[{"OA_place":"publisher","publication":"Cell Genomics","publisher":"Elsevier","corr_author":"1","status":"public","month":"06","publication_identifier":{"eissn":["2666-979X"]},"main_file_link":[{"open_access":"1","url":"https://doi.org/10.1016/j.xgen.2026.101277"}],"external_id":{"pmid":["40909755"]},"article_number":"101277","author":[{"id":"30d4014e-7753-11eb-b44b-db6d61112e73","orcid":"0000-0002-5636-9259","first_name":"Ilse","full_name":"Krätschmer, Ilse","last_name":"Krätschmer"},{"full_name":"Hegemann, Laura","first_name":"Laura","last_name":"Hegemann"},{"last_name":"Hofmeister","first_name":"Robin J.","full_name":"Hofmeister, Robin J."},{"last_name":"Corfield","full_name":"Corfield, Elizabeth C.","first_name":"Elizabeth C."},{"full_name":"Mahmoudi, Mahdi","first_name":"Mahdi","last_name":"Mahmoudi"},{"first_name":"Olivier","full_name":"Delaneau, Olivier","last_name":"Delaneau"},{"last_name":"Andreassen","first_name":"Ole A.","full_name":"Andreassen, Ole A."},{"last_name":"Campbell","full_name":"Campbell, Archie","first_name":"Archie"},{"first_name":"Caroline","full_name":"Hayward, Caroline","last_name":"Hayward"},{"first_name":"Riccardo E.","full_name":"Marioni, Riccardo E.","last_name":"Marioni"},{"full_name":"Ystrom, Eivind","first_name":"Eivind","last_name":"Ystrom"},{"full_name":"Havdahl, Alexandra","first_name":"Alexandra","last_name":"Havdahl"},{"orcid":"0000-0001-8982-8813","id":"E5D42276-F5DA-11E9-8E24-6303E6697425","last_name":"Robinson","full_name":"Robinson, Matthew Richard","first_name":"Matthew Richard"}],"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).","title":"Separating direct, indirect, and parent-of-origin genetic effects in the human population","article_processing_charge":"Yes","oa_version":"Published Version","pmid":1,"date_published":"2026-06-09T00:00:00Z","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","year":"2026","oa":1,"language":[{"iso":"eng"}],"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."}],"scopus_import":"1","OA_type":"gold","_id":"21987","acknowledged_ssus":[{"_id":"ScienComp"}],"DOAJ_listed":"1","citation":{"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>.","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>.","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.","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>","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>","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."},"quality_controlled":"1","date_updated":"2026-06-19T07:00:47Z","doi":"10.1016/j.xgen.2026.101277","department":[{"_id":"MaRo"}],"type":"journal_article","date_created":"2026-06-10T07:39:08Z","publication_status":"inpress","project":[{"name":"Improving estimation and prediction of common complex disease risk","grant_number":"PCEGP3_181181","_id":"9B8D11D6-BA93-11EA-9121-9846C619BF3A"}],"day":"09","article_type":"original"},{"date_created":"2026-03-23T15:02:54Z","publication_status":"epub_ahead","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"},"type":"journal_article","department":[{"_id":"MaRo"}],"doi":"10.1093/genetics/iyag042","article_type":"original","day":"12","PlanS_conform":"1","OA_type":"hybrid","abstract":[{"text":"An individual's phenotype reflects a complex interplay of the direct effects of their DNA, epigenetic modifications of their DNA induced by their parents, and indirect effects of their parents' DNA. Here, we derive how the genetic variance within a population is changed under the influence of indirect maternal, paternal and parent-of-origin effects under random mating. We also consider indirect effects of a sibling, in particular how the genetic variance is altered when looking at the phenotypic difference between two siblings. The calculations are then extended to include assortative mating (AM), which alters the variance by inducing increased homozygosity and correlations within and across loci. AM likely leads to covariance of parental genetic effects, a measure of the similarity of parents in the indirect effects they have on their children. We propose that this assortment for parental characteristics, where biological parents create similar environments for their children, can create shared parental effects across traits and the appearance of cross-trait AM. Our theory shows how the resemblance among relatives increases under both AM, indirect and parent-of-origin effects. When our model is used to predict correlations among relatives in human height, we find that explaining the patterns observed in real data requires both indirect genetic effects and assortative mating. The degree to which direct, indirect and epigenetic effects shape the phenotypic variance of complex traits remains an open question that requires large-scale family data to be resolved.","lang":"eng"}],"oa":1,"language":[{"iso":"eng"}],"license":"https://creativecommons.org/licenses/by/4.0/","date_updated":"2026-06-18T08:31:14Z","quality_controlled":"1","citation":{"ista":"Krätschmer I, Robinson MR. 2026. A quantitative genetic model for indirect genetic effects and genomic imprinting under random and assortative mating. Genetics., iyag042.","apa":"Krätschmer, I., &#38; Robinson, M. R. (2026). A quantitative genetic model for indirect genetic effects and genomic imprinting under random and assortative mating. <i>Genetics</i>. Oxford University Press. <a href=\"https://doi.org/10.1093/genetics/iyag042\">https://doi.org/10.1093/genetics/iyag042</a>","ama":"Krätschmer I, Robinson MR. A quantitative genetic model for indirect genetic effects and genomic imprinting under random and assortative mating. <i>Genetics</i>. 2026. doi:<a href=\"https://doi.org/10.1093/genetics/iyag042\">10.1093/genetics/iyag042</a>","mla":"Krätschmer, Ilse, and Matthew Richard Robinson. “A Quantitative Genetic Model for Indirect Genetic Effects and Genomic Imprinting under Random and Assortative Mating.” <i>Genetics</i>, iyag042, Oxford University Press, 2026, doi:<a href=\"https://doi.org/10.1093/genetics/iyag042\">10.1093/genetics/iyag042</a>.","ieee":"I. Krätschmer and M. R. Robinson, “A quantitative genetic model for indirect genetic effects and genomic imprinting under random and assortative mating,” <i>Genetics</i>. Oxford University Press, 2026.","chicago":"Krätschmer, Ilse, and Matthew Richard Robinson. “A Quantitative Genetic Model for Indirect Genetic Effects and Genomic Imprinting under Random and Assortative Mating.” <i>Genetics</i>. Oxford University Press, 2026. <a href=\"https://doi.org/10.1093/genetics/iyag042\">https://doi.org/10.1093/genetics/iyag042</a>.","short":"I. Krätschmer, M.R. Robinson, Genetics (2026)."},"related_material":{"link":[{"url":"https://github.com/medical-genomics-group/familyMC","relation":"software"}]},"_id":"21484","acknowledgement":"We thank members of the Medical Genomics group at ISTA for their comments, which improved this manuscript. This work was funded by an SNSF Eccellenza Grant to MRR (PCEGP3-181181), and by core funding from the Institute of Science and Technology Austria.","author":[{"orcid":"0000-0002-5636-9259","id":"30d4014e-7753-11eb-b44b-db6d61112e73","last_name":"Krätschmer","full_name":"Krätschmer, Ilse","first_name":"Ilse"},{"last_name":"Robinson","first_name":"Matthew Richard","full_name":"Robinson, Matthew Richard","orcid":"0000-0001-8982-8813","id":"E5D42276-F5DA-11E9-8E24-6303E6697425"}],"article_number":"iyag042","ddc":["570"],"external_id":{"pmid":["41677404"]},"date_published":"2026-02-12T00:00:00Z","pmid":1,"oa_version":"Published Version","year":"2026","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","title":"A quantitative genetic model for indirect genetic effects and genomic imprinting under random and assortative mating","article_processing_charge":"Yes (via OA deal)","corr_author":"1","has_accepted_license":"1","status":"public","publication":"Genetics","OA_place":"publisher","publisher":"Oxford University Press","main_file_link":[{"url":"https://doi.org/10.1093/genetics/iyag042","open_access":"1"}],"publication_identifier":{"issn":["1943-2631"]},"month":"02"},{"type":"journal_article","tmp":{"legal_code_url":"https://creativecommons.org/licenses/by-nc-nd/4.0/legalcode","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)"},"publication_status":"published","date_created":"2026-03-29T22:07:08Z","file":[{"checksum":"2fd3d7e48b779ac24245f6c35449b89a","success":1,"access_level":"open_access","date_updated":"2026-03-30T07:04:08Z","file_name":"2026_PharmacogenomicsJour_Hajto.pdf","content_type":"application/pdf","creator":"dernst","date_created":"2026-03-30T07:04:08Z","file_size":2618963,"file_id":"21506","relation":"main_file"}],"department":[{"_id":"MaRo"}],"doi":"10.1038/s41397-026-00399-0","article_type":"original","day":"09","scopus_import":"1","OA_type":"hybrid","license":"https://creativecommons.org/licenses/by-nc-nd/4.0/","oa":1,"file_date_updated":"2026-03-30T07:04:08Z","language":[{"iso":"eng"}],"abstract":[{"text":"Currently, pharmacogenetics relies on partially annotated star alleles, leaving novel variants and complex haplotypes uninterpretable. Computational scoring frameworks could overcome these limitations. Here, we comprehensively evaluated the ability of existing (CADD, FATHMM-XF, PROVEAN, MutationAssessor, SIFT, PhyloP100, APF, APF2) and novel (PharmGScore and PharmMLScore) variant effect predictors to assess pharmacogenetic alleles in multiple scenarios. Altogether we analyzed 541 PharmVar alleles, high‑throughput CYP2C9 and CYP2C19 mutational maps, and 200 642 UK Biobank exomes linked with health records containing antidepressant treatment outcomes. Many evaluated tools, especially ensemble frameworks, matched or exceeded star allele classifications (ROC‑AUC up to 0.85 for allele definitions, 0.95 in vitro; TPR up to 0.99 for exomes) and accurately predicted severe antidepressant adverse events for carriers of deleterious variants in CYP2C19 (OR 1.20–1.35). Our findings show that computational predictors deliver star allele accuracy while overcoming their limitations. With additional validation, computational tools could enhance clinical decision frameworks by enabling continuous scoring, incorporating previously unknown variants, and providing genome-wide applicability.","lang":"eng"}],"date_updated":"2026-03-30T07:10:50Z","citation":{"apa":"Hajto, J., Piechota, M., Krätschmer, I., Konowalska, P., Boyle, G. E., Fowler, D. M., … Korostynski, M. (2026). Computational variant predictors for pharmacogenomics: From evaluation of single alleles to assessment of adverse drug reactions to antidepressants. <i>Pharmacogenomics Journal</i>. Springer Nature. <a href=\"https://doi.org/10.1038/s41397-026-00399-0\">https://doi.org/10.1038/s41397-026-00399-0</a>","ama":"Hajto J, Piechota M, Krätschmer I, et al. Computational variant predictors for pharmacogenomics: From evaluation of single alleles to assessment of adverse drug reactions to antidepressants. <i>Pharmacogenomics Journal</i>. 2026;26(2). doi:<a href=\"https://doi.org/10.1038/s41397-026-00399-0\">10.1038/s41397-026-00399-0</a>","ista":"Hajto J, Piechota M, Krätschmer I, Konowalska P, Boyle GE, Fowler DM, Borczyk M, Korostynski M. 2026. Computational variant predictors for pharmacogenomics: From evaluation of single alleles to assessment of adverse drug reactions to antidepressants. Pharmacogenomics Journal. 26(2), 8.","chicago":"Hajto, Jacek, Marcin Piechota, Ilse Krätschmer, Paula Konowalska, Gabriel E. Boyle, Douglas M. Fowler, Malgorzata Borczyk, and Michal Korostynski. “Computational Variant Predictors for Pharmacogenomics: From Evaluation of Single Alleles to Assessment of Adverse Drug Reactions to Antidepressants.” <i>Pharmacogenomics Journal</i>. Springer Nature, 2026. <a href=\"https://doi.org/10.1038/s41397-026-00399-0\">https://doi.org/10.1038/s41397-026-00399-0</a>.","short":"J. Hajto, M. Piechota, I. Krätschmer, P. Konowalska, G.E. Boyle, D.M. Fowler, M. Borczyk, M. Korostynski, Pharmacogenomics Journal 26 (2026).","mla":"Hajto, Jacek, et al. “Computational Variant Predictors for Pharmacogenomics: From Evaluation of Single Alleles to Assessment of Adverse Drug Reactions to Antidepressants.” <i>Pharmacogenomics Journal</i>, vol. 26, no. 2, 8, Springer Nature, 2026, doi:<a href=\"https://doi.org/10.1038/s41397-026-00399-0\">10.1038/s41397-026-00399-0</a>.","ieee":"J. Hajto <i>et al.</i>, “Computational variant predictors for pharmacogenomics: From evaluation of single alleles to assessment of adverse drug reactions to antidepressants,” <i>Pharmacogenomics Journal</i>, vol. 26, no. 2. Springer Nature, 2026."},"quality_controlled":"1","issue":"2","_id":"21503","author":[{"last_name":"Hajto","full_name":"Hajto, Jacek","first_name":"Jacek"},{"last_name":"Piechota","full_name":"Piechota, Marcin","first_name":"Marcin"},{"last_name":"Krätschmer","first_name":"Ilse","full_name":"Krätschmer, Ilse","orcid":"0000-0002-5636-9259","id":"30d4014e-7753-11eb-b44b-db6d61112e73"},{"first_name":"Paula","full_name":"Konowalska, Paula","last_name":"Konowalska"},{"first_name":"Gabriel E.","full_name":"Boyle, Gabriel E.","last_name":"Boyle"},{"first_name":"Douglas M.","full_name":"Fowler, Douglas M.","last_name":"Fowler"},{"last_name":"Borczyk","first_name":"Malgorzata","full_name":"Borczyk, Malgorzata"},{"last_name":"Korostynski","first_name":"Michal","full_name":"Korostynski, Michal"}],"acknowledgement":"This research has been conducted using the UK Biobank Resource under Application Number 62979. We are grateful to the UK Biobank and all its voluntary participants. This work used data provided by patients and collected by the NHS as part of their care and support.\r\n\r\nThis study was funded by the National Science Center, Poland: PRELUDIUM BIS-3 grant no. 2021/43/O/NZ7/01187 (development and benchmarking of variant scores) and SONATINA 5 grant 2021/40/C/NZ2/00218 (UKB analyses). Additional support came from the statutory funds of the Maj Institute of Pharmacology PAS. We gratefully acknowledge Poland’s high-performance Infrastructure PLGrid ACK Cyfronet AGH, for providing computer facilities and support within computational grant no PLG/2022/015861. DMF and GEB were funded by NIH grants NIH R35GM152106 and UM1HG011969.","external_id":{"pmid":["41803106"]},"article_number":"8","ddc":["570"],"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","year":"2026","pmid":1,"volume":26,"oa_version":"Published Version","date_published":"2026-03-09T00:00:00Z","article_processing_charge":"Yes (in subscription journal)","title":"Computational variant predictors for pharmacogenomics: From evaluation of single alleles to assessment of adverse drug reactions to antidepressants","status":"public","has_accepted_license":"1","publisher":"Springer Nature","OA_place":"publisher","publication":"Pharmacogenomics Journal","publication_identifier":{"issn":[" 1470-269X"],"eissn":["1473-1150"]},"month":"03","intvolume":"        26"},{"author":[{"id":"3591A0AA-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0001-6617-9742","full_name":"Machnik, Nick N","first_name":"Nick N","last_name":"Machnik"},{"id":"b9f6d5ef-7774-11eb-a47f-df2c75c02ee7","first_name":"Seyed Mahdi","full_name":"Mahmoudi, Seyed Mahdi","last_name":"Mahmoudi"},{"last_name":"Borczyk","full_name":"Borczyk, Malgorzata","first_name":"Malgorzata"},{"orcid":"0000-0002-5636-9259","id":"30d4014e-7753-11eb-b44b-db6d61112e73","last_name":"Krätschmer","first_name":"Ilse","full_name":"Krätschmer, Ilse"},{"last_name":"Bauer","first_name":"Markus J.","full_name":"Bauer, Markus J."},{"orcid":"0000-0001-8982-8813","id":"E5D42276-F5DA-11E9-8E24-6303E6697425","last_name":"Robinson","first_name":"Matthew Richard","full_name":"Robinson, Matthew Richard"}],"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). ","article_processing_charge":"No","title":"Causal inference for multiple risk factors and diseases from genomics data","user_id":"8b945eb4-e2f2-11eb-945a-df72226e66a9","year":"2024","oa_version":"Preprint","date_published":"2024-08-10T00:00:00Z","OA_place":"repository","publication":"bioRxiv","status":"public","corr_author":"1","month":"08","main_file_link":[{"url":"https://doi.org/10.1101/2023.12.06.570392","open_access":"1"}],"doi":"10.1101/2023.12.06.570392","department":[{"_id":"MaRo"}],"tmp":{"image":"/images/cc_by_nc.png","legal_code_url":"https://creativecommons.org/licenses/by-nc/4.0/legalcode","name":"Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0)","short":"CC BY-NC (4.0)"},"type":"preprint","date_created":"2024-12-11T10:42:59Z","publication_status":"published","day":"10","project":[{"grant_number":"PCEGP3_181181","name":"Improving estimation and prediction of common complex disease risk","_id":"9B8D11D6-BA93-11EA-9121-9846C619BF3A"},{"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"}],"license":"https://creativecommons.org/licenses/by-nc/4.0/","oa":1,"language":[{"iso":"eng"}],"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"}],"OA_type":"free access","_id":"18648","related_material":{"record":[{"relation":"dissertation_contains","id":"18642","status":"public"}]},"acknowledged_ssus":[{"_id":"ScienComp"}],"date_updated":"2026-07-08T22:30:27Z","citation":{"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>.","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.","chicago":"Machnik, Nick N, Seyed Mahdi Mahmoudi, Malgorzata Borczyk, Ilse Krätschmer, Markus J. Bauer, and Matthew Richard Robinson. “Causal Inference for Multiple Risk Factors and Diseases from Genomics Data.” <i>BioRxiv</i>, 2024. <a href=\"https://doi.org/10.1101/2023.12.06.570392\">https://doi.org/10.1101/2023.12.06.570392</a>.","short":"N.N. Machnik, S.M. Mahmoudi, M. Borczyk, I. Krätschmer, M.J. Bauer, M.R. Robinson, BioRxiv (2024).","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>.","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>"}},{"keyword":["Nuclear and High Energy Physics"],"month":"05","intvolume":"       840","publication_identifier":{"eissn":["1873-2445"],"issn":["0370-2693"]},"publisher":"Elsevier","publication":"Physics Letters B","status":"public","has_accepted_license":"1","article_processing_charge":"Yes (via OA deal)","title":"Low-pT quarkonium polarization measurements: Challenges and opportunities","user_id":"317138e5-6ab7-11ef-aa6d-ffef3953e345","year":"2023","date_published":"2023-05-10T00:00:00Z","volume":840,"oa_version":"Published Version","external_id":{"isi":["000967947300001"]},"ddc":["530"],"article_number":"137871","author":[{"full_name":"Faccioli, Pietro","first_name":"Pietro","last_name":"Faccioli"},{"first_name":"Ilse","full_name":"Krätschmer, Ilse","last_name":"Krätschmer","id":"30d4014e-7753-11eb-b44b-db6d61112e73","orcid":"0000-0002-5636-9259"},{"last_name":"Lourenço","first_name":"Carlos","full_name":"Lourenço, Carlos"}],"acknowledgement":"P.F. and C.L. acknowledge support from Fundação para a Ciência e a Tecnologia, Portugal, under contract CERN/FIS-PAR/0010/2019.\r\nOpen Access funded by SCOAP3.","_id":"14753","citation":{"ieee":"P. Faccioli, I. Krätschmer, and C. Lourenço, “Low-pT quarkonium polarization measurements: Challenges and opportunities,” <i>Physics Letters B</i>, vol. 840. Elsevier, 2023.","mla":"Faccioli, Pietro, et al. “Low-PT Quarkonium Polarization Measurements: Challenges and Opportunities.” <i>Physics Letters B</i>, vol. 840, 137871, Elsevier, 2023, doi:<a href=\"https://doi.org/10.1016/j.physletb.2023.137871\">10.1016/j.physletb.2023.137871</a>.","short":"P. Faccioli, I. Krätschmer, C. Lourenço, Physics Letters B 840 (2023).","chicago":"Faccioli, Pietro, Ilse Krätschmer, and Carlos Lourenço. “Low-PT Quarkonium Polarization Measurements: Challenges and Opportunities.” <i>Physics Letters B</i>. Elsevier, 2023. <a href=\"https://doi.org/10.1016/j.physletb.2023.137871\">https://doi.org/10.1016/j.physletb.2023.137871</a>.","ista":"Faccioli P, Krätschmer I, Lourenço C. 2023. Low-pT quarkonium polarization measurements: Challenges and opportunities. Physics Letters B. 840, 137871.","ama":"Faccioli P, Krätschmer I, Lourenço C. Low-pT quarkonium polarization measurements: Challenges and opportunities. <i>Physics Letters B</i>. 2023;840. doi:<a href=\"https://doi.org/10.1016/j.physletb.2023.137871\">10.1016/j.physletb.2023.137871</a>","apa":"Faccioli, P., Krätschmer, I., &#38; Lourenço, C. (2023). Low-pT quarkonium polarization measurements: Challenges and opportunities. <i>Physics Letters B</i>. Elsevier. <a href=\"https://doi.org/10.1016/j.physletb.2023.137871\">https://doi.org/10.1016/j.physletb.2023.137871</a>"},"date_updated":"2025-09-09T14:13:46Z","quality_controlled":"1","language":[{"iso":"eng"}],"file_date_updated":"2024-01-09T08:59:24Z","oa":1,"abstract":[{"text":"Several fixed-target experiments reported J/ψ and ϒ polarizations, as functions of Feynman x (xF) and transverse momentum (PT), in three different frames, using different combinations of beam particles, target nuclei, and collision energies. Despite the diverse and heterogeneous picture formed by these measurements, a detailed look allows us to discern qualitative physical patterns that inspire a simple empirical model. This data-driven scenario offers a good quantitative description of the J/ψ and ϒ(1S) polarizations measured in proton- and pion-nucleus collisions, in the xF 0.5 domain: more than 80 data points (not statistically independent) are well reproduced with only one free parameter. This study sets the context for future low-PT\r\n quarkonium polarization measurements in proton- and pion-nucleus collisions, such as those to be made by the AMBER experiment, and shows that such measurements provide significant constraints on the poorly-known parton distribution functions of the pion.","lang":"eng"}],"scopus_import":"1","isi":1,"day":"10","article_type":"original","file":[{"file_size":855494,"date_created":"2024-01-09T08:59:24Z","creator":"dernst","file_id":"14762","relation":"main_file","file_name":"2023_PhysicsLettersB_Faccioli.pdf","date_updated":"2024-01-09T08:59:24Z","success":1,"access_level":"open_access","checksum":"02dec160dbc81d95985e755869d8afbf","content_type":"application/pdf"}],"doi":"10.1016/j.physletb.2023.137871","department":[{"_id":"MaRo"}],"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"},"type":"journal_article","publication_status":"published","date_created":"2024-01-08T13:09:17Z"}]