[{"acknowledged_ssus":[{"_id":"ScienComp"}],"user_id":"8b945eb4-e2f2-11eb-945a-df72226e66a9","publication_identifier":{"issn":["2663-337X"]},"type":"dissertation","date_created":"2026-07-10T13:27:20Z","keyword":["Approximate Message Passing","GWAS","Genomics","Proteomics","Survival modeling"],"doi":"10.15479/AT-ISTA-22258","project":[{"_id":"059876FA-7A3F-11EA-A408-12923DDC885E","name":"Prix Lopez-Loretta 2019 - Marco Mondelli"},{"_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","name":"Improving estimation and prediction of common complex disease risk","grant_number":"PCEGP3_181181"}],"citation":{"mla":"Depope, Al. <i>From Sparse Selection to Risk Prediction: Approximate Message Passing for Proteomic Survival Models and Large-Scale Genomics</i>. Institute of Science and Technology Austria, 2026, doi:<a href=\"https://doi.org/10.15479/AT-ISTA-22258\">10.15479/AT-ISTA-22258</a>.","chicago":"Depope, Al. “From Sparse Selection to Risk Prediction: Approximate Message Passing for Proteomic Survival Models and Large-Scale Genomics.” Institute of Science and Technology Austria, 2026. <a href=\"https://doi.org/10.15479/AT-ISTA-22258\">https://doi.org/10.15479/AT-ISTA-22258</a>.","ista":"Depope A. 2026. From sparse selection to risk prediction: Approximate message passing for proteomic survival models and large-scale genomics. Institute of Science and Technology Austria.","apa":"Depope, A. (2026). <i>From sparse selection to risk prediction: Approximate message passing for proteomic survival models and large-scale genomics</i>. Institute of Science and Technology Austria. <a href=\"https://doi.org/10.15479/AT-ISTA-22258\">https://doi.org/10.15479/AT-ISTA-22258</a>","ieee":"A. Depope, “From sparse selection to risk prediction: Approximate message passing for proteomic survival models and large-scale genomics,” Institute of Science and Technology Austria, 2026.","short":"A. Depope, From Sparse Selection to Risk Prediction: Approximate Message Passing for Proteomic Survival Models and Large-Scale Genomics, Institute of Science and Technology Austria, 2026.","ama":"Depope A. From sparse selection to risk prediction: Approximate message passing for proteomic survival models and large-scale genomics. 2026. doi:<a href=\"https://doi.org/10.15479/AT-ISTA-22258\">10.15479/AT-ISTA-22258</a>"},"article_processing_charge":"No","supervisor":[{"full_name":"Robinson, Matthew Richard","first_name":"Matthew Richard","orcid":"0000-0001-8982-8813","id":"E5D42276-F5DA-11E9-8E24-6303E6697425","last_name":"Robinson"},{"last_name":"Mondelli","orcid":"0000-0002-3242-7020","id":"27EB676C-8706-11E9-9510-7717E6697425","full_name":"Mondelli, Marco","first_name":"Marco"}],"publication_status":"published","language":[{"iso":"eng"}],"title":"From sparse selection to risk prediction: Approximate message passing for proteomic survival models and large-scale genomics","month":"07","oa_version":"Published Version","oa":1,"related_material":{"record":[{"relation":"part_of_dissertation","status":"public","id":"21488"}]},"has_accepted_license":"1","alternative_title":["ISTA Thesis"],"das_tickbox":"1","department":[{"_id":"GradSch"},{"_id":"MaRo"},{"_id":"MaMo"}],"status":"public","ddc":["576","610","006"],"date_updated":"2026-07-28T07:08:15Z","date_published":"2026-07-11T00:00:00Z","day":"11","author":[{"full_name":"Depope, Al","first_name":"Al","last_name":"Depope","id":"0b77531d-dbcd-11ea-9d1d-a8eee0bf3830"}],"abstract":[{"lang":"eng","text":"Uncovering the genetic architecture of complex traits and pinpointing causal molecular drivers require the ability to distinguish true signals from noise within massive, high-dimensional omics datasets. To extract meaningful biological insights from these datasets, such as identifying causal genetic variants and proteins, scalable and accurate inference methods are essential. To this end, this thesis develops novel Bayesian inference frameworks based on Vector Approximate Message Passing and demonstrates their effectiveness in the modeling of disease onset times and quantitative physical and clinical measures.\r\n\r\nFirst, we introduce gVAMP, a Bayesian framework tailored for Genome-Wide Association Studies that enables the joint modeling of quantitative complex traits across millions of genetic variants. gVAMP demonstrates superior accuracy in variable selection and out-of-sample polygenic risk prediction compared to state-of-the-art approaches. We model human height using 17 million whole-genome sequence variants from the UK Biobank, incorporating a vast number of rare variants and revealing novel associations. gVAMP achieves a prediction accuracy of approximately 46% for human height, representing the highest reported performance for this trait to date. \r\n\r\nSecond, we present vampW, a Bayesian framework for survival analysis applied to proteomic data. By effectively handling right-censoring and complex protein dependencies within the UK Biobank Pharma Proteomics Project dataset, vampW identifies 219 protein associations across 24 disease outcomes, the majority of which are not among the top marginal discoveries. We further adjust protein levels for exponential age effects, yielding 1,308 associations and highlighting the sensitivity of the analysis to the chosen age-correction methodology. Finally, vampW improves upon the variable selection capabilities of the commonly used (penalized) variants of the Cox proportional hazards model and delivers state-of-the-art out-of-sample prediction of disease onset times.\r\n\r\nCollectively, these methods provide powerful tools for dissecting the genetic architecture of complex traits and the proteomic drivers of disease onset. Furthermore, by delivering accurate polygenic risk scores and precise predictions of onset times, this work advances the capabilities of personalized medicine and clinical risk stratification."}],"doi_confirm":"1","publisher":"Institute of Science and Technology Austria","file_date_updated":"2026-07-13T14:56:41Z","year":"2026","page":"169","OA_place":"publisher","file":[{"file_name":"2026_Depope_Al_Thesis.pdf","relation":"main_file","file_size":25109878,"date_updated":"2026-07-13T14:52:19Z","access_level":"open_access","content_type":"application/pdf","file_id":"22316","checksum":"9ab386790515628d957a194f30a7ccb4","date_created":"2026-07-13T14:52:19Z","creator":"adepope"},{"file_name":"2026_Depope_Al_Thesis.zip","relation":"source_file","access_level":"closed","content_type":"application/zip","date_updated":"2026-07-13T14:56:41Z","file_size":1203199939,"checksum":"8ed8fb63f76a695d5b6fec35343f4b90","date_created":"2026-07-13T14:56:41Z","creator":"adepope","file_id":"22317"}],"acknowledgement":"This work was supported in part by the Swiss National Science Foundation through the\r\nEccellenza Grant \"Improving estimation and prediction of common complex disease risk\"\r\n(grant number PCEGP3_181181); the European Research Council through the grant\r\n\"Inference in High Dimensions: Light-speed Algorithms and Information Limits\" (grant\r\nnumber 101161364); and the Fondation Jean-Jacques et Felicia Lopez-Loreta through the\r\nPrix Lopez-Loretta 2019.\r\n","degree_awarded":"PhD","corr_author":"1","_id":"22258"},{"related_material":{"link":[{"description":"News on ISTA website","relation":"press_release","url":"https://ista.ac.at/en/news/human-traits-beyond-inherited-genes/"}]},"has_accepted_license":"1","oa_version":"Published Version","issue":"7","oa":1,"supplementarymaterial":"yes","month":"07","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.","title":"Separating direct, indirect, and parent-of-origin genetic effects in the human population","language":[{"iso":"eng"}],"publication_status":"published","article_processing_charge":"Yes","quality_controlled":"1","project":[{"grant_number":"PCEGP3_181181","name":"Improving estimation and prediction of common complex disease risk","_id":"9B8D11D6-BA93-11EA-9121-9846C619BF3A"}],"citation":{"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>","mla":"Krätschmer, Ilse, et al. “Separating Direct, Indirect, and Parent-of-Origin Genetic Effects in the Human Population.” <i>Cell Genomics</i>, vol. 6, no. 7, 101277, Elsevier, 2026, doi:<a href=\"https://doi.org/10.1016/j.xgen.2026.101277\">10.1016/j.xgen.2026.101277</a>.","chicago":"Krätschmer, Ilse, Laura Hegemann, Robin J. Hofmeister, Elizabeth C. Corfield, Mahdi Mahmoudi, Olivier Delaneau, Ole A. Andreassen, et al. “Separating Direct, Indirect, and Parent-of-Origin Genetic Effects in the Human Population.” <i>Cell Genomics</i>. Elsevier, 2026. <a href=\"https://doi.org/10.1016/j.xgen.2026.101277\">https://doi.org/10.1016/j.xgen.2026.101277</a>.","apa":"Krätschmer, I., Hegemann, L., Hofmeister, R. J., Corfield, E. C., Mahmoudi, M., Delaneau, O., … Robinson, M. R. (2026). Separating direct, indirect, and parent-of-origin genetic effects in the human population. <i>Cell Genomics</i>. Elsevier. <a href=\"https://doi.org/10.1016/j.xgen.2026.101277\">https://doi.org/10.1016/j.xgen.2026.101277</a>","ieee":"I. Krätschmer <i>et al.</i>, “Separating direct, indirect, and parent-of-origin genetic effects in the human population,” <i>Cell Genomics</i>, vol. 6, no. 7. Elsevier, 2026.","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."},"doi":"10.1016/j.xgen.2026.101277","date_created":"2026-06-10T07:39:08Z","keyword":["direct genetic effects","DGE","indirect genetic effects","IGE","parent-of-origin effects","phenotypic variation","assortative mating","within-family GWAS","MoBa","EstBB"],"volume":6,"article_type":"original","scopus_import":"1","type":"journal_article","researchdata_availability":"yes","acknowledged_ssus":[{"_id":"ScienComp"}],"pmid":1,"user_id":"ba8df636-2132-11f1-aed0-ed93e2281fdd","publication_identifier":{"eissn":["2666-979X"]},"article_number":"101277","_id":"21987","corr_author":"1","OA_place":"publisher","file":[{"checksum":"f896b510480d2d4e4a7fd46c2e2761f4","date_created":"2026-07-28T07:24:50Z","creator":"dernst","file_id":"22597","access_level":"open_access","content_type":"application/pdf","file_size":3679297,"date_updated":"2026-07-28T07:24:50Z","success":1,"relation":"main_file","file_name":"2026_CellGenomics_Kraetschmer.pdf"}],"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).","year":"2026","file_date_updated":"2026-07-28T07:24:50Z","OA_type":"gold","publisher":"Elsevier","author":[{"last_name":"Krätschmer","id":"30d4014e-7753-11eb-b44b-db6d61112e73","orcid":"0000-0002-5636-9259","first_name":"Ilse","full_name":"Krätschmer, Ilse"},{"last_name":"Hegemann","full_name":"Hegemann, Laura","first_name":"Laura"},{"last_name":"Hofmeister","first_name":"Robin J.","full_name":"Hofmeister, Robin J."},{"first_name":"Elizabeth C.","full_name":"Corfield, Elizabeth C.","last_name":"Corfield"},{"full_name":"Mahmoudi, Mahdi","first_name":"Mahdi","last_name":"Mahmoudi"},{"last_name":"Delaneau","full_name":"Delaneau, Olivier","first_name":"Olivier"},{"last_name":"Andreassen","first_name":"Ole A.","full_name":"Andreassen, Ole A."},{"first_name":"Archie","full_name":"Campbell, Archie","last_name":"Campbell"},{"full_name":"Hayward, Caroline","first_name":"Caroline","last_name":"Hayward"},{"last_name":"Marioni","full_name":"Marioni, Riccardo E.","first_name":"Riccardo E."},{"last_name":"Ystrom","full_name":"Ystrom, Eivind","first_name":"Eivind"},{"last_name":"Havdahl","first_name":"Alexandra","full_name":"Havdahl, Alexandra"},{"last_name":"Robinson","orcid":"0000-0001-8982-8813","id":"E5D42276-F5DA-11E9-8E24-6303E6697425","full_name":"Robinson, Matthew Richard","first_name":"Matthew Richard"}],"abstract":[{"lang":"eng","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."}],"intvolume":"         6","day":"08","date_published":"2026-07-08T00:00:00Z","external_id":{"pmid":["40909755"]},"date_updated":"2026-08-04T09:34:08Z","status":"public","tmp":{"image":"/images/cc_by_nc_nd.png","name":"Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)","short":"CC BY-NC-ND (4.0)","legal_code_url":"https://creativecommons.org/licenses/by-nc-nd/4.0/legalcode"},"ddc":["570"],"DOAJ_listed":"1","das_tickbox":"1","publication":"Cell Genomics","department":[{"_id":"MaRo"}]}]