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Genome-wide association study and functional characterization identifies candidate genes for insulin-stimulated glucose uptake
Alice Williamson, Dougall M. Norris, Xianyong Yin, K. Alaine Broadaway, Anne H. Moxley, Swarooparani Vadlamudi, Emma P Wilson, Anne Jackson, Vasudha Ahuja, Mette K. Andersen, Zorayr Arzumanyan, Lori L. Bonnycastle, Stefan R. Bornstein, Maxi Pia Bretschneider, Thomas A. Buchanan, Yi‐Cheng Chang, Lee‐Ming Chuang, Ren‐Hua Chung, Tine D. Clausen, Peter Damm, Graciela E. Delgado, Vanessa D. de Mello, Josée Dupuis, Om Prakash Dwivedi, Michael R. Erdos, Lilian Fernandes Silva, Timothy M. Frayling, Christian Gieger, Mark O. Goodarzi, Xiuqing Guo, Stefan Gustafsson, Liisa Hakaste, Ulf Hammar, Gad Hatem, Sandra M. Herrmann, Kurt Højlund, Katrin Horn, Willa A. Hsueh, Yi‐Jen Hung, Chii‐Min Hwu, Anna Jonsson, Line Lund Kårhus, Marcus E. Kleber, Péter Kovács, Timo A. Lakka, Marie Lauzon, I‐Te Lee, Cecilia M. Lindgren, Jaana Lindström, Allan Linneberg, Ching‐Ti Liu, Jian’an Luan, Dina Mansour Aly, Elisabeth R. Mathiesen, Angela P. Moissl, Andrew P. Morris, Narisu Narisu, Nikolaos Perakakis, Annette Peters, Rashmi B. Prasad, Roman N. Rodionov, Kathryn Roll, Carsten F. Rundsten, Chloé Sarnowski, Kai Savonen, Markus Scholz, Sapna Sharma, Sara Stinson, Sufyan Suleman, Jingyi Tan, Kent D. Taylor, Matti Uusitupa, Dorte Vistisen, Daniel R. Witte, Romy Walther, Peitao Wu, Anny H. Xiang, Björn Zethelius, Emma Ahlqvist, Richard N. Bergman, Yii‐Der Ida Chen, Francis S. Collins, Tove Fall, José C. Florez, Andreas Fritsche, Harald Grallert, Leif Groop, Torben Hansen, Heikki A. Koistinen, Pirjo Komulainen, Markku Laakso, Lars Lind, Markus Loeffler, Winfried März, James B. Meigs, Leslie J. Raffel, Rainer Rauramaa, Jerome I. Rotter, Peter E. H. Schwarz, Michael Stümvoll, Johan Sundström, Anke Tönjes, Jaakko Tuomilehto, Róbert Wágner, Inês Barroso, Mark Walker, Niels Grarup, Michael Boehnke, Nicholas J. Wareham, Karen L. Mohlke, Eleanor Wheeler, Stephen O’Rahilly, Daniel J. Fazakerley, Claudia Langenberg
Bibliographic record
VenueNature Genetics · 2023
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetic Associations and Epidemiology
Canadian institutionsMcGill University
FundersSteno Diabetes Center AarhusCommon FundNational Institute of Neurological Disorders and StrokeAbbott DiagnosticsBayerNational Cancer InstituteQuest DiagnosticsNational Institute on AgingBayer VitalNational Institutes of HealthNIH Office of the DirectorNovo Nordisk FondenSteno Diabetes Center CopenhagenDanoneNational Institute of General Medical SciencesNational Institute on Drug AbuseNovo NordiskNational Human Genome Research InstituteNational Institute of Diabetes and Digestive and Kidney DiseasesSanofiNational Center for Advancing Translational SciencesMedical Research CouncilAstraZenecaNational Institute of Mental HealthNational Heart, Lung, and Blood InstitutePfizerAmgen
KeywordsBiologyGenome-wide association studyCandidate geneGeneGeneticsGenetic associationInsulinGenomeComputational biologySingle-nucleotide polymorphismBiotechnologyGenotype
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)0.001
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 score0.200
Threshold uncertainty score0.760
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.001 | 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 head0.012
GPT teacher head0.265
Teacher spread0.253 · 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
Citations74
Published2023
Admission routes1
Has abstractno