[{"article_number":"101277","year":"2026","publisher":"Elsevier","project":[{"name":"Improving estimation and prediction of common complex disease risk","grant_number":"PCEGP3_181181","_id":"9B8D11D6-BA93-11EA-9121-9846C619BF3A"}],"article_processing_charge":"Yes","pmid":1,"date_published":"2026-06-09T00:00:00Z","author":[{"last_name":"Krätschmer","id":"30d4014e-7753-11eb-b44b-db6d61112e73","full_name":"Krätschmer, Ilse","first_name":"Ilse","orcid":"0000-0002-5636-9259"},{"first_name":"Laura","full_name":"Hegemann, Laura","last_name":"Hegemann"},{"last_name":"Hofmeister","full_name":"Hofmeister, Robin J.","first_name":"Robin J."},{"first_name":"Elizabeth C.","full_name":"Corfield, Elizabeth C.","last_name":"Corfield"},{"full_name":"Mahmoudi, Mahdi","last_name":"Mahmoudi","first_name":"Mahdi"},{"first_name":"Olivier","last_name":"Delaneau","full_name":"Delaneau, Olivier"},{"first_name":"Ole A.","last_name":"Andreassen","full_name":"Andreassen, Ole A."},{"first_name":"Archie","full_name":"Campbell, Archie","last_name":"Campbell"},{"first_name":"Caroline","last_name":"Hayward","full_name":"Hayward, Caroline"},{"last_name":"Marioni","full_name":"Marioni, Riccardo E.","first_name":"Riccardo E."},{"full_name":"Ystrom, Eivind","last_name":"Ystrom","first_name":"Eivind"},{"first_name":"Alexandra","last_name":"Havdahl","full_name":"Havdahl, Alexandra"},{"last_name":"Robinson","id":"E5D42276-F5DA-11E9-8E24-6303E6697425","full_name":"Robinson, Matthew Richard","first_name":"Matthew Richard","orcid":"0000-0001-8982-8813"}],"_id":"21987","external_id":{"pmid":["40909755"]},"scopus_import":"1","acknowledgement":"We thank Zoltan Kutalik, Peter Visscher, and members of the Robinson group at ISTA for their comments, which improved this manuscript. This work was funded by an SNSF Eccellenza Grant to M.R.R. (PCEGP3-181181) and by core funding from the Institute of Science and Technology Austria.\r\nThe Norwegian Mother, Father, and Child Cohort Study is supported by the Norwegian Ministry of Health and Care Services and the Ministry of Education and Research. We are grateful to all the participating families in Norway who take part in this on-going cohort study. We thank the Norwegian Institute of Public Health (NIPH) for generating high-quality genomic data. The research is part of the HARVEST collaboration, supported by the Research Council of Norway (#229624). We also thank the NORMENT Center for providing genotype data, funded by the Research Council of Norway (#223273), South East Norway Health Authorities, and Stiftelsen Kristian Gerhard Jebsen, and in collaboration with deCODE Genetics. We further thank the Center for Diabetes Research, the University of Bergen for providing genotype data funded by the ERC AdG project SELECTionPREDISPOSED, Stiftelsen Kristian Gerhard Jebsen, Trond Mohn Foundation, the Research Council of Norway, the Novo Nordisk Foundation, the University of Bergen, and the Western Norway Health Authorities. The MoBa work was performed on the TSD (Tjeneste for Sensitive Data) facilities, owned by the University of Oslo, operated and developed by the TSD service group at the University of Oslo, IT Department (USIT, tsd-drift@usit.uio.no). E.Y. is supported by the European Union (grant numbers 101045526 and 101073237) and the Research Council of Norway (grant numbers 336078, 288083, and 331640).\r\nWe would like to acknowledge the participants and investigators of the Generation Scotland Cohort study. Generation Scotland received core support from the Chief Scientist Office of the Scottish Government Health Directorates (CZD/16/6) and the Scottish Funding Council (HR03006). Genotyping and methylation typing of the GS:SFHS samples was carried out by the Genetics Core Laboratory at the Wellcome Trust Clinical Research Facility, Edinburgh, Scotland and was funded by the Medical Research Council UK and the Wellcome Trust (Wellcome Trust Strategic Award “STratifying Resilience and Depression Longitudinally” [STRADL] ref. 104036/Z/14/Z).\r\nWe would like to thank and acknowledge the participants and investigators of the Estonian Biobank (EstBB) study. The research was conducted using the Estonian Center of Genomics/Roadmap II funded by the Estonian Research Council (project number TT17).\r\nNorwegian analyses were performed on resources provided by Sigma2 - the National Infrastructure for High-Performance Computing and Data Storage in Norway. Estonian Data analysis was carried out in the High-Performance Computing Center cloud provided by University of Tartu. Analysis of the Generation Scotland data and the summary statistics obtained from the other analyses was conducted at IST Austria and is supported by the Scientific Service Units (SSU) of IST Austria through resources provided by Scientific Computing (SciComp).","citation":{"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. Separating direct, indirect, and parent-of-origin genetic effects in the human population. Cell Genomics., 101277.","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 (n.d.).","mla":"Krätschmer, Ilse, et al. “Separating Direct, Indirect, and Parent-of-Origin Genetic Effects in the Human Population.” <i>Cell Genomics</i>, 101277, Elsevier, doi:<a href=\"https://doi.org/10.1016/j.xgen.2026.101277\">10.1016/j.xgen.2026.101277</a>.","chicago":"Krätschmer, Ilse, Laura Hegemann, Robin J. Hofmeister, Elizabeth C. Corfield, Mahdi Mahmoudi, Olivier Delaneau, Ole A. Andreassen, et al. “Separating Direct, Indirect, and Parent-of-Origin Genetic Effects in the Human Population.” <i>Cell Genomics</i>. Elsevier, n.d. <a href=\"https://doi.org/10.1016/j.xgen.2026.101277\">https://doi.org/10.1016/j.xgen.2026.101277</a>.","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>. 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. (n.d.). Separating direct, indirect, and parent-of-origin genetic effects in the human population. <i>Cell Genomics</i>. Elsevier. <a href=\"https://doi.org/10.1016/j.xgen.2026.101277\">https://doi.org/10.1016/j.xgen.2026.101277</a>","ieee":"I. Krätschmer <i>et al.</i>, “Separating direct, indirect, and parent-of-origin genetic effects in the human population,” <i>Cell Genomics</i>. Elsevier."},"main_file_link":[{"open_access":"1","url":"https://doi.org/10.1016/j.xgen.2026.101277"}],"status":"public","oa_version":"Published Version","quality_controlled":"1","language":[{"iso":"eng"}],"acknowledged_ssus":[{"_id":"ScienComp"}],"day":"09","doi":"10.1016/j.xgen.2026.101277","department":[{"_id":"MaRo"}],"publication_status":"inpress","corr_author":"1","month":"06","date_updated":"2026-06-19T07:00:47Z","type":"journal_article","DOAJ_listed":"1","publication_identifier":{"eissn":["2666-979X"]},"date_created":"2026-06-10T07:39:08Z","publication":"Cell Genomics","abstract":[{"text":"We introduce JODIE, a genetic joint modeling approach that estimates how DNA loci influence human traits by partitioning genetic effects into four components: direct effects (from a child’s alleles), indirect maternal and paternal effects (from parents’ alleles), and parent-of-origin (PofO) effects (dependent on parental transmission of alleles), while uniquely accounting for assortative mating. We analyze 30,000 child-mother-father trios from the Estonian Biobank and the Norwegian Mother, Father, and Child Cohort, focusing on height, body mass index, and childhood educational test scores. We find direct effects to be the largest contributor to trait variation, but combined, indirect parental and PofO effects are similarly substantial. We support our results by within-family genome-wide association testing and identify 276 independently associated DNA regions with a complex interplay between direct, indirect, and PofO effects. By joint modeling, we show that direct, indirect, and PofO effects collectively shape human phenotypic variation across loci genome-wide.","lang":"eng"}],"oa":1,"OA_type":"gold","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","title":"Separating direct, indirect, and parent-of-origin genetic effects in the human population","OA_place":"publisher","article_type":"original"},{"project":[{"name":"Prix Lopez-Loretta 2019 - Marco Mondelli","_id":"059876FA-7A3F-11EA-A408-12923DDC885E"},{"_id":"911e6d1f-16d5-11f0-9cad-c5c68c6a1cdf","name":"Inference in High Dimensions: Light-speed Algorithms and Information Limits","grant_number":"101161364"},{"_id":"9B8D11D6-BA93-11EA-9121-9846C619BF3A","grant_number":"PCEGP3_181181","name":"Improving estimation and prediction of common complex disease risk"}],"publisher":"Elsevier","ddc":["000","570"],"year":"2026","article_number":"101162","date_published":"2026-02-18T00:00:00Z","article_processing_charge":"Yes","_id":"21488","author":[{"full_name":"Depope, Al","id":"0b77531d-dbcd-11ea-9d1d-a8eee0bf3830","last_name":"Depope","first_name":"Al"},{"first_name":"Jakub","full_name":"Bajzik, Jakub","id":"b995e25b-8c4b-11ed-a6d8-f71b7bcd6122","last_name":"Bajzik"},{"first_name":"Marco","orcid":"0000-0002-3242-7020","last_name":"Mondelli","id":"27EB676C-8706-11E9-9510-7717E6697425","full_name":"Mondelli, Marco"},{"orcid":"0000-0001-8982-8813","first_name":"Matthew Richard","last_name":"Robinson","full_name":"Robinson, Matthew Richard","id":"E5D42276-F5DA-11E9-8E24-6303E6697425"}],"main_file_link":[{"open_access":"1","url":"https://doi.org/10.1016/j.xgen.2026.101162"}],"status":"public","tmp":{"name":"Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)","short":"CC BY-NC-ND (4.0)","image":"/images/cc_by_nc_nd.png","legal_code_url":"https://creativecommons.org/licenses/by-nc-nd/4.0/legalcode"},"citation":{"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>. Elsevier, 2026.","apa":"Depope, A., Bajzik, J., Mondelli, M., &#38; Robinson, M. R. (2026). Joint modeling of whole-genome sequencing data for human height via approximate message passing. <i>Cell Genomics</i>. Elsevier. <a href=\"https://doi.org/10.1016/j.xgen.2026.101162\">https://doi.org/10.1016/j.xgen.2026.101162</a>","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. doi:<a href=\"https://doi.org/10.1016/j.xgen.2026.101162\">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>. Elsevier, 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 (2026).","mla":"Depope, Al, et al. “Joint Modeling of Whole-Genome Sequencing Data for Human Height via Approximate Message Passing.” <i>Cell Genomics</i>, 101162, Elsevier, 2026, doi:<a href=\"https://doi.org/10.1016/j.xgen.2026.101162\">10.1016/j.xgen.2026.101162</a>.","ista":"Depope A, Bajzik J, Mondelli M, Robinson MR. 2026. Joint modeling of whole-genome sequencing data for human height via approximate message passing. Cell Genomics., 101162."},"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.","related_material":{"link":[{"description":"News on ISTA website","relation":"press_release","url":"https://ista.ac.at/en/news/big-data-and-human-height/"}]},"month":"02","corr_author":"1","department":[{"_id":"MaMo"},{"_id":"MaRo"}],"publication_status":"epub_ahead","day":"18","doi":"10.1016/j.xgen.2026.101162","language":[{"iso":"eng"}],"oa_version":"Published Version","quality_controlled":"1","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":"Cell Genomics","date_created":"2026-03-23T15:10:03Z","publication_identifier":{"eissn":["2666-979X"]},"DOAJ_listed":"1","type":"journal_article","date_updated":"2026-04-28T12:08:37Z","article_type":"original","OA_place":"publisher","user_id":"ba8df636-2132-11f1-aed0-ed93e2281fdd","title":"Joint modeling of whole-genome sequencing data for human height via approximate message passing","OA_type":"gold","oa":1,"has_accepted_license":"1"},{"author":[{"last_name":"Bernabeu","full_name":"Bernabeu, Elena","first_name":"Elena"},{"full_name":"Chybowska, Aleksandra D.","last_name":"Chybowska","first_name":"Aleksandra D."},{"last_name":"Kresovich","full_name":"Kresovich, Jacob K.","first_name":"Jacob K."},{"full_name":"Suderman, Matthew","last_name":"Suderman","first_name":"Matthew"},{"first_name":"Daniel L.","last_name":"Mccartney","full_name":"Mccartney, Daniel L."},{"last_name":"Hillary","full_name":"Hillary, Robert F.","first_name":"Robert F."},{"first_name":"Janie","full_name":"Corley, Janie","last_name":"Corley"},{"last_name":"Valdés-Hernández","full_name":"Valdés-Hernández, Maria Del C.","first_name":"Maria Del C."},{"first_name":"Susana Muñoz","full_name":"Maniega, Susana Muñoz","last_name":"Maniega"},{"last_name":"Bastin","full_name":"Bastin, Mark E.","first_name":"Mark E."},{"first_name":"Joanna M.","last_name":"Wardlaw","full_name":"Wardlaw, Joanna M."},{"last_name":"Xu","full_name":"Xu, Zongli","first_name":"Zongli"},{"first_name":"Dale P.","last_name":"Sandler","full_name":"Sandler, Dale P."},{"first_name":"Archie","last_name":"Campbell","full_name":"Campbell, Archie"},{"first_name":"Sarah E.","full_name":"Harris, Sarah E.","last_name":"Harris"},{"last_name":"Mcintosh","full_name":"Mcintosh, Andrew M.","first_name":"Andrew M."},{"first_name":"Jack A.","full_name":"Taylor, Jack A.","last_name":"Taylor"},{"first_name":"Paul","last_name":"Yousefi","full_name":"Yousefi, Paul"},{"first_name":"Simon R.","last_name":"Cox","full_name":"Cox, Simon R."},{"full_name":"Evans, Kathryn L.","last_name":"Evans","first_name":"Kathryn L."},{"last_name":"Robinson","full_name":"Robinson, Matthew Richard","id":"E5D42276-F5DA-11E9-8E24-6303E6697425","orcid":"0000-0001-8982-8813","first_name":"Matthew Richard"},{"full_name":"Vallejos, Catalina A.","last_name":"Vallejos","first_name":"Catalina A."},{"first_name":"Riccardo E.","last_name":"Marioni","full_name":"Marioni, Riccardo E."}],"isi":1,"external_id":{"isi":["001406495600001"],"pmid":["39863868"]},"_id":"19023","volume":17,"intvolume":"        17","article_processing_charge":"Yes","date_published":"2025-01-25T00:00:00Z","pmid":1,"year":"2025","article_number":"14","ddc":["570"],"project":[{"name":"Improving estimation and prediction of common complex disease risk","grant_number":"PCEGP3_181181","_id":"9B8D11D6-BA93-11EA-9121-9846C619BF3A"}],"publisher":"Springer Nature","has_accepted_license":"1","oa":1,"title":"Blood-based epigenome-wide association study and prediction of alcohol consumption","user_id":"317138e5-6ab7-11ef-aa6d-ffef3953e345","OA_type":"gold","article_type":"original","OA_place":"publisher","DOAJ_listed":"1","type":"journal_article","date_updated":"2025-09-30T10:31:08Z","file_date_updated":"2025-02-17T08:44:23Z","date_created":"2025-02-16T23:02:33Z","publication":"Clinical Epigenetics","publication_identifier":{"issn":["1868-7075"],"eissn":["1868-7083"]},"abstract":[{"text":"Alcohol consumption is an important risk factor for multiple diseases. It is typically assessed via self-report, which is open to measurement error through recall bias. Instead, molecular data such as blood-based DNA methylation (DNAm) could be used to derive a more objective measure of alcohol consumption by incorporating information from cytosine-phosphate-guanine (CpG) sites known to be linked to the trait. Here, we explore the epigenetic architecture of self-reported weekly units of alcohol consumption in the Generation Scotland study. We first create a blood-based epigenetic score (EpiScore) of alcohol consumption using elastic net penalized linear regression. We explore the effect of pre-filtering for CpG features ahead of elastic net, as well as differential patterns by sex and by units consumed in the last week relative to an average week. The final EpiScore was trained on 16,717 individuals and tested in four external cohorts: the Lothian Birth Cohorts (LBC) of 1921 and 1936, the Sister Study, and the Avon Longitudinal Study of Parents and Children (total N across studies > 10,000). The maximum Pearson correlation between the EpiScore and self-reported alcohol consumption within cohort ranged from 0.41 to 0.53. In LBC1936, higher EpiScore levels had significant associations with poorer global brain imaging metrics, whereas self-reported alcohol consumption did not. Finally, we identified two novel CpG loci via a Bayesian penalized regression epigenome-wide association study of alcohol consumption. Together, these findings show how DNAm can objectively characterize patterns of alcohol consumption that associate with brain health, unlike self-reported estimates.","lang":"eng"}],"oa_version":"Published Version","language":[{"iso":"eng"}],"quality_controlled":"1","publication_status":"published","department":[{"_id":"MaRo"}],"day":"25","doi":"10.1186/s13148-025-01818-y","month":"01","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.","scopus_import":"1","tmp":{"name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","short":"CC BY (4.0)","legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","image":"/images/cc_by.png"},"file":[{"content_type":"application/pdf","checksum":"c32511f2d09e6c164116793e784944b8","relation":"main_file","access_level":"open_access","success":1,"date_updated":"2025-02-17T08:44:23Z","creator":"dernst","file_size":1170930,"file_id":"19030","date_created":"2025-02-17T08:44:23Z","file_name":"2025_ClinicalEpigenetics_Bernabeu.pdf"}],"citation":{"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).","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>","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>","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."},"status":"public"},{"month":"04","corr_author":"1","publication_status":"published","department":[{"_id":"MaMo"},{"_id":"MaRo"}],"doi":"10.1109/ICASSP48485.2024.10447198","day":"19","acknowledged_ssus":[{"_id":"ScienComp"}],"language":[{"iso":"eng"}],"oa_version":"Submitted Version","quality_controlled":"1","main_file_link":[{"url":"https://openreview.net/forum?id=aQYCDxfZV0","open_access":"1"}],"status":"public","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.","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>","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>","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>.","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>.","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."},"scopus_import":"1","acknowledgement":"This work was supported by a Lopez-Loreta Prize to MM, an SNSF Eccellenza Grant to MRR (PCEGP3-181181), and core funding from ISTA. The authors thank Philip Schniter, Matthew Stephens and Pragya Sur for valuable suggestions on an early version of the work. The authors acknowledge the participants and investigators of the UK Biobank study. High-performance\r\ncomputing was supported by the Scientific Service Units (SSU) of IST Austria through resources provided by Scientific Computing (SciComp).","OA_place":"repository","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","title":"Inference of genetic effects via approximate message passing","OA_type":"green","conference":{"location":"Seoul, Korea","end_date":"2024-04-19","name":"ICASSP: International Conference on Acoustics, Speech and Signal Processing","start_date":"2024-04-14"},"oa":1,"abstract":[{"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.","lang":"eng"}],"publication":"2024 IEEE International Conference on Acoustics, Speech, and Signal Processing","date_created":"2024-06-16T22:01:07Z","publication_identifier":{"isbn":["9798350344851"],"issn":["1520-6149"]},"type":"conference","date_updated":"2025-11-05T07:21:31Z","date_published":"2024-04-19T00:00:00Z","article_processing_charge":"No","page":"13151-13155","project":[{"_id":"059876FA-7A3F-11EA-A408-12923DDC885E","name":"Prix Lopez-Loretta 2019 - Marco Mondelli"},{"grant_number":"PCEGP3_181181","name":"Improving estimation and prediction of common complex disease risk","_id":"9B8D11D6-BA93-11EA-9121-9846C619BF3A"}],"publisher":"IEEE","year":"2024","external_id":{"isi":["001396233806078"]},"_id":"17147","isi":1,"author":[{"first_name":"Al","last_name":"Depope","full_name":"Depope, Al","id":"0b77531d-dbcd-11ea-9d1d-a8eee0bf3830"},{"orcid":"0000-0002-3242-7020","first_name":"Marco","full_name":"Mondelli, Marco","id":"27EB676C-8706-11E9-9510-7717E6697425","last_name":"Mondelli"},{"full_name":"Robinson, Matthew Richard","id":"E5D42276-F5DA-11E9-8E24-6303E6697425","last_name":"Robinson","orcid":"0000-0001-8982-8813","first_name":"Matthew Richard"}]},{"OA_place":"publisher","user_id":"ba8df636-2132-11f1-aed0-ed93e2281fdd","title":"Algorithms for causal learning and comparative analysis for genomic data","oa":1,"has_accepted_license":"1","abstract":[{"lang":"eng","text":"This thesis consists of two pieces of work in the broader feld of computational biology,\r\nboth of which are methods for the analysis of large scale biological data, implemented in\r\nefcient software.\r\nChapter 2 introduces a statistical software for causal discovery and inference from observed\r\ngenetic marker and phenotypic trait data. We explore in simulation how well the method\r\ncan fne-map genetic efects, fnd the correct causal structure among tens of traits and\r\nmillions of genetic markers, and infer the causal efect size for the discovered causal\r\nrelations. We then apply the method to 8 million markers and 17 traits from the UK\r\nBiobank and show that many relationships found with other methods are likely due to\r\nthe efects of hidden confounders.\r\nChapter 3 describes how this method can be applied to longitudinal data. I show how one\r\ncan incorporate the background knowledge present in the known order of measurements to\r\nimprove the accuracy of the causal discovery process, and explore the method’s ability to\r\nidentify age specifc genetic efects, and how the error rates of this recovery are infuenced\r\nby missing data due to diferent censoring mechanisms.\r\nChapter 4 introduces a statistical software for the comparison of chromatin contact maps\r\nbased on the structural similarity index. We explore the robustness of the method to\r\nnoise and size diferences of the compared maps, show how it can measure evolutionary\r\nconservation of topological features by providing a similarity ranking of syntenic regions,\r\nand fnally how it can detect alterations in 3D genome structure due to genetic mutations\r\nin samples of medical relevance.\r\n"}],"supervisor":[{"id":"E5D42276-F5DA-11E9-8E24-6303E6697425","full_name":"Robinson, Matthew Richard","last_name":"Robinson","first_name":"Matthew Richard","orcid":"0000-0001-8982-8813"}],"date_created":"2024-12-10T13:49:15Z","publication_identifier":{"issn":["2663-337X"]},"file_date_updated":"2025-06-12T22:30:02Z","type":"dissertation","date_updated":"2026-04-07T13:23:06Z","related_material":{"record":[{"status":"public","id":"18648","relation":"part_of_dissertation"},{"status":"public","relation":"part_of_dissertation","id":"8707"}]},"month":"12","corr_author":"1","publication_status":"published","department":[{"_id":"GradSch"},{"_id":"MaRo"}],"day":"11","doi":"10.15479/at:ista:18642","language":[{"iso":"eng"}],"oa_version":"Published Version","status":"public","citation":{"apa":"Machnik, N. N. (2024). <i>Algorithms for causal learning and comparative analysis for genomic data</i>. Institute of Science and Technology Austria. <a href=\"https://doi.org/10.15479/at:ista:18642\">https://doi.org/10.15479/at:ista:18642</a>","ama":"Machnik NN. Algorithms for causal learning and comparative analysis for genomic data. 2024. doi:<a href=\"https://doi.org/10.15479/at:ista:18642\">10.15479/at:ista:18642</a>","ieee":"N. N. Machnik, “Algorithms for causal learning and comparative analysis for genomic data,” Institute of Science and Technology Austria, 2024.","ista":"Machnik NN. 2024. Algorithms for causal learning and comparative analysis for genomic data. Institute of Science and Technology Austria.","short":"N.N. Machnik, Algorithms for Causal Learning and Comparative Analysis for Genomic Data, Institute of Science and Technology Austria, 2024.","chicago":"Machnik, Nick N. “Algorithms for Causal Learning and Comparative Analysis for Genomic Data.” Institute of Science and Technology Austria, 2024. <a href=\"https://doi.org/10.15479/at:ista:18642\">https://doi.org/10.15479/at:ista:18642</a>.","mla":"Machnik, Nick N. <i>Algorithms for Causal Learning and Comparative Analysis for Genomic Data</i>. Institute of Science and Technology Austria, 2024, doi:<a href=\"https://doi.org/10.15479/at:ista:18642\">10.15479/at:ista:18642</a>."},"file":[{"file_size":12845009,"creator":"nmachnik","date_updated":"2025-06-12T22:30:02Z","embargo":"2025-06-12","file_name":"NickMachnikThesisFinal_pdfa_conv.pdf","date_created":"2024-12-11T11:59:54Z","file_id":"18649","relation":"main_file","checksum":"d45e4d170f9a70a1f69b44b99bd058e4","content_type":"application/pdf","access_level":"open_access"},{"creator":"nmachnik","file_size":14189810,"date_updated":"2025-06-12T22:30:02Z","file_name":"thesis.zip","date_created":"2024-12-11T11:59:34Z","file_id":"18650","checksum":"f88c9acc62002395ec4dcbdb5eea8b82","relation":"source_file","content_type":"application/zip","embargo_to":"open_access","access_level":"closed"}],"acknowledgement":"I would like to thank the Swiss National Science Foundation for funding parts of this work\r\nthrough the Eccellenza Grant \"Improving estimation and prediction of common complex\r\ndisease risk\" with grant number PCEGP3_181181.","alternative_title":["ISTA Thesis"],"_id":"18642","author":[{"first_name":"Nick N","orcid":"0000-0001-6617-9742","id":"3591A0AA-F248-11E8-B48F-1D18A9856A87","full_name":"Machnik, Nick N","last_name":"Machnik"}],"date_published":"2024-12-11T00:00:00Z","article_processing_charge":"No","page":"138","project":[{"_id":"9B8D11D6-BA93-11EA-9121-9846C619BF3A","grant_number":"PCEGP3_181181","name":"Improving estimation and prediction of common complex disease risk"}],"publisher":"Institute of Science and Technology Austria","ddc":["576"],"year":"2024","degree_awarded":"PhD"},{"author":[{"first_name":"Nick N","orcid":"0000-0001-6617-9742","id":"3591A0AA-F248-11E8-B48F-1D18A9856A87","full_name":"Machnik, Nick N","last_name":"Machnik"},{"first_name":"Seyed Mahdi","last_name":"Mahmoudi","id":"b9f6d5ef-7774-11eb-a47f-df2c75c02ee7","full_name":"Mahmoudi, Seyed Mahdi"},{"full_name":"Borczyk, Malgorzata","last_name":"Borczyk","first_name":"Malgorzata"},{"last_name":"Krätschmer","full_name":"Krätschmer, Ilse","id":"30d4014e-7753-11eb-b44b-db6d61112e73","orcid":"0000-0002-5636-9259","first_name":"Ilse"},{"first_name":"Markus J.","last_name":"Bauer","full_name":"Bauer, Markus J."},{"id":"E5D42276-F5DA-11E9-8E24-6303E6697425","full_name":"Robinson, Matthew Richard","last_name":"Robinson","first_name":"Matthew Richard","orcid":"0000-0001-8982-8813"}],"_id":"18648","year":"2024","project":[{"_id":"9B8D11D6-BA93-11EA-9121-9846C619BF3A","name":"Improving estimation and prediction of common complex disease risk","grant_number":"PCEGP3_181181"},{"grant_number":"590359","name":"Advanced statistical modelling to facilitate more accurate characterisation of disease phenotypes, improved genetic mapping, and effective therapeutic hypothesis generation","_id":"bd936e6f-d553-11ed-ba76-a82299f63e8c"}],"article_processing_charge":"No","date_published":"2024-08-10T00:00:00Z","type":"preprint","date_updated":"2026-06-24T22:30:25Z","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"}],"publication":"bioRxiv","date_created":"2024-12-11T10:42:59Z","oa":1,"OA_place":"repository","user_id":"8b945eb4-e2f2-11eb-945a-df72226e66a9","title":"Causal inference for multiple risk factors and diseases from genomics data","OA_type":"free access","acknowledgement":"We thank Zoltan Kutalik and members of the Robinson group \r\nat ISTA for their comments, which improved this manuscript. This work was funded \r\nby a research collaboration agreement between Boehringer Ingelheim and the research \r\ngroup of MRR at the Institute of Science and Technology Austria. Additional funding \r\nwas also provided by an SNSF Eccellenza Grant to MRR (PCEGP3-181181), and by \r\ncore funding from the Institute of Science and Technology Austria. We would like \r\nto acknowledge the participants and investigators of the UK Biobank study. High- \r\nperformance computing was supported by the Scientific Service Units (SSU) of IST \r\nAustria through resources provided by Scientific Computing (SciComp). ","main_file_link":[{"open_access":"1","url":"https://doi.org/10.1101/2023.12.06.570392"}],"status":"public","tmp":{"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","image":"/images/cc_by_nc.png","short":"CC BY-NC (4.0)"},"citation":{"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>.","short":"N.N. Machnik, S.M. Mahmoudi, M. Borczyk, I. Krätschmer, M.J. Bauer, M.R. Robinson, BioRxiv (2024).","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>.","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>.","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>","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."},"acknowledged_ssus":[{"_id":"ScienComp"}],"oa_version":"Preprint","language":[{"iso":"eng"}],"related_material":{"record":[{"relation":"dissertation_contains","id":"18642","status":"public"}]},"month":"08","corr_author":"1","department":[{"_id":"MaRo"}],"publication_status":"published","day":"10","doi":"10.1101/2023.12.06.570392"},{"volume":109,"intvolume":"       109","author":[{"last_name":"Ojavee","full_name":"Ojavee, Sven E.","first_name":"Sven E."},{"first_name":"Zoltan","last_name":"Kutalik","full_name":"Kutalik, Zoltan"},{"last_name":"Robinson","id":"E5D42276-F5DA-11E9-8E24-6303E6697425","full_name":"Robinson, Matthew Richard","first_name":"Matthew Richard","orcid":"0000-0001-8982-8813"}],"isi":1,"issue":"11","_id":"12142","external_id":{"isi":["000898683500006"],"pmid":["36265482"]},"year":"2022","ddc":["570"],"publisher":"Elsevier","project":[{"grant_number":"PCEGP3_181181","name":"Improving estimation and prediction of common complex disease risk","_id":"9B8D11D6-BA93-11EA-9121-9846C619BF3A"}],"page":"2009-2017","article_processing_charge":"Yes (via OA deal)","pmid":1,"date_published":"2022-11-03T00:00:00Z","date_updated":"2025-06-11T13:55:19Z","type":"journal_article","file_date_updated":"2023-01-24T09:23:01Z","publication_identifier":{"issn":["0002-9297"]},"publication":"The American Journal of Human Genetics","date_created":"2023-01-12T12:05:28Z","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."}],"has_accepted_license":"1","oa":1,"keyword":["Genetics (clinical)","Genetics"],"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","title":"Liability-scale heritability estimation for biobank studies of low-prevalence disease","article_type":"original","scopus_import":"1","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.","citation":{"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.","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>","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>","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>.","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>.","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."},"file":[{"checksum":"4cd7f12bfe21a8237bb095eedfa26361","relation":"main_file","content_type":"application/pdf","access_level":"open_access","success":1,"creator":"dernst","file_size":705195,"date_updated":"2023-01-24T09:23:01Z","file_name":"2022_AJHG_Ojavee.pdf","file_id":"12353","date_created":"2023-01-24T09:23:01Z"}],"tmp":{"name":"Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)","short":"CC BY-NC-ND (4.0)","image":"/images/cc_by_nc_nd.png","legal_code_url":"https://creativecommons.org/licenses/by-nc-nd/4.0/legalcode"},"status":"public","language":[{"iso":"eng"}],"quality_controlled":"1","oa_version":"Published Version","acknowledged_ssus":[{"_id":"ScienComp"}],"doi":"10.1016/j.ajhg.2022.09.011","day":"03","publication_status":"published","department":[{"_id":"MaRo"}],"corr_author":"1","month":"11"},{"issue":"1","_id":"10702","external_id":{"isi":["000744358300002"],"pmid":["35039062"]},"author":[{"full_name":"McCartney, Daniel L.","last_name":"McCartney","first_name":"Daniel L."},{"full_name":"Hillary, Robert F.","last_name":"Hillary","first_name":"Robert F."},{"first_name":"Eleanor L.S.","last_name":"Conole","full_name":"Conole, Eleanor L.S."},{"first_name":"Daniel Trejo","full_name":"Banos, Daniel Trejo","last_name":"Banos"},{"first_name":"Danni A.","full_name":"Gadd, Danni A.","last_name":"Gadd"},{"first_name":"Rosie M.","last_name":"Walker","full_name":"Walker, Rosie M."},{"last_name":"Nangle","full_name":"Nangle, Cliff","first_name":"Cliff"},{"full_name":"Flaig, Robin","last_name":"Flaig","first_name":"Robin"},{"first_name":"Archie","last_name":"Campbell","full_name":"Campbell, Archie"},{"last_name":"Murray","full_name":"Murray, Alison D.","first_name":"Alison D."},{"full_name":"Maniega, Susana Muñoz","last_name":"Maniega","first_name":"Susana Muñoz"},{"first_name":"María Del C.","full_name":"Valdés-Hernández, María Del C.","last_name":"Valdés-Hernández"},{"first_name":"Mathew A.","full_name":"Harris, Mathew A.","last_name":"Harris"},{"first_name":"Mark E.","last_name":"Bastin","full_name":"Bastin, Mark E."},{"last_name":"Wardlaw","full_name":"Wardlaw, Joanna M.","first_name":"Joanna M."},{"first_name":"Sarah E.","full_name":"Harris, Sarah E.","last_name":"Harris"},{"first_name":"David J.","last_name":"Porteous","full_name":"Porteous, David J."},{"first_name":"Elliot M.","last_name":"Tucker-Drob","full_name":"Tucker-Drob, Elliot M."},{"first_name":"Andrew M.","last_name":"McIntosh","full_name":"McIntosh, Andrew M."},{"first_name":"Kathryn L.","full_name":"Evans, Kathryn L.","last_name":"Evans"},{"first_name":"Ian J.","last_name":"Deary","full_name":"Deary, Ian J."},{"full_name":"Cox, Simon R.","last_name":"Cox","first_name":"Simon R."},{"id":"E5D42276-F5DA-11E9-8E24-6303E6697425","full_name":"Robinson, Matthew Richard","last_name":"Robinson","first_name":"Matthew Richard","orcid":"0000-0001-8982-8813"},{"first_name":"Riccardo E.","last_name":"Marioni","full_name":"Marioni, Riccardo E."}],"isi":1,"intvolume":"        23","volume":23,"pmid":1,"date_published":"2022-01-17T00:00:00Z","article_processing_charge":"No","project":[{"grant_number":"PCEGP3_181181","name":"Improving estimation and prediction of common complex disease risk","_id":"9B8D11D6-BA93-11EA-9121-9846C619BF3A"}],"publisher":"Springer Nature","ddc":["570"],"year":"2022","article_number":"26","article_type":"original","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","title":"Blood-based epigenome-wide analyses of cognitive abilities","oa":1,"has_accepted_license":"1","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":"Genome Biology","date_created":"2022-01-30T23:01:33Z","publication_identifier":{"eissn":["1474-760X"],"issn":["1474-7596"]},"file_date_updated":"2022-01-31T13:16:05Z","type":"journal_article","date_updated":"2025-06-11T13:54:53Z","related_material":{"link":[{"relation":"earlier_version","url":"https://doi.org/10.1101/2021.05.24.21257698"}],"record":[{"status":"public","id":"13072","relation":"research_data"}]},"month":"01","corr_author":"1","publication_status":"published","department":[{"_id":"MaRo"}],"day":"17","doi":"10.1186/s13059-021-02596-5","language":[{"iso":"eng"}],"oa_version":"Published Version","quality_controlled":"1","status":"public","tmp":{"name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","short":"CC BY (4.0)","legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","image":"/images/cc_by.png"},"file":[{"date_updated":"2022-01-31T13:16:05Z","file_size":1540606,"creator":"cchlebak","file_id":"10708","date_created":"2022-01-31T13:16:05Z","file_name":"2022_GenomeBio_McCartney.pdf","content_type":"application/pdf","relation":"main_file","checksum":"34f10bb2b0594189dcac24d13b691d52","success":1,"access_level":"open_access"}],"citation":{"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>.","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>.","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).","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.","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>","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>"},"scopus_import":"1","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."},{"scopus_import":"1","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 and the University of Lausanne; the work of KF was supported by the grant PUT1665 by the Estonian Research Council. We would like to thank Mike Goddard for comments which greatly improved the work, the participants of the cohort studies, and the Ecole Polytechnique Federal Lausanne (EPFL) SCITAS for their excellent compute resources, their generosity with their time and the kindness of their support.","tmp":{"name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","short":"CC BY (4.0)","legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","image":"/images/cc_by.png"},"file":[{"content_type":"application/pdf","relation":"main_file","checksum":"eca8b9ae713835c5b785211dd08d8a2e","success":1,"access_level":"open_access","date_updated":"2021-05-04T15:07:50Z","file_size":6474239,"creator":"kschuh","file_id":"9372","date_created":"2021-05-04T15:07:50Z","file_name":"2021_nature_communications_Ojavee.pdf"}],"citation":{"ieee":"S. E. Ojavee <i>et al.</i>, “Genomic architecture and prediction of censored time-to-event phenotypes with a Bayesian genome-wide analysis,” <i>Nature Communications</i>, vol. 12, no. 1. Nature Research, 2021.","apa":"Ojavee, S. E., Kousathanas, A., Trejo Banos, D., Orliac, E. J., Patxot, M., Lall, K., … Robinson, M. R. (2021). Genomic architecture and prediction of censored time-to-event phenotypes with a Bayesian genome-wide analysis. <i>Nature Communications</i>. Nature Research. <a href=\"https://doi.org/10.1038/s41467-021-22538-w\">https://doi.org/10.1038/s41467-021-22538-w</a>","ama":"Ojavee SE, Kousathanas A, Trejo Banos D, et al. Genomic architecture and prediction of censored time-to-event phenotypes with a Bayesian genome-wide analysis. <i>Nature Communications</i>. 2021;12(1). doi:<a href=\"https://doi.org/10.1038/s41467-021-22538-w\">10.1038/s41467-021-22538-w</a>","mla":"Ojavee, Sven E., et al. “Genomic Architecture and Prediction of Censored Time-to-Event Phenotypes with a Bayesian Genome-Wide Analysis.” <i>Nature Communications</i>, vol. 12, no. 1, 2337, Nature Research, 2021, doi:<a href=\"https://doi.org/10.1038/s41467-021-22538-w\">10.1038/s41467-021-22538-w</a>.","short":"S.E. Ojavee, A. Kousathanas, D. Trejo Banos, E.J. Orliac, M. Patxot, K. Lall, R. Magi, K. Fischer, Z. Kutalik, M.R. Robinson, Nature Communications 12 (2021).","chicago":"Ojavee, Sven E, Athanasios Kousathanas, Daniel Trejo Banos, Etienne J Orliac, Marion Patxot, Kristi Lall, Reedik Magi, Krista Fischer, Zoltan Kutalik, and Matthew Richard Robinson. “Genomic Architecture and Prediction of Censored Time-to-Event Phenotypes with a Bayesian Genome-Wide Analysis.” <i>Nature Communications</i>. Nature Research, 2021. <a href=\"https://doi.org/10.1038/s41467-021-22538-w\">https://doi.org/10.1038/s41467-021-22538-w</a>.","ista":"Ojavee SE, Kousathanas A, Trejo Banos D, Orliac EJ, Patxot M, Lall K, Magi R, Fischer K, Kutalik Z, Robinson MR. 2021. Genomic architecture and prediction of censored time-to-event phenotypes with a Bayesian genome-wide analysis. Nature Communications. 12(1), 2337."},"status":"public","oa_version":"Published Version","language":[{"iso":"eng"}],"quality_controlled":"1","department":[{"_id":"MaRo"}],"publication_status":"published","doi":"10.1038/s41467-021-22538-w","day":"20","related_material":{"link":[{"relation":"press_release","url":"https://ist.ac.at/en/news/predicting-the-onset-of-diseases/","description":"News on IST Homepage"}]},"month":"04","type":"journal_article","date_updated":"2026-04-03T09:31:17Z","file_date_updated":"2021-05-04T15:07:50Z","date_created":"2020-09-17T10:53:00Z","publication":"Nature Communications","publication_identifier":{"eissn":["2041-1723"]},"abstract":[{"lang":"eng","text":"While recent advancements in computation and modelling have improved the analysis of complex traits, our understanding of the genetic basis of the time at symptom onset remains limited. Here, we develop a Bayesian approach (BayesW) that provides probabilistic inference of the genetic architecture of age-at-onset phenotypes in a sampling scheme that facilitates biobank-scale time-to-event analyses. We show in extensive simulation work the benefits BayesW provides in terms of number of discoveries, model performance and genomic prediction. In the UK Biobank, we find many thousands of common genomic regions underlying the age-at-onset of high blood pressure (HBP), cardiac disease (CAD), and type-2 diabetes (T2D), and for the genetic basis of onset reflecting the underlying genetic liability to disease. Age-at-menopause and age-at-menarche are also highly polygenic, but with higher variance contributed by low frequency variants. Genomic prediction into the Estonian Biobank data shows that BayesW gives higher prediction accuracy than other approaches."}],"oa":1,"has_accepted_license":"1","user_id":"ba8df636-2132-11f1-aed0-ed93e2281fdd","title":"Genomic architecture and prediction of censored time-to-event phenotypes with a Bayesian genome-wide analysis","year":"2021","article_number":"2337","ddc":["570"],"project":[{"grant_number":"PCEGP3_181181","name":"Improving estimation and prediction of common complex disease risk","_id":"9B8D11D6-BA93-11EA-9121-9846C619BF3A"}],"publisher":"Nature Research","article_processing_charge":"No","date_published":"2021-04-20T00:00:00Z","pmid":1,"volume":12,"intvolume":"        12","author":[{"first_name":"Sven E","full_name":"Ojavee, Sven E","last_name":"Ojavee"},{"first_name":"Athanasios","full_name":"Kousathanas, Athanasios","last_name":"Kousathanas"},{"full_name":"Trejo Banos, Daniel","last_name":"Trejo Banos","first_name":"Daniel"},{"full_name":"Orliac, Etienne J","last_name":"Orliac","first_name":"Etienne J"},{"first_name":"Marion","last_name":"Patxot","full_name":"Patxot, Marion"},{"last_name":"Lall","full_name":"Lall, Kristi","first_name":"Kristi"},{"first_name":"Reedik","last_name":"Magi","full_name":"Magi, Reedik"},{"first_name":"Krista","last_name":"Fischer","full_name":"Fischer, Krista"},{"full_name":"Kutalik, Zoltan","last_name":"Kutalik","first_name":"Zoltan"},{"first_name":"Matthew Richard","orcid":"0000-0001-8982-8813","last_name":"Robinson","id":"E5D42276-F5DA-11E9-8E24-6303E6697425","full_name":"Robinson, Matthew Richard"}],"isi":1,"issue":"1","_id":"8430","external_id":{"isi":["000642509600006"],"pmid":["33879782"]}}]