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Record W2784696894 · doi:10.1093/hmg/ddy237

Consortium-based genome-wide meta-analysis for childhood dental caries traits

2018· article· en· W2784696894 on OpenAlexfundno aff
Simon Haworth, Dmitry Shungin, Justin T. van der Tas, Carolina Medina‐Gómez, Victor Yakimov, Bjarke Feenstra, John R. Shaffer, Myoung Keun Lee, Marie Standl, Elisabeth Thiering, Carol A. Wang, Klaus Bønnelykke, Johannes Waage, Leon Eyrich Jessen, Pia Elisabeth Nørrisgaard, Raimo Joro, Ilkka Seppälä, Olli T. Raitakari, Tom Dudding, Olja Grgić, Edwin M. Ongkosuwito, Anu Vierola, Aino-Maija Eloranta, Nicola West, Steven J. Thomas, Daniel W. McNeil, Steven M. Levy, Rebecca L. Slayton, Ellen A. Nøhr, Terho Lehtimäki, Timo A. Lakka, Hans Bisgaard, Craig E. Pennell, Jan Kühnisch, Mary L. Marazita, Mads Melbye, Frank Geller, Fernando Rivadeneira, Eppo B. Wolvius, Paul W. Franks, Ingegerd Johansson, Nicholas J. Timpson

Bibliographic record

VenueHuman Molecular Genetics · 2018
Typearticle
Languageen
FieldDentistry
TopicDental Health and Care Utilization
Canadian institutionsnot available
FundersNational Institute of Dental and Craniofacial ResearchNational Human Genome Research InstituteCanadian Institutes of Health ResearchNational Institutes of HealthGovernment of Western AustraliaPaavo Nurmen SäätiöDiabetesliittoNational Health and Medical Research CouncilEmil Aaltosen SäätiöTampereen YliopistoVetenskapsrådetUniversity of BristolKuopion Yliopistollinen SairaalaJuho Vainion SäätiöTurun Yliopistollisen Keskussairaalan Koulutus- ja TutkimussäätiöFoundation for Cardiovascular ResearchLundbeckfondenZonMwSuomen KulttuurirahastoAcademy of FinlandMedical Research CouncilSigne ja Ane Gyllenbergin SäätiöH2020 European Research CouncilMurdoch UniversityErasmus Universiteit RotterdamEdith Cowan UniversityTampereen TuberkuloosisäätiöKelaRaine Medical Research FoundationErasmus Medisch CentrumSydäntutkimussäätiöCurtin University of TechnologyEuropean CommissionNational Institute for Health and Care ResearchUniversity of Notre DameAustralian GovernmentNederlandse Organisatie voor Wetenschappelijk OnderzoekWellcome TrustWellcome
KeywordsBiologyGeneticsMeta-analysisGenome-wide association studyGenomeComputational biologyGeneSingle-nucleotide polymorphismGenotype

Abstract

fetched live from OpenAlex

Prior studies suggest dental caries traits in children and adolescents are partially heritable, but there has been no large-scale consortium genome-wide association study (GWAS) to date. We therefore performed GWAS for caries in participants aged 2.5-18.0 years from nine contributing centres. Phenotype definitions were created for the presence or absence of treated or untreated caries, stratified by primary and permanent dentition. All studies tested for association between caries and genotype dosage and the results were combined using fixed-effects meta-analysis. Analysis included up to 19 003 individuals (7530 affected) for primary teeth and 13 353 individuals (5875 affected) for permanent teeth. Evidence for association with caries status was observed at rs1594318-C for primary teeth [intronic within ALLC, odds ratio (OR) 0.85, effect allele frequency (EAF) 0.60, P 4.13e-8] and rs7738851-A (intronic within NEDD9, OR 1.28, EAF 0.85, P 1.63e-8) for permanent teeth. Consortium-wide estimated heritability of caries was low [h2 of 1% (95% CI: 0%: 7%) and 6% (95% CI 0%: 13%) for primary and permanent dentitions, respectively] compared with corresponding within-study estimates [h2 of 28% (95% CI: 9%: 48%) and 17% (95% CI: 2%: 31%)] or previously published estimates. This study was designed to identify common genetic variants with modest effects which are consistent across different populations. We found few single variants associated with caries status under these assumptions. Phenotypic heterogeneity between cohorts and limited statistical power will have contributed; these findings could also reflect complexity not captured by our study design, such as genetic effects which are conditional on environmental exposure.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.499
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.044
GPT teacher head0.320
Teacher spread0.275 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

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

Citations48
Published2018
Admission routes1
Has abstractyes

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