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Refining the accuracy of validated target identification through coding variant fine-mapping in type 2 diabetes
Anubha Mahajan, Jennifer Wessel, Sara M. Willems, Wei Zhao, Neil R. Robertson, Audrey Y. Chu, Wei Gan, Hidetoshi Kitajima, Daniel Taliun, Nigel W. Rayner, Xiuqing Guo, Yingchang Lu, Man Li, Richard A. Jensen, Yao Hu, Shaofeng Huo, Kurt K. Lohman, Weihua Zhang, James P. Cook, Bram P. Prins, Jason Flannick, Niels Grarup, Vassily Trubetskoy, Jasmina Kravić, Young Jin Kim, Denis Rybin, Hanieh Yaghootkar, Martina Müller‐Nurasyid, Karina Meidtner, Ruifang Li‐Gao, Tibor V. Varga, Jonathan Marten, Jin Li, Albert V. Smith, Ping An, Symen Ligthart, Stefan Gustafsson, Giovanni Malerba, Ayşe Demirkan, Juan Fernández Tajes, Valgerður Steinthórsdóttir, Matthias Wuttke, Michael Preuß, Lawrence F. Bielak, Marielisa Graff, Heather M. Highland, Anne E. Justice, Dajiang J. Liu, Eirini Marouli, Gina M. Peloso, Helen Warren, Saima Afaq, Shoaib Afzal, Emma Ahlqvist, Peter Almgren, Najaf Amin, Lia B. Bang, Alain G. Bertoni, Cristina Bombieri, Jette Bork‐Jensen, Ivan Brandslund, Jennifer A. Brody, Noël P. Burtt, Mickaël Canouil, Yii‐Der Ida Chen, Yoon Shin Cho, Cramer Christensen, Sophie V. Eastwood, Kai‐Uwe Eckardt, Krista Fischer, Giovanni Gambaro, Vilmantas Giedraitis, Megan L. Grove, Hugoline G. de Haan, Sophie Hackinger, Yang Hai, Sohee Han, Anne Tybjærg‐Hansen, Marie‐France Hivert, Bo Isomaa, Susanne Jäger, Marit E. Jørgensen, Torben Jørgensen, Annemari Käräjämäki, Bong-Jo Kim, Sung Soo Kim, Heikki A. Koistinen, Péter Kovács, Jennifer Kriebel, Florian Kronenberg, Kristi Läll, Leslie A. Lange, Jung‐Jin Lee, Benjamin Lehne, Huaixing Li, Keng‐Hung Lin, Allan Linneberg, Ching‐Ti Liu, Jun Liu, Marie Loh, Reedik Mägi, Vasiliki Mamakou, Roberta McKean‐Cowdin, Girish N. Nadkarni, Matt J. Neville, Sune F. Nielsen, Ιωάννα Ντάλλα, Patricia A. Peyser, Wolfgang Rathmann, Kenneth Rice, Stephen S. Rich, Line Rode, Olov Rolandsson, Sebastian Schönherr, Elizabeth Selvin, Kerrin S. Small, Alena Stančáková, Praveen Surendran, Kent D. Taylor, Tanya M. Teslovich, Barbara Thorand, Guðmar Þorleifsson, Adrienne Tin, Anke Tönjes, Anette Varbo, Daniel R. Witte, Andrew R. Wood, Pranav Yajnik, Jie Yao, Loïc Yengo, Robin Young, Philippe Amouyel, Heiner Boeing, Eric Boerwinkle, Erwin P. Böttinger, Rajiv Chowdhury, Francis S. Collins, George Dedoussis, Abbas Dehghan, Panos Deloukas, Maurizio Ferrario, Jean Ferrières, José C. Florez, Philippe Frossard, Vilmundur Guðnason, Tamara B. Harris, Susan R. Heckbert, Joanna M. M. Howson, Martin Ingelsson, Sekar Kathiresan, Frank Kee, Johanna Kuusisto, Claudia Langenberg, Lenore J. Launer, Cecilia M. Lindgren, Satu Männistö, Thomas Meitinger, Olle Melander, Karen L. Mohlke, Marie Moitry, Andrew D. Morris, Alison D. Murray, Renée de Mutsert, Marju Orho‐Melander, Katharine R. Owen, Markus Perola, Annette Peters, Michael A. Province, Asif Rasheed, Paul M. Ridker, Fernando Rivadineira, Frits R. Rosendaal, Anders H. Rosengren, Veikko Salomaa, Wayne H.-H. Sheu, Robert Sladek, Blair H. Smith, Konstantin Strauch, André G. Uitterlinden, Rohit Varma, Cristen J. Willer, Matthias Blüher, Adam S. Butterworth, John C. Chambers, Daniel I. Chasman, John Danesh, Cornelia M. van Duijn, Josée Dupuis, Oscar H. Franco, Paul W. Franks, Philippe Froguel, Harald Grallert, Leif Groop, Bok‐Ghee Han, Torben Hansen, Andrew T. Hattersley, Caroline Hayward, Erik Ingelsson, Sharon L. R. Kardia, Fredrik Karpe, Jaspal S. Kooner, Anna Köttgen, Kari Kuulasmaa, Markku Laakso, Lin Xu, Lars Lind, Ruth J. F. Loos, Jonathan Marchini, Andres Metspalu, Dennis O. Mook‐Kanamori, Børge G. Nordestgaard, James S. Pankow, Oluf Pedersen, Bruce M. Psaty, Rainer Rauramaa, Naveed Sattar, Matthias B. Schulze, Nicole Soranzo, Timothy D. Spector, Kāri Stefánsson, Michael Stümvoll, Unnur Þorsteinsdóttir, Jaakko Tuomilehto, Nicholas J. Wareham, James Wilson, Eleftheria Zeggini, Robert A. Scott, Inês Barroso, Timothy M. Frayling, Mark O. Goodarzi, James B. Meigs, Michael Boehnke, Danish Saleheen, Andrew P. Morris, Jerome I. Rotter, Mark I. McCarthy
Bibliographic record
VenueNature Genetics · 2018
Typereview
Languageen
FieldComputer Science
TopicAI in cancer detection
Canadian institutionsMcGill UniversityMcGill University and Génome Québec Innovation CentreUniversité de Sherbrooke
FundersNational Institute of General Medical SciencesNational Heart, Lung, and Blood InstituteNIHR Cambridge Biomedical Research CentreHelmholtz Zentrum MünchenKorea Centers for Disease Control and PreventionKorea National Institute of HealthNational Center for Advancing Translational SciencesMedical Research CouncilMünchner Zentrum für GesundheitswissenschaftenErasmus Medisch CentrumGenentechNederlandse Organisatie voor Wetenschappelijk OnderzoekCenters for Disease Control and PreventionRegeneron PharmaceuticalsNational Institutes of HealthHjartaverndNovo Nordisk FondenEli Lilly and CompanyLundbeckfondenZonMwBritish Heart FoundationEuropean CommissionSteno Diabetes Center CopenhagenWellcome TrustBundesministerium für Bildung und ForschungNational Institute on AgingNational Institute for Health and Care ResearchNational Institute of Diabetes and Digestive and Kidney DiseasesSanofiGlaxoSmithKlinePfizerIncyteYale University
KeywordsBiologyComputational biologyIdentification (biology)Coding (social sciences)Refining (metallurgy)Type 2 diabetesGeneticsDiabetes mellitusStatisticsEndocrinologyMathematics
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 machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.004
Threshold uncertainty score0.020
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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 head0.059
GPT teacher head0.345
Teacher spread0.285 · 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 source (direct Gemma or distilled Codex), not a consensus.
The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreReview
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".
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Citations470
Published2018
Admission routes1
Has abstractno