--- OA_place: publisher _id: '22258' 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." acknowledged_ssus: - _id: ScienComp 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" alternative_title: - ISTA Thesis article_processing_charge: No author: - first_name: Al full_name: Depope, Al id: 0b77531d-dbcd-11ea-9d1d-a8eee0bf3830 last_name: Depope citation: ama: 'Depope A. From sparse selection to risk prediction: Approximate message passing for proteomic survival models and large-scale genomics. 2026. doi:10.15479/AT-ISTA-22258' apa: '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. https://doi.org/10.15479/AT-ISTA-22258' 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. https://doi.org/10.15479/AT-ISTA-22258.' 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.' ista: 'Depope A. 2026. From sparse selection to risk prediction: Approximate message passing for proteomic survival models and large-scale genomics. Institute of Science and Technology Austria.' mla: 'Depope, Al. From Sparse Selection to Risk Prediction: Approximate Message Passing for Proteomic Survival Models and Large-Scale Genomics. Institute of Science and Technology Austria, 2026, doi:10.15479/AT-ISTA-22258.' 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.' corr_author: '1' das_tickbox: '1' date_created: 2026-07-10T13:27:20Z date_published: 2026-07-11T00:00:00Z date_updated: 2026-07-28T07:08:15Z day: '11' ddc: - '576' - '610' - '006' degree_awarded: PhD department: - _id: GradSch - _id: MaRo - _id: MaMo doi: 10.15479/AT-ISTA-22258 doi_confirm: '1' file: - access_level: open_access checksum: 9ab386790515628d957a194f30a7ccb4 content_type: application/pdf creator: adepope date_created: 2026-07-13T14:52:19Z date_updated: 2026-07-13T14:52:19Z file_id: '22316' file_name: 2026_Depope_Al_Thesis.pdf file_size: 25109878 relation: main_file - access_level: closed checksum: 8ed8fb63f76a695d5b6fec35343f4b90 content_type: application/zip creator: adepope date_created: 2026-07-13T14:56:41Z date_updated: 2026-07-13T14:56:41Z file_id: '22317' file_name: 2026_Depope_Al_Thesis.zip file_size: 1203199939 relation: source_file file_date_updated: 2026-07-13T14:56:41Z has_accepted_license: '1' keyword: - Approximate Message Passing - GWAS - Genomics - Proteomics - Survival modeling language: - iso: eng month: '07' oa: 1 oa_version: Published Version page: '169' project: - _id: 059876FA-7A3F-11EA-A408-12923DDC885E name: Prix Lopez-Loretta 2019 - Marco Mondelli - _id: 911e6d1f-16d5-11f0-9cad-c5c68c6a1cdf grant_number: '101161364' name: 'Inference in High Dimensions: Light-speed Algorithms and Information Limits' - _id: 9B8D11D6-BA93-11EA-9121-9846C619BF3A grant_number: PCEGP3_181181 name: Improving estimation and prediction of common complex disease risk publication_identifier: issn: - 2663-337X publication_status: published publisher: Institute of Science and Technology Austria related_material: record: - id: '21488' relation: part_of_dissertation status: public status: public supervisor: - first_name: Matthew Richard full_name: Robinson, Matthew Richard id: E5D42276-F5DA-11E9-8E24-6303E6697425 last_name: Robinson orcid: 0000-0001-8982-8813 - first_name: Marco full_name: Mondelli, Marco id: 27EB676C-8706-11E9-9510-7717E6697425 last_name: Mondelli orcid: 0000-0002-3242-7020 title: 'From sparse selection to risk prediction: Approximate message passing for proteomic survival models and large-scale genomics' type: dissertation user_id: 8b945eb4-e2f2-11eb-945a-df72226e66a9 year: '2026' ... --- DOAJ_listed: '1' OA_place: publisher OA_type: gold _id: '21987' 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.' acknowledged_ssus: - _id: ScienComp 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)." article_number: '101277' article_processing_charge: Yes article_type: original author: - first_name: Ilse full_name: Krätschmer, Ilse id: 30d4014e-7753-11eb-b44b-db6d61112e73 last_name: Krätschmer orcid: 0000-0002-5636-9259 - first_name: Laura full_name: Hegemann, Laura last_name: Hegemann - first_name: Robin J. full_name: Hofmeister, Robin J. last_name: Hofmeister - first_name: Elizabeth C. full_name: Corfield, Elizabeth C. last_name: Corfield - first_name: Mahdi full_name: Mahmoudi, Mahdi last_name: Mahmoudi - first_name: Olivier full_name: Delaneau, Olivier last_name: Delaneau - first_name: Ole A. full_name: Andreassen, Ole A. last_name: Andreassen - first_name: Archie full_name: Campbell, Archie last_name: Campbell - first_name: Caroline full_name: Hayward, Caroline last_name: Hayward - first_name: Riccardo E. full_name: Marioni, Riccardo E. last_name: Marioni - first_name: Eivind full_name: Ystrom, Eivind last_name: Ystrom - first_name: Alexandra full_name: Havdahl, Alexandra last_name: Havdahl - first_name: Matthew Richard full_name: Robinson, Matthew Richard id: E5D42276-F5DA-11E9-8E24-6303E6697425 last_name: Robinson orcid: 0000-0001-8982-8813 citation: ama: Krätschmer I, Hegemann L, Hofmeister RJ, et al. Separating direct, indirect, and parent-of-origin genetic effects in the human population. Cell Genomics. 2026;6(7). doi:10.1016/j.xgen.2026.101277 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. Cell Genomics. Elsevier. https://doi.org/10.1016/j.xgen.2026.101277 chicago: Krätschmer, Ilse, Laura Hegemann, Robin J. Hofmeister, Elizabeth C. Corfield, Mahdi Mahmoudi, Olivier Delaneau, Ole A. Andreassen, et al. “Separating Direct, Indirect, and Parent-of-Origin Genetic Effects in the Human Population.” Cell Genomics. Elsevier, 2026. https://doi.org/10.1016/j.xgen.2026.101277. ieee: I. Krätschmer et al., “Separating direct, indirect, and parent-of-origin genetic effects in the human population,” Cell Genomics, 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. mla: Krätschmer, Ilse, et al. “Separating Direct, Indirect, and Parent-of-Origin Genetic Effects in the Human Population.” Cell Genomics, vol. 6, no. 7, 101277, Elsevier, 2026, doi:10.1016/j.xgen.2026.101277. 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 6 (2026). corr_author: '1' das_tickbox: '1' 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." date_created: 2026-06-10T07:39:08Z date_published: 2026-07-08T00:00:00Z date_updated: 2026-08-04T09:34:08Z day: '08' ddc: - '570' department: - _id: MaRo doi: 10.1016/j.xgen.2026.101277 external_id: pmid: - '40909755' file: - access_level: open_access checksum: f896b510480d2d4e4a7fd46c2e2761f4 content_type: application/pdf creator: dernst date_created: 2026-07-28T07:24:50Z date_updated: 2026-07-28T07:24:50Z file_id: '22597' file_name: 2026_CellGenomics_Kraetschmer.pdf file_size: 3679297 relation: main_file success: 1 file_date_updated: 2026-07-28T07:24:50Z has_accepted_license: '1' intvolume: ' 6' issue: '7' keyword: - direct genetic effects - DGE - indirect genetic effects - IGE - parent-of-origin effects - phenotypic variation - assortative mating - within-family GWAS - MoBa - EstBB language: - iso: eng month: '07' oa: 1 oa_version: Published Version pmid: 1 project: - _id: 9B8D11D6-BA93-11EA-9121-9846C619BF3A grant_number: PCEGP3_181181 name: Improving estimation and prediction of common complex disease risk publication: Cell Genomics publication_identifier: eissn: - 2666-979X publication_status: published publisher: Elsevier quality_controlled: '1' related_material: link: - description: News on ISTA website relation: press_release url: https://ista.ac.at/en/news/human-traits-beyond-inherited-genes/ researchdata_availability: yes scopus_import: '1' status: public supplementarymaterial: yes title: Separating direct, indirect, and parent-of-origin genetic effects in the human population tmp: image: /images/cc_by_nc_nd.png legal_code_url: https://creativecommons.org/licenses/by-nc-nd/4.0/legalcode name: Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) short: CC BY-NC-ND (4.0) type: journal_article user_id: ba8df636-2132-11f1-aed0-ed93e2281fdd volume: 6 year: '2026' ...