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Genomic and phenotypic insights from an atlas of genetic effects on DNA methylation
Josine L. Min, Gibran Hemani, Eilís Hannon, Koen F. Dekkers, Juan Castillo‐Fernandez, René Luijk, Elena Carnero‐Montoro, Daniel J. Lawson, Kimberley Burrows, Matthew Suderman, Andrew D. Bretherick, Tom G. Richardson, Johanna Klughammer, Valentina Iotchkova, Gemma C. Sharp, Ahmad Al Khleifat, Aleksey Shatunov, Alfredo Iacoangeli, Wendy L. McArdle, Karen Ho, Ashish Kumar, Cilla Söderhäll, Carolina Soriano‐Tárraga, Eva Giralt‐Steinhauer, Nabila Kazmi, Dan Mason, Allan F. McRae, David L. Corcoran, Karen Sugden, Silva Kasela, Alexia Cardona, Felix R. Day, Giovanni Cugliari, Clara Viberti, Simonetta Guarrera, Michael C. Lerro, Richa Gupta, Sailalitha Bollepalli, Pooja R. Mandaviya, Yanni Zeng, Toni‐Kim Clarke, Rosie M. Walker, Vanessa Schmoll, Darina Czamara, Carlos Ruiz-Arenas, Faisal I. Rezwan, Riccardo E. Marioni, Tian Lin, Yvonne Awaloff, Marine Germain, Dylan Aïssi, Ramona Zwamborn, Kristel van Eijk, Annelot M. Dekker, Jenny van Dongen, Jouke‐Jan Hottenga, Gonneke Willemsen, Cheng‐Jian Xu, Guillermo Barturen, Francesc Català‐Moll, Martin Kerick, Carol A. Wang, Phillip E. Melton, Hannah R. Elliott, Jean Shin, Manon Bernard, İdil Yet, Melissa Smart, T.J. Gorrie-Stone, Chris Shaw, Ammar Al‐Chalabi, Susan M. Ring, Göran Pershagen, Erik Melén, Jordi Jiménez‐Conde, Jaume Roquer, Debbie A. Lawlor, John Wright, Nicholas G. Martin, Grant W. Montgomery, Terrie E. Moffitt, Richie Poulton, Tõnu Esko, Lili Milani, Andres Metspalu, John R. B. Perry, Ken K. Ong, Nicholas J. Wareham, Giuseppe Matullo, Carlotta Sacerdote, Salvatore Panico, Avshalom Caspi, Louise Arseneault, France Gagnon, Miina Ollikainen, Jaakko Kaprio, Janine F. Felix, Fernando Rivadeneira, Henning Tiemeier, Marinus H. van IJzendoorn, André G. Uitterlinden, Vincent W. V. Jaddoe, Chris Haley, Andrew M. McIntosh, Kathryn L. Evans, Alison D. Murray, Katri Räikkönen, Jari Lahti, Ellen A. Nøhr, Thorkild I. A. Sørensen, Torben Hansen, Camilla S. Morgen, Elisabeth B. Binder, Susanne Lucae, Juan R. González, Mariona Bustamante, Jordi Sunyer, John W. Holloway, Wilfried Karmaus, Hongmei Zhang, Ian J. Deary, Naomi R. Wray, John M. Starr, Marian Beekman, Diana van Heemst, P. Eline Slagboom, Pierre‐Emmanuel Morange, David‐Alexandre Trégouët, Jan H. Veldink, Gareth E. Davies, Eco J. C. de Geus, Dorret I. Boomsma, Judith M. Vonk, Bert Brunekreef, Gerard H. Koppelman, Marta E. Alarcón‐Riquelme, Rae‐Chi Huang, Craig E. Pennell, Joyce B. J. van Meurs, M. Arfan Ikram, Alun D. Hughes, Therese Tillin, Nish Chaturvedi, Zdenka Pausová, Tomáš Paus, Timothy D. Spector, Meena Kumari, Leonard C. Schalkwyk, Peter M. Visscher, George Davey Smith, Christoph Bock, Tom R. Gaunt, Jordana T. Bell, Bastiaan T. Heijmans, Jonathan Mill, Caroline L. Relton
Bibliographic record
VenueNature Genetics · 2021
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicEpigenetics and DNA Methylation
Canadian institutionsSickKids FoundationHospital for Sick ChildrenPublic Health OntarioUniversity of Toronto
FundersEconomic and Social Research CouncilMedical Research CouncilDirectorate for Biological SciencesNederlandse Federatie van Universitair Medische CentraBiotechnology and Biological Sciences Research CouncilZonMwÖsterreichischen Akademie der WissenschaftenWellcome TrustAgence Nationale de la RechercheResearch Councils UKMotor Neurone Disease AssociationNational Institute for Health and Care ResearchUniversity of BristolJoint Programming Initiative A healthy diet for a healthy lifeCancer Research UKBritish Heart FoundationRoyal SocietyWellcome
KeywordsBiologyDNA methylationGeneticsPhenotypeComputational biologyEvolutionary biologyGeneGene expression
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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.015
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 head0.006
GPT teacher head0.249
Teacher spread0.243 · 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 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
Citations539
Published2021
Admission routes1
Has abstractno