[{"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."}],"title":"From sparse selection to risk prediction: Approximate message passing for proteomic survival models and large-scale genomics","keyword":["Approximate Message Passing","GWAS","Genomics","Proteomics","Survival modeling"],"author":[{"first_name":"Al","last_name":"Depope","id":"0b77531d-dbcd-11ea-9d1d-a8eee0bf3830","full_name":"Depope, Al"}],"has_accepted_license":"1","doi":"10.15479/AT-ISTA-22258","corr_author":"1","publisher":"Institute of Science and Technology Austria","acknowledged_ssus":[{"_id":"ScienComp"}],"date_published":"2026-07-11T00:00:00Z","publication_identifier":{"issn":["2663-337X"]},"citation":{"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.","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>","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>","mla":"Depope, Al. <i>From Sparse Selection to Risk Prediction: Approximate Message Passing for Proteomic Survival Models and Large-Scale Genomics</i>. Institute of Science and Technology Austria, 2026, doi:<a href=\"https://doi.org/10.15479/AT-ISTA-22258\">10.15479/AT-ISTA-22258</a>.","ieee":"A. Depope, “From sparse selection to risk prediction: Approximate message passing for proteomic survival models and large-scale genomics,” Institute of Science and Technology Austria, 2026.","chicago":"Depope, Al. “From Sparse Selection to Risk Prediction: Approximate Message Passing for Proteomic Survival Models and Large-Scale Genomics.” Institute of Science and Technology Austria, 2026. <a href=\"https://doi.org/10.15479/AT-ISTA-22258\">https://doi.org/10.15479/AT-ISTA-22258</a>.","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."},"_id":"22258","degree_awarded":"PhD","article_processing_charge":"No","oa_version":"Published Version","file_date_updated":"2026-07-13T14:56:41Z","page":"169","supervisor":[{"orcid":"0000-0001-8982-8813","first_name":"Matthew Richard","full_name":"Robinson, Matthew Richard","id":"E5D42276-F5DA-11E9-8E24-6303E6697425","last_name":"Robinson"},{"orcid":"0000-0002-3242-7020","first_name":"Marco","id":"27EB676C-8706-11E9-9510-7717E6697425","last_name":"Mondelli","full_name":"Mondelli, Marco"}],"project":[{"name":"Prix Lopez-Loretta 2019 - Marco Mondelli","_id":"059876FA-7A3F-11EA-A408-12923DDC885E"},{"name":"Inference in High Dimensions: Light-speed Algorithms and Information Limits","grant_number":"101161364","_id":"911e6d1f-16d5-11f0-9cad-c5c68c6a1cdf"},{"name":"Improving estimation and prediction of common complex disease risk","grant_number":"PCEGP3_181181","_id":"9B8D11D6-BA93-11EA-9121-9846C619BF3A"}],"ddc":["576","610","006"],"alternative_title":["ISTA Thesis"],"related_material":{"record":[{"status":"public","id":"21488","relation":"part_of_dissertation"}]},"status":"public","date_created":"2026-07-10T13:27:20Z","department":[{"_id":"GradSch"},{"_id":"MaRo"},{"_id":"MaMo"}],"das_tickbox":"1","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","date_updated":"2026-07-23T05:33:50Z","oa":1,"language":[{"iso":"eng"}],"month":"07","OA_place":"publisher","file":[{"date_created":"2026-07-13T14:52:19Z","file_name":"2026_Depope_Al_Thesis.pdf","file_id":"22316","content_type":"application/pdf","date_updated":"2026-07-13T14:52:19Z","file_size":25109878,"access_level":"open_access","relation":"main_file","creator":"adepope","checksum":"9ab386790515628d957a194f30a7ccb4"},{"file_id":"22317","content_type":"application/zip","file_name":"2026_Depope_Al_Thesis.zip","date_created":"2026-07-13T14:56:41Z","checksum":"8ed8fb63f76a695d5b6fec35343f4b90","creator":"adepope","relation":"source_file","date_updated":"2026-07-13T14:56:41Z","file_size":1203199939,"access_level":"closed"}],"doi_confirm":"1","publication_status":"published","type":"dissertation","day":"11","year":"2026","user_id":"8b945eb4-e2f2-11eb-945a-df72226e66a9"},{"project":[{"name":"Prix Lopez-Loretta 2019 - Marco Mondelli","_id":"059876FA-7A3F-11EA-A408-12923DDC885E"},{"name":"Inference in High Dimensions: Light-speed Algorithms and Information Limits","grant_number":"101161364","_id":"911e6d1f-16d5-11f0-9cad-c5c68c6a1cdf"},{"_id":"9B8D11D6-BA93-11EA-9121-9846C619BF3A","name":"Improving estimation and prediction of common complex disease risk","grant_number":"PCEGP3_181181"}],"oa_version":"Published Version","article_processing_charge":"Yes","publication_identifier":{"eissn":["2666-979X"]},"citation":{"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.","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>","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>","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>.","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.","short":"A. Depope, J. Bajzik, M. Mondelli, M.R. Robinson, Cell Genomics (2026).","chicago":"Depope, Al, Jakub Bajzik, Marco Mondelli, and Matthew Richard Robinson. “Joint Modeling of Whole-Genome Sequencing Data for Human Height via Approximate Message Passing.” <i>Cell Genomics</i>. Elsevier, 2026. <a href=\"https://doi.org/10.1016/j.xgen.2026.101162\">https://doi.org/10.1016/j.xgen.2026.101162</a>."},"date_published":"2026-02-18T00:00:00Z","publisher":"Elsevier","_id":"21488","corr_author":"1","quality_controlled":"1","has_accepted_license":"1","author":[{"full_name":"Depope, Al","last_name":"Depope","id":"0b77531d-dbcd-11ea-9d1d-a8eee0bf3830","first_name":"Al"},{"last_name":"Bajzik","id":"b995e25b-8c4b-11ed-a6d8-f71b7bcd6122","full_name":"Bajzik, Jakub","first_name":"Jakub"},{"first_name":"Marco","orcid":"0000-0002-3242-7020","full_name":"Mondelli, Marco","last_name":"Mondelli","id":"27EB676C-8706-11E9-9510-7717E6697425"},{"full_name":"Robinson, Matthew Richard","last_name":"Robinson","id":"E5D42276-F5DA-11E9-8E24-6303E6697425","orcid":"0000-0001-8982-8813","first_name":"Matthew Richard"}],"doi":"10.1016/j.xgen.2026.101162","DOAJ_listed":"1","main_file_link":[{"url":"https://doi.org/10.1016/j.xgen.2026.101162","open_access":"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"}],"title":"Joint modeling of whole-genome sequencing data for human height via approximate message passing","type":"journal_article","article_type":"original","publication_status":"epub_ahead","day":"18","year":"2026","user_id":"ba8df636-2132-11f1-aed0-ed93e2281fdd","tmp":{"name":"Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)","legal_code_url":"https://creativecommons.org/licenses/by-nc-nd/4.0/legalcode","short":"CC BY-NC-ND (4.0)","image":"/images/cc_by_nc_nd.png"},"OA_place":"publisher","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.","date_updated":"2026-07-23T05:33:49Z","month":"02","language":[{"iso":"eng"}],"oa":1,"department":[{"_id":"MaMo"},{"_id":"MaRo"}],"status":"public","publication":"Cell Genomics","date_created":"2026-03-23T15:10:03Z","OA_type":"gold","ddc":["000","570"],"related_material":{"record":[{"relation":"dissertation_contains","status":"public","id":"22258"}],"link":[{"description":"News on ISTA website","relation":"press_release","url":"https://ista.ac.at/en/news/big-data-and-human-height/"}]},"article_number":"101162"},{"acknowledgement":"This study was conducted at the Josef Ressel Centre for Recovery Strategies of Textiles which is funded by the Christian Doppler Research Society on behalf of the Austrian Federal Ministry of Labor and Economic Affairs and the National Foundation for Research, Technology. The authors acknowledge “Open Access Funding by TU Wien” for financial support through its Open Access Funding Program.\r\nSpecial thanks are extended to EREMA Group GmbH, SALESIANER MIETTEX GmbH and Starlinger & Co GmbH for their material support and valuable input throughout the development of this study.","month":"11","date_updated":"2025-12-01T12:58:17Z","oa":1,"language":[{"iso":"eng"}],"pmid":1,"isi":1,"department":[{"_id":"MaRo"}],"type":"journal_article","article_type":"original","publication_status":"published","year":"2025","day":"01","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","tmp":{"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","image":"/images/cc_by.png"},"OA_place":"publisher","file":[{"date_created":"2025-10-20T10:57:36Z","success":1,"file_name":"2025_WasteMgmt_Depope.pdf","content_type":"application/pdf","file_id":"20501","date_updated":"2025-10-20T10:57:36Z","access_level":"open_access","file_size":4511527,"creator":"dernst","relation":"main_file","checksum":"c232aae0ef7ed653813a835013f25bae"}],"article_number":"115177","status":"public","publication":"Waste Management","date_created":"2025-10-19T22:01:31Z","OA_type":"hybrid","ddc":["572"],"PlanS_conform":"1","volume":208,"article_processing_charge":"Yes (via OA deal)","publication_identifier":{"eissn":["1879-2456"],"issn":["0956-053X"]},"date_published":"2025-11-01T00:00:00Z","citation":{"mla":"Depope, Nika, et al. “Deep Eutectic Solvent as a Solution for Polyester/Cotton Textile Recycling.” <i>Waste Management</i>, vol. 208, 115177, Elsevier, 2025, doi:<a href=\"https://doi.org/10.1016/j.wasman.2025.115177\">10.1016/j.wasman.2025.115177</a>.","ieee":"N. Depope, A. Depope, V. M. Archodoulaki, W. Ipsmiller, and A. Bartl, “Deep eutectic solvent as a solution for polyester/cotton textile recycling,” <i>Waste Management</i>, vol. 208. Elsevier, 2025.","chicago":"Depope, Nika, Al Depope, Vasiliki Maria Archodoulaki, Wolfgang Ipsmiller, and Andreas Bartl. “Deep Eutectic Solvent as a Solution for Polyester/Cotton Textile Recycling.” <i>Waste Management</i>. Elsevier, 2025. <a href=\"https://doi.org/10.1016/j.wasman.2025.115177\">https://doi.org/10.1016/j.wasman.2025.115177</a>.","short":"N. Depope, A. Depope, V.M. Archodoulaki, W. Ipsmiller, A. Bartl, Waste Management 208 (2025).","ista":"Depope N, Depope A, Archodoulaki VM, Ipsmiller W, Bartl A. 2025. Deep eutectic solvent as a solution for polyester/cotton textile recycling. Waste Management. 208, 115177.","ama":"Depope N, Depope A, Archodoulaki VM, Ipsmiller W, Bartl A. Deep eutectic solvent as a solution for polyester/cotton textile recycling. <i>Waste Management</i>. 2025;208. doi:<a href=\"https://doi.org/10.1016/j.wasman.2025.115177\">10.1016/j.wasman.2025.115177</a>","apa":"Depope, N., Depope, A., Archodoulaki, V. M., Ipsmiller, W., &#38; Bartl, A. (2025). Deep eutectic solvent as a solution for polyester/cotton textile recycling. <i>Waste Management</i>. Elsevier. <a href=\"https://doi.org/10.1016/j.wasman.2025.115177\">https://doi.org/10.1016/j.wasman.2025.115177</a>"},"publisher":"Elsevier","_id":"20491","file_date_updated":"2025-10-20T10:57:36Z","oa_version":"Published Version","intvolume":"       208","author":[{"first_name":"Nika","last_name":"Depope","full_name":"Depope, Nika"},{"full_name":"Depope, Al","id":"0b77531d-dbcd-11ea-9d1d-a8eee0bf3830","last_name":"Depope","first_name":"Al"},{"first_name":"Vasiliki Maria","last_name":"Archodoulaki","full_name":"Archodoulaki, Vasiliki Maria"},{"first_name":"Wolfgang","full_name":"Ipsmiller, Wolfgang","last_name":"Ipsmiller"},{"full_name":"Bartl, Andreas","last_name":"Bartl","first_name":"Andreas"}],"has_accepted_license":"1","doi":"10.1016/j.wasman.2025.115177","abstract":[{"lang":"eng","text":"Global fibre production has expanded rapidly, with polyester and cotton dominating, significantly contributing to textile waste and increasing demand for sustainable solutions. This study presents innovative method to recycle polyester/cotton (PET/CO) blends using hydrophobic deep eutectic solvents (DESs), eliminating the need for toxic chemicals while achieving high dissolution yields. PET was completely dissolved within 5 min, substantially outperforming state-of-the-art methods and facilitating the efficient and selective recovery of both components, PET (97%) and CO (100%). SEM imaging confirmed no morphological changes in cotton fibres after treatment. The thermal stability of the recovered materials was validated using DSC and TGA analyses, while ATR-FTIR spectroscopy indicated no chemical changes. Mechanical testing confirmed recovered cotton’s tenacity and elongation are within expected ranges despite showing a decrease of 28% in tenacity and 34% in elongation. Hence, the proposed process provides an efficient and sustainable recycling solution for PET/CO blends, retaining both polymers in a condition similar to virgin materials used in textile manufacturing with minimal processing time."}],"title":"Deep eutectic solvent as a solution for polyester/cotton textile recycling","quality_controlled":"1","scopus_import":"1","external_id":{"isi":["001594629200003"],"pmid":["41066876"]}},{"corr_author":"1","scopus_import":"1","quality_controlled":"1","external_id":{"isi":["001396233806078"]},"main_file_link":[{"open_access":"1","url":"https://openreview.net/forum?id=aQYCDxfZV0"}],"doi":"10.1109/ICASSP48485.2024.10447198","author":[{"last_name":"Depope","id":"0b77531d-dbcd-11ea-9d1d-a8eee0bf3830","full_name":"Depope, Al","first_name":"Al"},{"full_name":"Mondelli, Marco","id":"27EB676C-8706-11E9-9510-7717E6697425","last_name":"Mondelli","orcid":"0000-0002-3242-7020","first_name":"Marco"},{"orcid":"0000-0001-8982-8813","first_name":"Matthew Richard","id":"E5D42276-F5DA-11E9-8E24-6303E6697425","last_name":"Robinson","full_name":"Robinson, Matthew Richard"}],"conference":{"end_date":"2024-04-19","name":"ICASSP: International Conference on Acoustics, Speech and Signal Processing","start_date":"2024-04-14","location":"Seoul, Korea"},"title":"Inference of genetic effects via approximate message passing","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."}],"project":[{"_id":"059876FA-7A3F-11EA-A408-12923DDC885E","name":"Prix Lopez-Loretta 2019 - Marco Mondelli"},{"name":"Improving estimation and prediction of common complex disease risk","grant_number":"PCEGP3_181181","_id":"9B8D11D6-BA93-11EA-9121-9846C619BF3A"}],"page":"13151-13155","oa_version":"Submitted Version","article_processing_charge":"No","_id":"17147","acknowledged_ssus":[{"_id":"ScienComp"}],"date_published":"2024-04-19T00:00:00Z","citation":{"short":"A. Depope, M. Mondelli, M.R. Robinson, in:, 2024 IEEE International Conference on Acoustics, Speech, and Signal Processing, IEEE, 2024, pp. 13151–13155.","ieee":"A. Depope, M. Mondelli, and M. R. Robinson, “Inference of genetic effects via approximate message passing,” in <i>2024 IEEE International Conference on Acoustics, Speech, and Signal Processing</i>, Seoul, Korea, 2024, pp. 13151–13155.","chicago":"Depope, Al, Marco Mondelli, and Matthew Richard Robinson. “Inference of Genetic Effects via Approximate Message Passing.” In <i>2024 IEEE International Conference on Acoustics, Speech, and Signal Processing</i>, 13151–55. IEEE, 2024. <a href=\"https://doi.org/10.1109/ICASSP48485.2024.10447198\">https://doi.org/10.1109/ICASSP48485.2024.10447198</a>.","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.","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>","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>"},"publication_identifier":{"isbn":["9798350344851"],"issn":["1520-6149"]},"publisher":"IEEE","OA_type":"green","date_created":"2024-06-16T22:01:07Z","status":"public","publication":"2024 IEEE International Conference on Acoustics, Speech, and Signal Processing","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","day":"19","year":"2024","type":"conference","publication_status":"published","OA_place":"repository","month":"04","date_updated":"2026-07-13T14:57:55Z","oa":1,"language":[{"iso":"eng"}],"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).","department":[{"_id":"MaMo"},{"_id":"MaRo"}],"isi":1}]