.org/10.1111/ejh.13844\">10.1111/ejh.13844</a>.","ama":"Patxot M, Stojanov M, Ojavee SE, et al. Haematological changes from conception to childbirth: An indicator of major pregnancy complications. <i>European Journal of Haematology</i>. 2022;109(5):566-575. doi:<a href=\"https://doi.org/10.1111/ejh.13844\">10.1111/ejh.13844</a>","chicago":"Patxot, Marion, Miloš Stojanov, Sven Erik Ojavee, Rosanna Pescini Gobert, Zoltán Kutalik, Mathilde Gavillet, David Baud, and Matthew Richard Robinson. “Haematological Changes from Conception to Childbirth: An Indicator of Major Pregnancy Complications.” <i>European Journal of Haematology</i>. Wiley, 2022. <a href=\"https://doi.org/10.1111/ejh.13844\">https://doi.org/10.1111/ejh.13844</a>.","short":"M. Patxot, M. Stojanov, S.E. Ojavee, R.P. Gobert, Z. Kutalik, M. Gavillet, D. Baud, M.R. Robinson, European Journal of Haematology 109 (2022) 566–575.","ista":"Patxot M, Stojanov M, Ojavee SE, Gobert RP, Kutalik Z, Gavillet M, Baud D, Robinson MR. 2022. Haematological changes from conception to childbirth: An indicator of major pregnancy complications. European Journal of Haematology. 109(5), 566–575.","ieee":"M. Patxot <i>et al.</i>, “Haematological changes from conception to childbirth: An indicator of major pregnancy complications,” <i>European Journal of Haematology</i>, vol. 109, no. 5. Wiley, pp. 566–575, 2022."},"file_date_updated":"2023-01-27T11:42:43Z","abstract":[{"lang":"eng","text":"Background: About 800 women die every day worldwide from pregnancy-related complications, including excessive blood loss, infections and high-blood pressure (World Health Organization, 2019). To improve screening for high-risk pregnancies, we set out to identify patterns of maternal hematological changes associated with future pregnancy complications.\r\n\r\nMethods: Using mixed effects models, we established changes in 14 complete blood count (CBC) parameters for 1710 healthy pregnancies and compared them to measurements from 98 pregnancy-induced hypertension, 106 gestational diabetes and 339 postpartum hemorrhage cases.\r\n\r\nResults: Results show interindividual variations, but good individual repeatability in CBC values during physiological pregnancies, allowing the identification of specific alterations in women with obstetric complications. For example, in women with uncomplicated pregnancies, haemoglobin count decreases of 0.12 g/L (95% CI −0.16, −0.09) significantly per gestation week (p value <.001). Interestingly, this decrease is three times more pronounced in women who will develop pregnancy-induced hypertension, with an additional decrease of 0.39 g/L (95% CI −0.51, −0.26). We also confirm that obstetric complications and white CBC predict the likelihood of giving birth earlier during pregnancy.\r\n\r\nConclusion: We provide a comprehensive description of the associations between haematological changes through pregnancy and three major obstetric complications to support strategies for prevention, early-diagnosis and maternal care."}],"publication_identifier":{"eissn":["1600-0609"],"issn":["0902-4441"]},"corr_author":"1","keyword":["Hematology","General Medicine"],"has_accepted_license":"1","department":[{"_id":"MaRo"}],"article_type":"original","pmid":1,"quality_controlled":"1","file":[{"file_size":1225073,"creator":"dernst","content_type":"application/pdf","file_id":"12426","success":1,"checksum":"a676d732f67c2990197e34f96b219370","date_updated":"2023-01-27T11:42:43Z","relation":"main_file","access_level":"open_access","date_created":"2023-01-27T11:42:43Z","file_name":"2022_EuropJourHaematology_Patxot.pdf"}],"volume":109,"page":"566-575","author":[{"full_name":"Patxot, Marion","first_name":"Marion","last_name":"Patxot"},{"full_name":"Stojanov, Miloš","first_name":"Miloš","last_name":"Stojanov"},{"last_name":"Ojavee","first_name":"Sven Erik","full_name":"Ojavee, Sven Erik"},{"last_name":"Gobert","first_name":"Rosanna Pescini","full_name":"Gobert, Rosanna Pescini"},{"last_name":"Kutalik","full_name":"Kutalik, Zoltán","first_name":"Zoltán"},{"last_name":"Gavillet","first_name":"Mathilde","full_name":"Gavillet, Mathilde"},{"last_name":"Baud","full_name":"Baud, David","first_name":"David"},{"last_name":"Robinson","orcid":"0000-0001-8982-8813","id":"E5D42276-F5DA-11E9-8E24-6303E6697425","first_name":"Matthew Richard","full_name":"Robinson, Matthew Richard"}],"isi":1,"day":"01","intvolume":"       109","status":"public","year":"2022","issue":"5","type":"journal_article","date_created":"2023-01-16T09:50:58Z","oa":1,"date_published":"2022-11-01T00:00:00Z","publication_status":"published","fulldoi":"https://doi.org/10.1111/ejh.13844","publication":"European Journal of Haematology","tmp":{"image":"/images/cc_by_nc_nd.png","short":"CC BY-NC-ND (4.0)","legal_code_url":"https://creativecommons.org/licenses/by-nc-nd/4.0/legalcode","name":"Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)"}},{"_id":"13064","publisher":"Dryad","related_material":{"record":[{"status":"public","id":"11733","relation":"used_in_publication"}]},"date_updated":"2025-06-12T06:22:36Z","status":"public","year":"2022","ddc":["570"],"type":"research_data_reference","date_created":"2023-05-23T16:28:13Z","oa":1,"title":"Improving genome-wide association discovery and genomic prediction accuracy in biobank data","date_published":"2022-09-02T00:00:00Z","fulldoi":"https://doi.org/10.5061/DRYAD.GTHT76HMZ","article_processing_charge":"No","doi":"10.5061/DRYAD.GTHT76HMZ","citation":{"mla":"Orliac, Etienne, et al. <i>Improving Genome-Wide Association Discovery and Genomic Prediction Accuracy in Biobank Data</i>. Dryad, 2022, doi:<a href=\"https://doi.org/10.5061/DRYAD.GTHT76HMZ\">10.5061/DRYAD.GTHT76HMZ</a>.","apa":"Orliac, E., Trejo Banos, D., Ojavee, S., Läll, K., Mägi, R., Visscher, P., &#38; Robinson, M. R. (2022). Improving genome-wide association discovery and genomic prediction accuracy in biobank data. Dryad. <a href=\"https://doi.org/10.5061/DRYAD.GTHT76HMZ\">https://doi.org/10.5061/DRYAD.GTHT76HMZ</a>","short":"E. Orliac, D. Trejo Banos, S. Ojavee, K. Läll, R. Mägi, P. Visscher, M.R. Robinson, (2022).","chicago":"Orliac, Etienne, Daniel Trejo Banos, Sven Ojavee, Kristi Läll, Reedik Mägi, Peter Visscher, and Matthew Richard Robinson. “Improving Genome-Wide Association Discovery and Genomic Prediction Accuracy in Biobank Data.” Dryad, 2022. <a href=\"https://doi.org/10.5061/DRYAD.GTHT76HMZ\">https://doi.org/10.5061/DRYAD.GTHT76HMZ</a>.","ama":"Orliac E, Trejo Banos D, Ojavee S, et al. Improving genome-wide association discovery and genomic prediction accuracy in biobank data. 2022. doi:<a href=\"https://doi.org/10.5061/DRYAD.GTHT76HMZ\">10.5061/DRYAD.GTHT76HMZ</a>","ieee":"E. Orliac <i>et al.</i>, “Improving genome-wide association discovery and genomic prediction accuracy in biobank data.” Dryad, 2022.","ista":"Orliac E, Trejo Banos D, Ojavee S, Läll K, Mägi R, Visscher P, Robinson MR. 2022. Improving genome-wide association discovery and genomic prediction accuracy in biobank data, Dryad, <a href=\"https://doi.org/10.5061/DRYAD.GTHT76HMZ\">10.5061/DRYAD.GTHT76HMZ</a>."},"abstract":[{"lang":"eng","text":"Genetically informed, deep-phenotyped biobanks are an important research resource and it is imperative that the most powerful, versatile, and efficient analysis approaches are used. Here, we apply our recently developed Bayesian grouped mixture of regressions model (GMRM) in the UK and Estonian Biobanks and obtain the highest genomic prediction accuracy reported to date across 21 heritable traits. When compared to other approaches, GMRM accuracy was greater than annotation prediction models run in the LDAK or LDPred-funct software by 15% (SE 7%) and 14% (SE 2%), respectively, and was 18% (SE 3%) greater than a baseline BayesR model without single-nucleotide polymorphism (SNP) markers grouped into minor allele frequency–linkage disequilibrium (MAF-LD) annotation categories. For height, the prediction accuracy R 2 was 47% in a UK Biobank holdout sample, which was 76% of the estimated h SNP 2 . We then extend our GMRM prediction model to provide mixed-linear model association (MLMA) SNP marker estimates for genome-wide association (GWAS) discovery, which increased the independent loci detected to 16,162 in unrelated UK Biobank individuals, compared to 10,550 from BoltLMM and 10,095 from Regenie, a 62 and 65% increase, respectively. The average χ2 value of the leading markers increased by 15.24 (SE 0.41) for every 1% increase in prediction accuracy gained over a baseline BayesR model across the traits. Thus, we show that modeling genetic associations accounting for MAF and LD differences among SNP markers, and incorporating prior knowledge of genomic function, is important for both genomic prediction and discovery in large-scale individual-level studies."}],"tmp":{"name":"Creative Commons Public Domain Dedication (CC0 1.0)","legal_code_url":"https://creativecommons.org/publicdomain/zero/1.0/legalcode","image":"/images/cc_0.png","short":"CC0 (1.0)"},"corr_author":"1","department":[{"_id":"MaRo"}],"main_file_link":[{"url":"https://doi.org/10.5061/dryad.gtht76hmz","open_access":"1"}],"oa_version":"Published Version","license":"https://creativecommons.org/publicdomain/zero/1.0/","author":[{"last_name":"Orliac","full_name":"Orliac, Etienne","first_name":"Etienne"},{"full_name":"Trejo Banos, Daniel","first_name":"Daniel","last_name":"Trejo Banos"},{"last_name":"Ojavee","first_name":"Sven","full_name":"Ojavee, Sven"},{"full_name":"Läll, Kristi","first_name":"Kristi","last_name":"Läll"},{"full_name":"Mägi, Reedik","first_name":"Reedik","last_name":"Mägi"},{"last_name":"Visscher","full_name":"Visscher, Peter","first_name":"Peter"},{"first_name":"Matthew Richard","full_name":"Robinson, Matthew Richard","orcid":"0000-0001-8982-8813","last_name":"Robinson","id":"E5D42276-F5DA-11E9-8E24-6303E6697425"}],"month":"09","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","day":"02"},{"oa_version":"Published Version","language":[{"iso":"eng"}],"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","month":"01","scopus_import":"1","project":[{"grant_number":"PCEGP3_181181","_id":"9B8D11D6-BA93-11EA-9121-9846C619BF3A","name":"Improving estimation and prediction of common complex disease risk"}],"article_number":"26","article_processing_charge":"No","file_date_updated":"2022-01-31T13:16:05Z","doi":"10.1186/s13059-021-02596-5","citation":{"apa":"McCartney, D. L., Hillary, R. F., Conole, E. L. S., Banos, D. T., Gadd, D. A., Walker, R. M., … Marioni, R. E. (2022). Blood-based epigenome-wide analyses of cognitive abilities. <i>Genome Biology</i>. Springer Nature. <a href=\"https://doi.org/10.1186/s13059-021-02596-5\">https://doi.org/10.1186/s13059-021-02596-5</a>","mla":"McCartney, Daniel L., et al. “Blood-Based Epigenome-Wide Analyses of Cognitive Abilities.” <i>Genome Biology</i>, vol. 23, no. 1, 26, Springer Nature, 2022, doi:<a href=\"https://doi.org/10.1186/s13059-021-02596-5\">10.1186/s13059-021-02596-5</a>.","ista":"McCartney DL, Hillary RF, Conole ELS, Banos DT, Gadd DA, Walker RM, Nangle C, Flaig R, Campbell A, Murray AD, Maniega SM, Valdés-Hernández MDC, Harris MA, Bastin ME, Wardlaw JM, Harris SE, Porteous DJ, Tucker-Drob EM, McIntosh AM, Evans KL, Deary IJ, Cox SR, Robinson MR, Marioni RE. 2022. Blood-based epigenome-wide analyses of cognitive abilities. Genome Biology. 23(1), 26.","ieee":"D. L. McCartney <i>et al.</i>, “Blood-based epigenome-wide analyses of cognitive abilities,” <i>Genome Biology</i>, vol. 23, no. 1. Springer Nature, 2022.","ama":"McCartney DL, Hillary RF, Conole ELS, et al. Blood-based epigenome-wide analyses of cognitive abilities. <i>Genome Biology</i>. 2022;23(1). doi:<a href=\"https://doi.org/10.1186/s13059-021-02596-5\">10.1186/s13059-021-02596-5</a>","short":"D.L. McCartney, R.F. Hillary, E.L.S. Conole, D.T. Banos, D.A. Gadd, R.M. Walker, C. Nangle, R. Flaig, A. Campbell, A.D. Murray, S.M. Maniega, M.D.C. Valdés-Hernández, M.A. Harris, M.E. Bastin, J.M. Wardlaw, S.E. Harris, D.J. Porteous, E.M. Tucker-Drob, A.M. McIntosh, K.L. Evans, I.J. Deary, S.R. Cox, M.R. Robinson, R.E. Marioni, Genome Biology 23 (2022).","chicago":"McCartney, Daniel L., Robert F. Hillary, Eleanor L.S. Conole, Daniel Trejo Banos, Danni A. Gadd, Rosie M. Walker, Cliff Nangle, et al. “Blood-Based Epigenome-Wide Analyses of Cognitive Abilities.” <i>Genome Biology</i>. Springer Nature, 2022. <a href=\"https://doi.org/10.1186/s13059-021-02596-5\">https://doi.org/10.1186/s13059-021-02596-5</a>."},"abstract":[{"lang":"eng","text":"Background: Blood-based markers of cognitive functioning might provide an accessible way to track neurodegeneration years prior to clinical manifestation of cognitive impairment and dementia. Results: Using blood-based epigenome-wide analyses of general cognitive function, we show that individual differences in DNA methylation (DNAm) explain 35.0% of the variance in general cognitive function (g). A DNAm predictor explains ~4% of the variance, independently of a polygenic score, in two external cohorts. It also associates with circulating levels of neurology- and inflammation-related proteins, global brain imaging metrics, and regional cortical volumes. Conclusions: As sample sizes increase, the ability to assess cognitive function from DNAm data may be informative in settings where cognitive testing is unreliable or unavailable."}],"publication_identifier":{"issn":["1474-7596"],"eissn":["1474-760X"]},"corr_author":"1","has_accepted_license":"1","department":[{"_id":"MaRo"}],"publisher":"Springer Nature","_id":"10702","ddc":["570"],"date_updated":"2025-06-11T13:54:53Z","acknowledgement":"GS 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 GS samples was carried out by the Genetics Core Laboratory at the Edinburgh 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; Reference 104036/Z/14/Z). The DNA methylation data assayed for Generation Scotland was 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). LBC1936 MRI brain imaging was supported by Medical Research Council (MRC) grants [G0701120], [G1001245], [MR/M013111/1] and [MR/R024065/1]. Magnetic resonance image acquisition and analyses were conducted at the Brain Research Imaging Centre, Neuroimaging Sciences, University of Edinburgh (www.bric.ed.ac.uk) which is part of SINAPSE (Scottish Imaging Network: A Platform for Scientific Excellence) collaboration (www.sinapse.ac.uk) funded by the Scottish Funding Council and the Chief Scientist Office. This work was supported by the European Union Horizon 2020 (PHC.03.15, project No 666881), SVDs@Target, the Fondation Leducq Transatlantic Network of Excellence for the Study of Perivascular Spaces in Small Vessel Disease [ref no. 16 CVD 05]. We thank the LBC1936 participants and team members who contributed to these studies. The LBC1936 is supported by Age UK (Disconnected Mind project, which supports S.E.H.), the Medical Research Council (G0701120, G1001245, MR/M013111/1, MR/R024065/1) 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 Biotechnology and Biological Sciences Research Council (BB/F019394/1). Proteomic analyses in LBC1936 were supported by the Age UK grant and NIH Grants R01AG054628 and R01AG05462802S1. M.V.H. is funded by the Row Fogo Charitable Trust (Grant no. BROD.FID3668413). J.M.W is supported by the UK Dementia Research Institute which receives its funding from DRI Ltd, funded by the UK Medical Research Council, Alzheimers Society and Alzheimers Research UK. R.F.H., E.L.S.C and D.A.G. are supported by funding from the Wellcome Trust 4 year PhD in Translational Neuroscience: training the next generation of basic neuroscientists to embrace clinical research [108890/Z/15/Z]. E.M.T.D. was supported by the National Institutes of Health (NIH) grants R01AG054628, R01MH120219, R01HD083613, P2CHD042849 and P30AG066614. S.R.C. was also supported by a National Institutes of Health (NIH) research grant R01AG054628 and is supported by a Sir Henry Dale Fellowship jointly funded by the Wellcome Trust and the Royal Society (Grant Number 221890/Z/20/Z). D.L.Mc.C. and R.E.M. are supported by Alzheimers Research UK major project grant ARUK/PG2017B/10. R.E.M. is supported by Alzheimer’s Society major project grant AS-PG-19b-010. This research was funded in whole, or in part, by Wellcome [104036/Z/14/Z and 108890/Z/15/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.","external_id":{"isi":["000744358300002"],"pmid":["35039062"]},"title":"Blood-based epigenome-wide analyses of cognitive abilities","author":[{"first_name":"Daniel L.","full_name":"McCartney, Daniel L.","last_name":"McCartney"},{"last_name":"Hillary","full_name":"Hillary, Robert F.","first_name":"Robert F."},{"last_name":"Conole","full_name":"Conole, Eleanor L.S.","first_name":"Eleanor L.S."},{"last_name":"Banos","first_name":"Daniel Trejo","full_name":"Banos, Daniel Trejo"},{"first_name":"Danni A.","full_name":"Gadd, Danni A.","last_name":"Gadd"},{"last_name":"Walker","full_name":"Walker, Rosie M.","first_name":"Rosie M."},{"full_name":"Nangle, Cliff","first_name":"Cliff","last_name":"Nangle"},{"last_name":"Flaig","full_name":"Flaig, Robin","first_name":"Robin"},{"full_name":"Campbell, Archie","first_name":"Archie","last_name":"Campbell"},{"first_name":"Alison D.","full_name":"Murray, Alison D.","last_name":"Murray"},{"first_name":"Susana Muñoz","full_name":"Maniega, Susana Muñoz","last_name":"Maniega"},{"last_name":"Valdés-Hernández","first_name":"María Del C.","full_name":"Valdés-Hernández, María Del C."},{"last_name":"Harris","full_name":"Harris, Mathew A.","first_name":"Mathew A."},{"last_name":"Bastin","full_name":"Bastin, Mark E.","first_name":"Mark E."},{"last_name":"Wardlaw","first_name":"Joanna M.","full_name":"Wardlaw, Joanna M."},{"first_name":"Sarah E.","full_name":"Harris, Sarah E.","last_name":"Harris"},{"last_name":"Porteous","first_name":"David J.","full_name":"Porteous, David J."},{"last_name":"Tucker-Drob","first_name":"Elliot M.","full_name":"Tucker-Drob, Elliot M."},{"full_name":"McIntosh, Andrew M.","first_name":"Andrew M.","last_name":"McIntosh"},{"last_name":"Evans","first_name":"Kathryn L.","full_name":"Evans, Kathryn L."},{"last_name":"Deary","full_name":"Deary, Ian J.","first_name":"Ian J."},{"first_name":"Simon R.","full_name":"Cox, Simon R.","last_name":"Cox"},{"last_name":"Robinson","orcid":"0000-0001-8982-8813","id":"E5D42276-F5DA-11E9-8E24-6303E6697425","first_name":"Matthew Richard","full_name":"Robinson, Matthew Richard"},{"last_name":"Marioni","first_name":"Riccardo E.","full_name":"Marioni, Riccardo E."}],"isi":1,"day":"17","article_type":"original","pmid":1,"quality_controlled":"1","file":[{"success":1,"content_type":"application/pdf","file_id":"10708","creator":"cchlebak","file_size":1540606,"file_name":"2022_GenomeBio_McCartney.pdf","access_level":"open_access","date_created":"2022-01-31T13:16:05Z","checksum":"34f10bb2b0594189dcac24d13b691d52","date_updated":"2022-01-31T13:16:05Z","relation":"main_file"}],"volume":23,"publication_status":"published","fulldoi":"https://doi.org/10.1186/s13059-021-02596-5","publication":"Genome Biology","tmp":{"image":"/images/cc_by.png","short":"CC BY (4.0)","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode"},"related_material":{"link":[{"relation":"earlier_version","url":"https://doi.org/10.1101/2021.05.24.21257698"}],"record":[{"relation":"research_data","status":"public","id":"13072"}]},"intvolume":"        23","year":"2022","status":"public","issue":"1","type":"journal_article","date_created":"2022-01-30T23:01:33Z","oa":1,"date_published":"2022-01-17T00:00:00Z"},{"volume":14,"quality_controlled":"1","file":[{"content_type":"application/pdf","file_id":"17356","success":1,"file_size":975181,"creator":"dernst","access_level":"open_access","date_created":"2024-07-31T11:27:29Z","file_name":"2023_AlzheimersDementia_Hillary.pdf","checksum":"49c8597b588ef1c63897703a32b7967b","relation":"main_file","date_updated":"2024-07-31T11:27:29Z"}],"article_type":"original","pmid":1,"day":"20","author":[{"first_name":"Robert F.","full_name":"Hillary, Robert F.","last_name":"Hillary"},{"last_name":"Gadd","full_name":"Gadd, Danni A.","first_name":"Danni A."},{"last_name":"McCartney","first_name":"Daniel L.","full_name":"McCartney, Daniel L."},{"first_name":"Liu","full_name":"Shi, Liu","last_name":"Shi"},{"last_name":"Campbell","full_name":"Campbell, Archie","first_name":"Archie"},{"first_name":"Rosie M.","full_name":"Walker, Rosie M.","last_name":"Walker"},{"last_name":"Ritchie","full_name":"Ritchie, Craig W.","first_name":"Craig W."},{"last_name":"Deary","full_name":"Deary, Ian J.","first_name":"Ian J."},{"last_name":"Evans","first_name":"Kathryn L.","full_name":"Evans, Kathryn L."},{"first_name":"Alejo J.","full_name":"Nevado‐Holgado, Alejo J.","last_name":"Nevado‐Holgado"},{"last_name":"Hayward","first_name":"Caroline","full_name":"Hayward, Caroline"},{"last_name":"Porteous","full_name":"Porteous, David J.","first_name":"David J."},{"last_name":"McIntosh","full_name":"McIntosh, Andrew M.","first_name":"Andrew M."},{"last_name":"Lovestone","full_name":"Lovestone, Simon","first_name":"Simon"},{"full_name":"Robinson, Matthew Richard","first_name":"Matthew Richard","id":"E5D42276-F5DA-11E9-8E24-6303E6697425","last_name":"Robinson","orcid":"0000-0001-8982-8813"},{"full_name":"Marioni, Riccardo E.","first_name":"Riccardo E.","last_name":"Marioni"}],"das_tickbox":"1","oa":1,"date_published":"2022-04-20T00:00:00Z","date_created":"2024-05-29T06:13:25Z","issue":"1","type":"journal_article","intvolume":"        14","status":"public","year":"2022","publication":"Alzheimer's & Dementia","tmp":{"image":"/images/cc_by.png","short":"CC BY (4.0)","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode"},"publication_status":"published","fulldoi":"https://doi.org/10.1002/dad2.12280","article_number":"e12280","scopus_import":"1","month":"04","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","language":[{"iso":"eng"}],"oa_version":"Published Version","title":"Genome‐ and epigenome‐wide studies of plasma protein biomarkers for Alzheimer's disease implicate TBCA and TREM2 in disease risk","acknowledgement":"This research was funded in whole, or in part, by Wellcome [108890/Z/15/Z, 104036/Z/14/Z]. For the purpose of open access, the author has applied a CC BY public copyright license to any Author Accepted Manuscript version arising from this submission. The authors are grateful to the families who took part in this study, the general practitioners, and the Scottish School of Primary Care for their help in recruiting them and the wider Generation Scotland team. 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 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 (MRC) UK and the Wellcome Trust (Wellcome Trust Strategic Award “STratifying Resilience and Depression Longitudinally” ([STRADL] Reference [104036/Z/14/Z]). Andrew M. McIntosh is supported by Wellcome [104036/Z/14/Z, 216767/Z/19/Z, 220857/Z/20/Z], United Kingdom Research and Innovation (UKRI) MRC [MC_PC_17209, MR/S035818/1] and the European Union H2020 [SEP-210574971]. Ian J. Deary received support from Age UK, Wellcome, and the Medical Research Council. David J. Porteous is supported by Wellcome as prinicpal investigator (PI), and MRC and National Institute for Health Research (NIHR) grants as co-PI, made to the University of Edinburgh. Robert F. Hillary and Danni A. Gadd are supported by funding from the Wellcome 4-year PhD in Translational Neuroscience—training the next generation of basic neuroscientists to embrace clinical research [108890/Z/15/Z]. Daniel L. McCartney and Riccardo E. Marioni are supported by Alzheimer's Research UK major project grant ARUK-PG2017B-10. Riccardo E. Marioni is supported by Alzheimer's Society major project grant AS-PG-19b-010. Proteomic analyses in STRADL were supported by Dementias Platform UK (DPUK). DPUK funded this work through core grant support from the Medical Research Council [MR/L023784/2]. Kathryn L. Evans was supported by a grant from Alzheimer's Research UK, paid to the University of Edinburgh. Alejo J. Nevado-Holgado was funded by a Horizon 2020 Virtual Brain Cloud project (H2020-SC1-DTH-2018-1), in addition to funding from the MRC, UK Rosetrees, and King Abdullah University of Science and Technology, Saudi Arabia. Caroline Hayward is supported by an MRC University Unit Programme Grant MC_UU_00007/10 (QTL in Health and Disease). Liu Shi is funded by DPUK through MRC [MR/L023784/2] and the UK Medical Research Council Award to the University of Oxford [MC_PC_17215]. Liu Shi received support from the NIHR Biomedical Research Centre at Oxford Health NHS Foundation Trust. Matthew R. Robinson is funded by a Swiss National Science Foundation Eccellenza Grant [PCEGP3-181181].","external_id":{"pmid":["35475137"]},"_id":"17076","publisher":"Wiley","ddc":["570"],"date_updated":"2026-07-08T05:51:18Z","has_accepted_license":"1","department":[{"_id":"MaRo"}],"doi":"10.1002/dad2.12280","file_date_updated":"2024-07-31T11:27:29Z","citation":{"chicago":"Hillary, Robert F., Danni A. Gadd, Daniel L. McCartney, Liu Shi, Archie Campbell, Rosie M. Walker, Craig W. Ritchie, et al. “Genome‐ and Epigenome‐wide Studies of Plasma Protein Biomarkers for Alzheimer’s Disease Implicate TBCA and TREM2 in Disease Risk.” <i>Alzheimer’s &#38; Dementia</i>. Wiley, 2022. <a href=\"https://doi.org/10.1002/dad2.12280\">https://doi.org/10.1002/dad2.12280</a>.","short":"R.F. Hillary, D.A. Gadd, D.L. McCartney, L. Shi, A. Campbell, R.M. Walker, C.W. Ritchie, I.J. Deary, K.L. Evans, A.J. Nevado‐Holgado, C. Hayward, D.J. Porteous, A.M. McIntosh, S. Lovestone, M.R. Robinson, R.E. Marioni, Alzheimer’s &#38; Dementia 14 (2022).","ama":"Hillary RF, Gadd DA, McCartney DL, et al. Genome‐ and epigenome‐wide studies of plasma protein biomarkers for Alzheimer’s disease implicate TBCA and TREM2 in disease risk. <i>Alzheimer’s &#38; Dementia</i>. 2022;14(1). doi:<a href=\"https://doi.org/10.1002/dad2.12280\">10.1002/dad2.12280</a>","ieee":"R. F. Hillary <i>et al.</i>, “Genome‐ and epigenome‐wide studies of plasma protein biomarkers for Alzheimer’s disease implicate TBCA and TREM2 in disease risk,” <i>Alzheimer’s &#38; Dementia</i>, vol. 14, no. 1. Wiley, 2022.","ista":"Hillary RF, Gadd DA, McCartney DL, Shi L, Campbell A, Walker RM, Ritchie CW, Deary IJ, Evans KL, Nevado‐Holgado AJ, Hayward C, Porteous DJ, McIntosh AM, Lovestone S, Robinson MR, Marioni RE. 2022. Genome‐ and epigenome‐wide studies of plasma protein biomarkers for Alzheimer’s disease implicate TBCA and TREM2 in disease risk. Alzheimer’s &#38; Dementia. 14(1), e12280.","mla":"Hillary, Robert F., et al. “Genome‐ and Epigenome‐wide Studies of Plasma Protein Biomarkers for Alzheimer’s Disease Implicate TBCA and TREM2 in Disease Risk.” <i>Alzheimer’s &#38; Dementia</i>, vol. 14, no. 1, e12280, Wiley, 2022, doi:<a href=\"https://doi.org/10.1002/dad2.12280\">10.1002/dad2.12280</a>.","apa":"Hillary, R. F., Gadd, D. A., McCartney, D. L., Shi, L., Campbell, A., Walker, R. M., … Marioni, R. E. (2022). Genome‐ and epigenome‐wide studies of plasma protein biomarkers for Alzheimer’s disease implicate TBCA and TREM2 in disease risk. <i>Alzheimer’s &#38; Dementia</i>. Wiley. <a href=\"https://doi.org/10.1002/dad2.12280\">https://doi.org/10.1002/dad2.12280</a>"},"abstract":[{"text":"Introduction: The levels of many blood proteins are associated with Alzheimer's disease (AD) or its pathological hallmarks. Elucidating the molecular factors that control circulating levels of these proteins may help to identify proteins associated with disease risk mechanisms.\r\n\r\nMethods: Genome-wide and epigenome-wide studies (nindividuals ≤1064) were performed on plasma levels of 282 AD-associated proteins, identified by a structured literature review. Bayesian penalized regression estimated contributions of genetic and epigenetic variation toward inter-individual differences in plasma protein levels. Mendelian randomization (MR) and co-localization tested associations between proteins and disease-related phenotypes.\r\n\r\nResults: Sixty-four independent genetic and 26 epigenetic loci were associated with 45 proteins. Novel findings included an association between plasma triggering receptor expressed on myeloid cells 2 (TREM2) levels and a polymorphism and cytosine-phosphate-guanine (CpG) site within the MS4A4A locus. Higher plasma tubulin-specific chaperone A (TBCA) and TREM2 levels were significantly associated with lower AD risk.\r\n\r\nDiscussion: Our data inform the regulation of biomarker levels and their relationships with AD.","lang":"eng"}],"publication_identifier":{"eissn":["2352-8729"]},"article_processing_charge":"Yes"},{"_id":"13063","related_material":{"link":[{"url":"https://github.com/medical-genomics-group/gmrm","relation":"software"}],"record":[{"relation":"used_in_publication","id":"8429","status":"public"}]},"publisher":"Dryad","year":"2021","status":"public","date_updated":"2025-06-12T06:54:51Z","ddc":["570"],"type":"research_data_reference","date_created":"2023-05-23T16:20:16Z","oa":1,"title":"Probabilistic inference of the genetic architecture of functional enrichment of complex traits","date_published":"2021-11-04T00:00:00Z","article_processing_charge":"No","fulldoi":"https://doi.org/10.5061/dryad.sqv9s4n51","doi":"10.5061/dryad.sqv9s4n51","citation":{"apa":"Robinson, M. R. (2021). Probabilistic inference of the genetic architecture of functional enrichment of complex traits. Dryad. <a href=\"https://doi.org/10.5061/dryad.sqv9s4n51\">https://doi.org/10.5061/dryad.sqv9s4n51</a>","mla":"Robinson, Matthew Richard. <i>Probabilistic Inference of the Genetic Architecture of Functional Enrichment of Complex Traits</i>. Dryad, 2021, doi:<a href=\"https://doi.org/10.5061/dryad.sqv9s4n51\">10.5061/dryad.sqv9s4n51</a>.","ama":"Robinson MR. Probabilistic inference of the genetic architecture of functional enrichment of complex traits. 2021. doi:<a href=\"https://doi.org/10.5061/dryad.sqv9s4n51\">10.5061/dryad.sqv9s4n51</a>","chicago":"Robinson, Matthew Richard. “Probabilistic Inference of the Genetic Architecture of Functional Enrichment of Complex Traits.” Dryad, 2021. <a href=\"https://doi.org/10.5061/dryad.sqv9s4n51\">https://doi.org/10.5061/dryad.sqv9s4n51</a>.","short":"M.R. Robinson, (2021).","ista":"Robinson MR. 2021. Probabilistic inference of the genetic architecture of functional enrichment of complex traits, Dryad, <a href=\"https://doi.org/10.5061/dryad.sqv9s4n51\">10.5061/dryad.sqv9s4n51</a>.","ieee":"M. R. Robinson, “Probabilistic inference of the genetic architecture of functional enrichment of complex traits.” Dryad, 2021."},"tmp":{"name":"Creative Commons Public Domain Dedication (CC0 1.0)","legal_code_url":"https://creativecommons.org/publicdomain/zero/1.0/legalcode","image":"/images/cc_0.png","short":"CC0 (1.0)"},"abstract":[{"text":"We develop a Bayesian model (BayesRR-RC) that provides robust SNP-heritability estimation, an alternative to marker discovery, and accurate genomic prediction, taking 22 seconds per iteration to estimate 8.4 million SNP-effects and 78 SNP-heritability parameters in the UK Biobank. We find that only $\\leq$ 10\\% of the genetic variation captured for height, body mass index, cardiovascular disease, and type 2 diabetes is attributable to proximal regulatory regions within 10kb upstream of genes, while 12-25% is attributed to coding regions, 32-44% to introns, and 22-28% to distal 10-500kb upstream regions. Up to 24% of all cis and coding regions of each chromosome are associated with each trait, with over 3,100 independent exonic and intronic regions and over 5,400 independent regulatory regions having &gt;95% probability of contributing &gt;0.001% to the genetic variance of these four traits. Our open-source software (GMRM) provides a scalable alternative to current approaches for biobank data.","lang":"eng"}],"corr_author":"1","department":[{"_id":"MaRo"}],"main_file_link":[{"url":"https://doi.org/10.5061/dryad.sqv9s4n51","open_access":"1"}],"oa_version":"Published Version","author":[{"orcid":"0000-0001-8982-8813","id":"E5D42276-F5DA-11E9-8E24-6303E6697425","last_name":"Robinson","full_name":"Robinson, Matthew Richard","first_name":"Matthew Richard"}],"month":"11","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","day":"04"}]