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Genome-wide association analyses define pathogenic signaling pathways and prioritize drug targets for IgA nephropathy
Krzysztof Kiryluk, Elena Sánchez, Shu‐Feng Zhou, Francesca Zanoni, Lili Liu, Nikol Mladkova, Atlas Khan, Maddalena Marasà, Jun Y. Zhang, Olivia Balderes, Simone Sanna‐Cherchi, Andrew S. Bomback, Pietro A. Canetta, Gerald B. Appel, Jai Radhakrishnan, Hernán Trimarchi, Ben Sprangers, Daniel Cattran, Heather N. Reich, York Pei, Pietro Ravani, Kres̆imir Gales̃ić, Dita Maixnerová, Vladimı́r Tesař, Bénédicte Stengel, Marie Metzger, Guillaume Canaud, Nicolas Maillard, F. Berthoux, Laureline Berthelot, Évangéline Pillebout, Renato C. Monteiro, Raoul D. Nelson, Robert Wyatt, William E. Smoyer, John D. Mahan, Al-Akash Samhar, Guillermo Hidalgo, Alejandro Quiroga, Patricia L. Weng, Raji Sreedharan, David T. Selewski, Keefe Davis, Mahmoud Kallash, Tetyana L. Vasylyeva, Michelle N. Rheault, Aftab S. Chishti, Daniel Ranch, Scott E. Wenderfer, Dmitry Samsonov, Donna Claes, Oleh M. Akchurin, Dimitrios Goumenos, Μaria Stangou, Judit Nagy, Tibor Kovács, Enrico Fiaccadori, Antonio Amoroso, Cristina Barlassina, Daniele Cusi, Lucia Del Vecchio, Giovanni Giorgio Battaglia, Monica Bodria, Emanuela Boer, Luisa Bono, Giuliano Boscutti, Gianluca Caridi, Francesca Lugani, Gian Marco Ghiggeri, Rosanna Coppo, Licia Peruzzi, Vittoria Esposito, Ciro Esposito, Sandro Feriozzi, Rosaria Polci, Giovanni M. Frascà, Marco Galliani, Maurizio Garozzo, Adele Mitrotti, Loreto Gesualdo, Simona Granata, Gianluigi Zaza, Francesco Londrino, Riccardo Magistroni, Isabella Pisani, Andrea Magnano, Carmelita Marcantoni, Piergiorgio Messa, Renzo Mignani, Antonello Pani, Claudio Ponticelli, Dario Roccatello, Maurizio Salvadori, Erica Salvi, Domenico Santoro, Guido Gembillo, Silvana Savoldi, Donatella Spotti, Pasquale Zamboli, Claudia Izzi, Federico Alberici, Elisa Delbarba, Michał Florczak, Natalia Krata, Krzysztof Mucha, Leszek Pączek, Stanisław Niemczyk, Barbara Moszczuk, Małgorzata Pańczyk-Tomaszewska, Małgorzata Mizerska-Wasiak, Agnieszka Perkowska‐Ptasińska, Teresa Bączkowska, Magdalena Durlik, Krzysztof Pawlaczyk, Przemysław Sikora, Marcin Zaniew, Dorota Kamińska, Magdalena Krajewska, Izabella Kuźmiuk-Glembin, Zbigniew Heleniak, Barbara Bułło‐Piontecka, Tomasz Liberek, Alicja Dębska‐Ślizień, Tomasz Hryszko, Anna Materna‐Kiryluk, Monika Miklaszewska, Katarzyna Dyga, Edyta Machura, Katarzyna Siniewicz‐Luzeńczyk, Monika Pawlak-Bratkowska, Marcin Tkaczyk, Dariusz Runowski, Norbert Kwella, Dorota Drożdż, Ireneusz Habura, Florian Kronenberg, Larisa Prikhodina, David A. van Heel, Bertrand Fontaine, Chris Cotsapas, Cisca Wijmenga, André Franke, Vito Annese, Peter K. Gregersen, Sreeja Parameswaran, Matthew T. Weirauch, Leah C. Kottyan, John B. Harley, Hitoshi Suzuki, Ichiei Narita, Hajeong Lee, Dong Ki Kim, Yon Su Kim, Jin‐Ho Park, Belong Cho, Murim Choi, Ans Van Wijk, Ana Huerta, Elisabet Ars, José Ballarín, Sigrid Lundberg, Bruno Vogt, Laila‐Yasmin Mani, Yaşar Çalışkan, Jonathan Barratt, Thilini Abeygunaratne, Philip A. Kalra, Daniel P. Gale, Ulf Panzer, Thomas Rauen, Jürgen Floege, Pascal Schlosser, Arif B. Ekici, Kai‐Uwe Eckardt, Nan Chen, Jingyuan Xie, Richard P. Lifton, Ruth J. F. Loos, Eimear E. Kenny, Iuliana Ionita‐Laza, Anna Köttgen, Bruce A. Julian, Jan Novák, Francesco Scolari, Hong Zhang, Ali G. Gharavi
Bibliographic record
VenueNature Genetics · 2023
Typearticle
Languageen
FieldMedicine
TopicRenal Diseases and Glomerulopathies
Canadian institutionsUniversity of CalgaryToronto General HospitalUniversity of Toronto
FundersNational Human Genome Research InstituteNational Institute of Allergy and Infectious DiseasesNational Center for Advancing Translational SciencesMedical Research CouncilKfH-Stiftung PräventivmedizinGilead SciencesUniwersytet Medyczny im. Karola Marcinkowskiego w PoznaniuFresenius Medical Care North AmericaNational Natural Science Foundation of ChinaDeutsche ForschungsgemeinschaftU.S. National Library of MedicineNational Institute of Neurological Disorders and StrokeKidney Research UKNational Institute of General Medical SciencesBundesministerium für Bildung und ForschungAlbert-Ludwigs-Universität FreiburgMedizinische Fakultät der Albert-Ludwigs-Universität FreiburgNateraNational Institute of Arthritis and Musculoskeletal and Skin DiseasesNational Institute of Diabetes and Digestive and Kidney DiseasesVšeobecná Fakultní Nemocnice v PrazeAmgenPirogov Russian National Research Medical UniversityU.S. Department of Veterans AffairsU.S. Department of Health and Human ServicesNational Science Foundation
KeywordsBiologyGenome-wide association studyComputational biologyNephropathyGeneticsGenetic associationGenomeDrug discoveryDrugBioinformaticsGeneGenotypeSingle-nucleotide polymorphismPharmacology
Abstract
No abstract in any covered source. Its absence is recorded, not treated as a negative.
No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.354
Threshold uncertainty score0.774
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
Opus teacher head0.019
GPT teacher head0.291
Teacher spread0.272 · how far apart the two teachers sit on this one work
Validation status
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".
Quick stats
Citations172
Published2023
Admission routes1
Has abstractno