{"entity": "researcher", "timestamp": "2026-07-20T12:36:33.366Z", "family": "Vi\u00f1uela", "given": "Ana", "initials": "A", "orcid": "0000-0003-3771-8537", "affiliations": ["Department of Genetic Medicine and Development, University of Geneva Medical School, Geneva, Switzerland.", "Institute for Genetics and Genomics in Geneva, University of Geneva Medical School, Geneva, Switzerland.", "Swiss Institute of Bioinformatics, Geneva, Switzerland."], "links": {"self": {"href": "https://publications.scilifelab.se/researcher/e6551fc3132f4fa597378c565247a315.json"}, "display": {"href": "https://publications.scilifelab.se/researcher/e6551fc3132f4fa597378c565247a315"}}, "publications": [{"entity": "publication", "iuid": "ebf1d35aec514119bc17e814be3919b4", "links": {"self": {"href": "https://publications.scilifelab.se/publication/ebf1d35aec514119bc17e814be3919b4.json"}, "display": {"href": "https://publications.scilifelab.se/publication/ebf1d35aec514119bc17e814be3919b4"}}, "title": "Discovery of drug-omics associations in type 2 diabetes with generative deep-learning models.", "authors": [{"family": "Alles\u00f8e", "given": "Rosa Lundbye", "initials": "RL"}, {"family": "Lundgaard", "given": "Agnete Troen", "initials": "AT", "orcid": "0000-0001-7447-6560", "researcher": {"href": "https://publications.scilifelab.se/researcher/fe5a72da2c1c4f538f6ee77ad74c1ee7.json"}}, {"family": "Hern\u00e1ndez Medina", "given": "Ricardo", "initials": "R", "orcid": "0000-0001-6373-2362", "researcher": {"href": "https://publications.scilifelab.se/researcher/398257b604b940e3861d8e994c49aed4.json"}}, {"family": "Aguayo-Orozco", "given": "Alejandro", "initials": "A"}, {"family": "Johansen", "given": "Joachim", "initials": "J", "orcid": "0000-0001-7052-1870", "researcher": {"href": "https://publications.scilifelab.se/researcher/57fd357e687d4bc0a85afb3291aa664a.json"}}, {"family": "Nissen", "given": "Jakob Nybo", "initials": "JN"}, {"family": "Brorsson", "given": "Caroline", "initials": "C"}, 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{"family": "Pattou", "given": "Francois", "initials": "F"}, {"family": "Raverdy", "given": "Violeta", "initials": "V"}, {"family": "Brage", "given": "Soren", "initials": "S"}, {"family": "Kokkola", "given": "Tarja", "initials": "T"}, {"family": "Heggie", "given": "Alison", "initials": "A"}, {"family": "McEvoy", "given": "Donna", "initials": "D"}, {"family": "Mourby", "given": "Miranda", "initials": "M"}, {"family": "Kaye", "given": "Jane", "initials": "J", "orcid": "0000-0002-7311-4725", "researcher": {"href": "https://publications.scilifelab.se/researcher/12a3f71816624fcd927caa8e787c1f1a.json"}}, {"family": "Hattersley", "given": "Andrew", "initials": "A", "orcid": "0000-0001-5620-473X", "researcher": {"href": "https://publications.scilifelab.se/researcher/25dad832cfe041a79e35170e789e66c7.json"}}, {"family": "McDonald", "given": "Timothy", "initials": "T"}, {"family": "Ridderstr\u00e5le", "given": "Martin", "initials": "M", "orcid": "0000-0002-3270-9167", "researcher": {"href": 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"researcher": {"href": "https://publications.scilifelab.se/researcher/aba5822711b246b397fffacb7ae403b3.json"}}, {"family": "Adamski", "given": "Jerzy", "initials": "J"}, {"family": "McCarthy", "given": "Mark I", "initials": "MI"}, {"family": "Pearson", "given": "Ewan", "initials": "E"}, {"family": "Banasik", "given": "Karina", "initials": "K"}, {"family": "Rasmussen", "given": "Simon", "initials": "S", "orcid": "0000-0001-6323-9041", "researcher": {"href": "https://publications.scilifelab.se/researcher/2db2989b0a0a4d10af1e0627b83e73bc.json"}}, {"family": "Brunak", "given": "S\u00f8ren", "initials": "S", "orcid": "0000-0003-0316-5866", "researcher": {"href": "https://publications.scilifelab.se/researcher/76fd1f30268c450092b654e25a5db4ee.json"}}, {"family": "IMI DIRECT Consortium", "given": "", "initials": ""}], "type": "journal article", "published": "2023-01-02", "journal": {"title": "Nat. Biotechnol.", "issn": "1546-1696", "issn-l": "1087-0156", "volume": null, "issue": null, "pages": null}, "abstract": "The application of multiple omics technologies in biomedical cohorts has the potential to reveal patient-level disease characteristics and individualized response to treatment. However, the scale and heterogeneous nature of multi-modal data makes integration and inference a non-trivial task. We developed a deep-learning-based framework, multi-omics variational autoencoders (MOVE), to integrate such data and applied it to a cohort of 789 people with newly diagnosed type 2 diabetes with deep multi-omics phenotyping from the DIRECT consortium. Using in silico perturbations, we identified drug-omics associations across the multi-modal datasets for the 20 most prevalent drugs given to people with type 2 diabetes with substantially higher sensitivity than univariate statistical tests. From these, we among others, identified novel associations between metformin and the gut microbiota as well as opposite molecular responses for the two statins, simvastatin and atorvastatin. We used the associations to quantify drug-drug similarities, assess the degree of polypharmacy and conclude that drug effects are distributed across the multi-omics modalities.", "doi": "10.1038/s41587-022-01520-x", "pmid": "36593394", "labels": {"Affinity Proteomics Stockholm": "Collaborative"}, "xrefs": [{"db": "pii", "key": "10.1038/s41587-022-01520-x"}], "notes": [], "created": "2023-02-14T10:11:56.416Z", "modified": "2023-06-19T12:30:42.395Z"}, {"entity": "publication", "iuid": "0f40db40a4a744a193d075e875abdbf1", "links": {"self": {"href": "https://publications.scilifelab.se/publication/0f40db40a4a744a193d075e875abdbf1.json"}, "display": {"href": "https://publications.scilifelab.se/publication/0f40db40a4a744a193d075e875abdbf1"}}, "title": "Genetic Landscape of the ACE2 Coronavirus Receptor.", "authors": [{"family": "Yang", "given": "Zhijian", "initials": "Z", "orcid": "0000-0003-4803-8633", "researcher": {"href": "https://publications.scilifelab.se/researcher/104e44a8c1e949d18d371fad8b2c9fa9.json"}}, {"family": "Macdonald-Dunlop", "given": "Erin", "initials": "E"}, {"family": "Chen", "given": "Jiantao", "initials": "J", "orcid": "0000-0002-7214-2257", "researcher": {"href": "https://publications.scilifelab.se/researcher/3e8d1f3a3d2f499cb948355fa256d5b9.json"}}, {"family": "Zhai", "given": "Ranran", "initials": "R", "orcid": "0000-0002-5834-9120", "researcher": {"href": "https://publications.scilifelab.se/researcher/43424d9c39ae413096249b0abecc08ee.json"}}, {"family": "Li", "given": "Ting", "initials": "T"}, {"family": "Richmond", "given": "Anne", "initials": "A"}, {"family": "Klari\u0107", "given": "Lucija", "initials": "L", "orcid": "0000-0003-3105-8929", "researcher": {"href": "https://publications.scilifelab.se/researcher/4e9259ccc4aa49e8bf6ea82b71d9be0f.json"}}, {"family": "Pirastu", "given": "Nicola", "initials": "N", "orcid": "0000-0002-5363-3886", "researcher": {"href": 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{"family": "Zanetti", "given": "Daniela", "initials": "D", "orcid": "0000-0002-1225-1021", "researcher": {"href": "https://publications.scilifelab.se/researcher/88b1725b56034ad18aa0e4a6cc73126e.json"}}, {"family": "Boutin", "given": "Thibaud", "initials": "T"}, {"family": "Hwang", "given": "Shih-Jen", "initials": "SJ", "orcid": "0000-0002-2129-5704", "researcher": {"href": "https://publications.scilifelab.se/researcher/eb2b949d7dd0449b841f40d6ae246fcb.json"}}, {"family": "Wheeler", "given": "Eleanor", "initials": "E"}, {"family": "Pietzner", "given": "Maik", "initials": "M"}, {"family": "Raffield", "given": "Laura M", "initials": "LM", "orcid": "0000-0002-7892-193X", "researcher": {"href": "https://publications.scilifelab.se/researcher/202a2d2ab8a54b93bcd728d74a7724ce.json"}}, {"family": "Kalnapenkis", "given": "Anette", "initials": "A"}, {"family": "Peters", "given": "James E", "initials": "JE"}, {"family": "Vi\u00f1uela", "given": "Ana", "initials": "A", "orcid": 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{"family": "Hayward", "given": "Caroline", "initials": "C", "orcid": "0000-0002-9405-9550", "researcher": {"href": "https://publications.scilifelab.se/researcher/cd49e9ad5a024c7ca2f1aa97d9e58eba.json"}}, {"family": "Assimes", "given": "Themistocles L", "initials": "TL", "orcid": "0000-0003-2349-0009", "researcher": {"href": "https://publications.scilifelab.se/researcher/9e155ca3dd01486580be888d1ded405c.json"}}, {"family": "Kooperberg", "given": "Charles", "initials": "C", "orcid": "0000-0002-7986-8560", "researcher": {"href": "https://publications.scilifelab.se/researcher/ede9a05559a041fa92259322da5e7151.json"}}, {"family": "Manichaikul", "given": "Ani W", "initials": "AW"}, {"family": "Siegbahn", "given": "Agneta", "initials": "A"}, {"family": "Wallentin", "given": "Lars", "initials": "L"}, {"family": "Lind", "given": "Lars", "initials": "L", "orcid": "0000-0003-2335-8542", "researcher": {"href": "https://publications.scilifelab.se/researcher/4c517dacca7c4ec58a3e03b59ffb4044.json"}}, {"family": "Zeggini", "given": "Eleftheria", "initials": "E"}, {"family": "Schwenk", "given": "Jochen M", "initials": "JM", "orcid": "0000-0001-8141-8449", "researcher": {"href": "https://publications.scilifelab.se/researcher/aba5822711b246b397fffacb7ae403b3.json"}}, {"family": "Butterworth", "given": "Adam S", "initials": "AS", "orcid": "0000-0002-6915-9015", "researcher": {"href": "https://publications.scilifelab.se/researcher/7b8c140c80d942c4b1b684876e4d6180.json"}}, {"family": "Micha\u00eblsson", "given": "Karl", "initials": "K", "orcid": "0000-0003-2815-1217", "researcher": {"href": "https://publications.scilifelab.se/researcher/eff63868e95240f695d47e871e31947f.json"}}, {"family": "Pawitan", "given": "Yudi", "initials": "Y"}, {"family": "Joshi", "given": "Peter K", "initials": "PK", "orcid": "0000-0002-6361-5059", "researcher": {"href": "https://publications.scilifelab.se/researcher/46050ddec8f64054b3bab46c5016aebf.json"}}, {"family": "Baillie", "given": "J Kenneth", "initials": "JK"}, {"family": "M\u00e4larstig", "given": "Anders", "initials": "A"}, {"family": "Reiner", "given": "Alexander P", "initials": "AP", "orcid": "0000-0002-1427-4470", "researcher": {"href": "https://publications.scilifelab.se/researcher/931fcf4444904ec398a450a9ec4f4389.json"}}, {"family": "Wilson", "given": "James F", "initials": "JF"}, {"family": "Shen", "given": "Xia", "initials": "X", "orcid": "0000-0003-4390-1979", "researcher": {"href": "https://publications.scilifelab.se/researcher/b40d1f7f07ed482b9c95f56c61f0a836.json"}}], "type": "journal article", "published": "2022-05-03", "journal": {"title": "Circulation", "issn": "1524-4539", "volume": "145", "issue": "18", "pages": "1398-1411", "issn-l": "0009-7322"}, "abstract": "SARS-CoV-2, the causal agent of COVID-19, enters human cells using the ACE2 (angiotensin-converting enzyme 2) protein as a receptor. ACE2 is thus key to the infection and treatment of the coronavirus. ACE2 is highly expressed in the heart and respiratory and gastrointestinal tracts, playing important regulatory roles in the cardiovascular and other biological systems. However, the genetic basis of the ACE2 protein levels is not well understood.\n\nWe have conducted the largest genome-wide association meta-analysis of plasma ACE2 levels in >28 000 individuals of the SCALLOP Consortium (Systematic and Combined Analysis of Olink Proteins). We summarize the cross-sectional epidemiological correlates of circulating ACE2. Using the summary statistics-based high-definition likelihood method, we estimate relevant genetic correlations with cardiometabolic phenotypes, COVID-19, and other human complex traits and diseases. We perform causal inference of soluble ACE2 on vascular disease outcomes and COVID-19 severity using mendelian randomization. We also perform in silico functional analysis by integrating with other types of omics data.\n\nWe identified 10 loci, including 8 novel, capturing 30% of the heritability of the protein. We detected that plasma ACE2 was genetically correlated with vascular diseases, severe COVID-19, and a wide range of human complex diseases and medications. An X-chromosome cis-protein quantitative trait loci-based mendelian randomization analysis suggested a causal effect of elevated ACE2 levels on COVID-19 severity (odds ratio, 1.63 [95% CI, 1.10-2.42]; P=0.01), hospitalization (odds ratio, 1.52 [95% CI, 1.05-2.21]; P=0.03), and infection (odds ratio, 1.60 [95% CI, 1.08-2.37]; P=0.02). Tissue- and cell type-specific transcriptomic and epigenomic analysis revealed that the ACE2 regulatory variants were enriched for DNA methylation sites in blood immune cells.\n\nHuman plasma ACE2 shares a genetic basis with cardiovascular disease, COVID-19, and other related diseases. The genetic architecture of the ACE2 protein is mapped, providing a useful resource for further biological and clinical studies on this coronavirus receptor.", "doi": "10.1161/CIRCULATIONAHA.121.057888", "pmid": "35387486", "labels": {"National Genomics Infrastructure": "Service", "NGI SNP genotyping": "Service", "NGI Uppsala (SNP&SEQ Technology Platform)": "Service", "Affinity Proteomics Stockholm": "Service"}, "xrefs": [{"db": "pmc", "key": "PMC9047645"}], "notes": [], "created": "2022-05-06T10:18:12.729Z", "modified": "2022-12-05T01:33:32.862Z"}, {"entity": "publication", "iuid": "e81411b4717b4690acc189ae0314d13d", "links": {"self": {"href": "https://publications.scilifelab.se/publication/e81411b4717b4690acc189ae0314d13d.json"}, "display": {"href": "https://publications.scilifelab.se/publication/e81411b4717b4690acc189ae0314d13d"}}, "title": "Predicting and elucidating the etiology of fatty liver disease: A machine learning modeling and validation study in the IMI DIRECT cohorts.", 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{"href": "https://publications.scilifelab.se/researcher/a51bbc9e2ca34b75ba75e6b3be85da87.json"}}, {"family": "Jones", "given": "Angus G", "initials": "AG", "orcid": "0000-0002-0883-7599", "researcher": {"href": "https://publications.scilifelab.se/researcher/e61710036445440ea18c4cc05bbd8a0b.json"}}, {"family": "Thomas", "given": "E Louise", "initials": "EL", "orcid": "0000-0003-4235-4694", "researcher": {"href": "https://publications.scilifelab.se/researcher/186f6dee4a8a4e199e519199a62d4918.json"}}, {"family": "Koivula", "given": "Robert W", "initials": "RW", "orcid": "0000-0002-1646-4163", "researcher": {"href": "https://publications.scilifelab.se/researcher/29fa233507ea4e6190e70953cbb743a8.json"}}, {"family": "Kurbasic", "given": "Azra", "initials": "A", "orcid": "0000-0002-1910-2619", "researcher": {"href": "https://publications.scilifelab.se/researcher/64a6b0e38fdc43f59725c6013e9ffd75.json"}}, {"family": "Mutie", "given": "Pascal M", "initials": "PM"}, {"family": "Fitipaldi", 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"researcher": {"href": "https://publications.scilifelab.se/researcher/ad86129a6eec49f1b0fe4ce09643c8a1.json"}}, {"family": "Mahajan", "given": "Anubha", "initials": "A", "orcid": "0000-0001-5585-3420", "researcher": {"href": "https://publications.scilifelab.se/researcher/194be8a851164e2ea4d004dd2febc9be.json"}}, {"family": "McEvoy", "given": "Donna", "initials": "D", "orcid": "0000-0003-1546-5567", "researcher": {"href": "https://publications.scilifelab.se/researcher/c4455f5ace534325ba30494630686066.json"}}, {"family": "Pattou", "given": "Francois", "initials": "F"}, {"family": "Raverdy", "given": "Violeta", "initials": "V"}, {"family": "H\u00e4ussler", "given": "Ragna S", "initials": "RS", "orcid": "0000-0003-1664-8875", "researcher": {"href": "https://publications.scilifelab.se/researcher/ca04d9b9132747efb7db0efb6e34756a.json"}}, {"family": "Sharma", "given": "Sapna", "initials": "S"}, {"family": "Thomsen", "given": "Henrik S", "initials": "HS"}, {"family": "Vangipurapu", "given": "Jagadish", "initials": "J", "orcid": "0000-0001-6657-2659", "researcher": {"href": "https://publications.scilifelab.se/researcher/cc3c64aafa4841729040479765aae6f2.json"}}, {"family": "Vestergaard", "given": "Henrik", "initials": "H", "orcid": "0000-0003-3090-269X", "researcher": {"href": "https://publications.scilifelab.se/researcher/4a79ddd7842e480f85b0a303125fdc9a.json"}}, {"family": "'t Hart", "given": "Leen M", "initials": "LM", "orcid": "0000-0003-4401-2938", "researcher": {"href": "https://publications.scilifelab.se/researcher/3cb1193c2ad3419899ec86d9d95bf25d.json"}}, {"family": "Adamski", "given": "Jerzy", "initials": "J"}, {"family": "Musholt", "given": "Petra B", "initials": "PB"}, {"family": "Brage", "given": "Soren", "initials": "S", "orcid": "0000-0002-1265-7355", "researcher": {"href": "https://publications.scilifelab.se/researcher/2edeab82aa9f4513baa3dc724c0a4a88.json"}}, {"family": "Brunak", "given": "S\u00f8ren", "initials": "S"}, {"family": "Dermitzakis", "given": "Emmanouil", "initials": "E"}, {"family": "Frost", "given": "Gary", "initials": "G", "orcid": "0000-0003-0529-6325", "researcher": {"href": "https://publications.scilifelab.se/researcher/b9312f44496b4bdb86ad9c5096aeb94c.json"}}, {"family": "Hansen", "given": "Torben", "initials": "T", "orcid": "0000-0001-8748-3831", "researcher": {"href": "https://publications.scilifelab.se/researcher/da403496660346079fc41975cae418d9.json"}}, {"family": "Laakso", "given": "Markku", "initials": "M"}, {"family": "Pedersen", "given": "Oluf", "initials": "O"}, {"family": "Ridderstr\u00e5le", "given": "Martin", "initials": "M"}, {"family": "Ruetten", "given": "Hartmut", "initials": "H"}, {"family": "Hattersley", "given": "Andrew T", "initials": "AT", "orcid": "0000-0001-5620-473X", "researcher": {"href": "https://publications.scilifelab.se/researcher/25dad832cfe041a79e35170e789e66c7.json"}}, {"family": "Walker", "given": "Mark", "initials": "M"}, {"family": "Beulens", "given": "Joline W J", "initials": "JWJ"}, {"family": "Mari", "given": "Andrea", "initials": "A", "orcid": "0000-0002-1436-5591", "researcher": {"href": "https://publications.scilifelab.se/researcher/c8b959c8980a4d108b602d82cd345ba6.json"}}, {"family": "Schwenk", "given": "Jochen M", "initials": "JM", "orcid": "0000-0001-8141-8449", "researcher": {"href": "https://publications.scilifelab.se/researcher/aba5822711b246b397fffacb7ae403b3.json"}}, {"family": "Gupta", "given": "Ramneek", "initials": "R", "orcid": "0000-0001-6841-6676", "researcher": {"href": "https://publications.scilifelab.se/researcher/445ec9fcd4a740a385550d10d19e8317.json"}}, {"family": "McCarthy", "given": "Mark I", "initials": "MI", "orcid": "0000-0002-4393-0510", "researcher": {"href": "https://publications.scilifelab.se/researcher/5e44f4b1ca3a49cb8f83450ef9482e91.json"}}, {"family": "Pearson", "given": "Ewan R", "initials": "ER", "orcid": "0000-0001-9237-8585", "researcher": {"href": "https://publications.scilifelab.se/researcher/320eb6ae20014620ad8f69c3f7b216b3.json"}}, {"family": "Bell", "given": "Jimmy D", "initials": "JD", "orcid": "0000-0003-3804-1281", "researcher": {"href": "https://publications.scilifelab.se/researcher/74a9ddf1d40e4b4fb1614a89a220e143.json"}}, {"family": "Pavo", "given": "Imre", "initials": "I"}, {"family": "Franks", "given": "Paul W", "initials": "PW", "orcid": "0000-0002-0520-7604", "researcher": {"href": "https://publications.scilifelab.se/researcher/1ebbf40c0f7e49dd8c75a2b6cbf27276.json"}}], "type": "journal article", "published": "2020-06-00", "journal": {"title": "PLoS Med.", "issn": "1549-1676", "issn-l": "1549-1277", "volume": "17", "issue": "6", "pages": "e1003149"}, "abstract": "Non-alcoholic fatty liver disease (NAFLD) is highly prevalent and causes serious health complications in individuals with and without type 2 diabetes (T2D). Early diagnosis of NAFLD is important, as this can help prevent irreversible damage to the liver and, ultimately, hepatocellular carcinomas. We sought to expand etiological understanding and develop a diagnostic tool for NAFLD using machine learning.\n\nWe utilized the baseline data from IMI DIRECT, a multicenter prospective cohort study of 3,029 European-ancestry adults recently diagnosed with T2D (n = 795) or at high risk of developing the disease (n = 2,234). Multi-omics (genetic, transcriptomic, proteomic, and metabolomic) and clinical (liver enzymes and other serological biomarkers, anthropometry, measures of beta-cell function, insulin sensitivity, and lifestyle) data comprised the key input variables. The models were trained on MRI-image-derived liver fat content (<5% or \u22655%) available for 1,514 participants. We applied LASSO (least absolute shrinkage and selection operator) to select features from the different layers of omics data and random forest analysis to develop the models. The prediction models included clinical and omics variables separately or in combination. A model including all omics and clinical variables yielded a cross-validated receiver operating characteristic area under the curve (ROCAUC) of 0.84 (95% CI 0.82, 0.86; p < 0.001), which compared with a ROCAUC of 0.82 (95% CI 0.81, 0.83; p < 0.001) for a model including 9 clinically accessible variables. The IMI DIRECT prediction models outperformed existing noninvasive NAFLD prediction tools. One limitation is that these analyses were performed in adults of European ancestry residing in northern Europe, and it is unknown how well these findings will translate to people of other ancestries and exposed to environmental risk factors that differ from those of the present cohort. Another key limitation of this study is that the prediction was done on a binary outcome of liver fat quantity (<5% or \u22655%) rather than a continuous one.\n\nIn this study, we developed several models with different combinations of clinical and omics data and identified biological features that appear to be associated with liver fat accumulation. In general, the clinical variables showed better prediction ability than the complex omics variables. However, the combination of omics and clinical variables yielded the highest accuracy. We have incorporated the developed clinical models into a web interface (see: https://www.predictliverfat.org/) and made it available to the community.\n\nClinicalTrials.gov NCT03814915.", "doi": "10.1371/journal.pmed.1003149", "pmid": "32559194", "labels": {"Affinity Proteomics Stockholm": "Collaborative"}, "xrefs": [{"db": "pii", "key": "PMEDICINE-D-20-00136"}, {"db": "pmc", "key": "PMC7304567"}, {"db": "ClinicalTrials.gov", "key": "NCT03814915"}], "notes": [], "created": "2020-12-10T19:03:48.331Z", "modified": "2021-11-10T12:50:35.188Z"}]}