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Population-scale tissue transcriptomics maps long non-coding RNAs to complex disease
Olivia M. de Goede, Daniel Nachun, Nicole M. Ferraro, Michael J. Gloudemans, Abhiram Rao, Craig Smail, Tiffany Eulalio, François Aguet, Bernard Ng, Jishu Xu, Alvaro Barbeira, Stephane E. Castel, Sarah Kim-Hellmuth, YoSon Park, Alexandra J. Scott, Benjamin J. Strober, Shankara Anand, Stacey Gabriel, Gad Getz, Aaron Graubert, Kane Hadley, Robert E. Handsaker, Katherine Huang, Xiao Li, Daniel G. MacArthur, Samuel R. Meier, Jared L. Nedzel, Duyen T. Nguyen, Ayellet V. Segrè, Ellen Todres, Brunilda Balliu, Rodrigo Bonazzola, Andrew Brown, Donald F. Conrad, Daniel J. Cotter, Nancy J. Cox, Sayantan Das, Emmanouil T. Dermitzakis, Jonah Einson, Barbara E. Engelhardt, Eleazar Eskin, Elise D. Flynn, Laure Frésard, Eric R. Gamazon, Diego Garrido-Martín, Nicole R. Gay, Roderic Guigó, Andrew R. Hamel, Yuan He, Paul Hoffman, Farhad Hormozdiari, Lei Hou, Brian Jo, Silva Kasela, Seva Kashin, Manolis Kellis, Alan Kwong, Xin Li, Yanyu Liang, Serghei Mangul, Pejman Mohammadi, Manuel Muñoz-Aguirre, Andrew B. Nobel, Meritxell Oliva, Yong‐Jin Park, Princy Parsana, Ferrán Reverter, John M. Rouhana, Chiara Sabatti, Ashis Saha, Matthew Stephens, Barbara E. Stranger, Nicole A. Teran, Ana Viñuela, Gao Wang, Fred A. Wright, Valentin Wucher, Yuxin Zou, Pedro G. Ferreira, Gen Li, Marta Melé, Esti Yeger‐Lotem, Debra Bradbury, Tanya Krubit, Jeffrey A. McLean, Liqun Qi, Karna Robinson, Nancy Roche, Anna M. Smith, David E. Tabor, Anita H. Undale, Jason Bridge, Lori E. Brigham, Barbara A. Foster, Bryan M. Gillard, Richard Hasz, Marcus Hunter, Christopher Johns, Mark R. Johnson, Ellen Karasik, Gene Kopen, William F. Leinweber, Alisa McDonald, Michael T. Moser, Kevin Myer, Kimberley D. Ramsey, Brian Roe, Saboor Shad, Jeffrey A. Thomas, Gary Walters, Michael Washington, J. Gary Wheeler, Scott D. Jewell, Daniel C. Rohrer, Dana R. Valley, David A. Davis, Deborah C. Mash, Mary E. Barcus, Philip A. Branton, Leslie H. Sobin, Laura K. Barker, Heather M. Gardiner, Maghboeba Mosavel, Laura A. Siminoff, Paul Flicek, Maximilian Haeussler, Thomas Juettemann, W. James Kent, Christopher M. Lee, Conner C. Powell, Kate R. Rosenbloom, Magali Ruffier, Dan Sheppard, Kieron Taylor, Stephen J. Trevanion, Daniel R. Zerbino, Nathan S. Abell, Joshua M. Akey, Lin Chen, Kathryn Demanelis, Jennifer A. Doherty, Andrew P. Feinberg, Kasper D. Hansen, Peter F. Hickey, Farzana Jasmine, Lihua Jiang, Rajinder Kaul, Muhammad G. Kibriya, Jin Billy Li, Qin Li, Shin Lin, Sandra E. Linder, Brandon L. Pierce, Lindsay F. Rizzardi, Andrew D. Skol, Kevin S. Smith, M Snyder, J Stamatoyannopoulos, Hua Tang, Meng Wang, Latarsha J. Carithers, Ping Guan, Susan E. Koester, A. Roger Little, Helen M. Moore, Concepcion R. Nierras, Abhi K. Rao, Jimmie B. Vaught, Simona Volpi, Christopher D. Brown, Xiaoquan Wen, Ira M. Hall, Alexis Battle, Tuuli Lappalainen, Hae Kyung Im, Kristin Ardlie, Sara Mostafavi, Thomas Quertermous, Karla Kirkegaard, Stephen B. Montgomery
Bibliographic record
VenueCell · 2021
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCancer-related molecular mechanisms research
Canadian institutionsPacific Centre for Reproductive MedicineUniversity of British Columbia
FundersNational Human Genome Research InstituteNational Institute of Neurological Disorders and StrokeNational Institute of General Medical SciencesNational Institute of Mental HealthNational Institute of Diabetes and Digestive and Kidney DiseasesNational Heart, Lung, and Blood InstituteU.S. National Library of MedicineNational Institute on AgingNational Institutes of Health
KeywordsBiologyGeneTraitQuantitative trait locusGeneticsTranscriptomeExpression quantitative trait lociGenome-wide association studyComputational biologyDiseasePopulationGene expressionGenetic associationLong non-coding RNARNAGenotypeSingle-nucleotide polymorphism
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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.618
Threshold uncertainty score0.709
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 head0.014
GPT teacher head0.275
Teacher spread0.262 · 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 designBench or experimental
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
Citations212
Published2021
Admission routes1
Has abstractno