Novel Genetic Determinants of Dental Maturation in Children
Bibliographic record
Abstract
Dental occlusion requires harmonious development of teeth, jaws, and other elements of the craniofacial complex, which are regulated by environmental and genetic factors. We performed the first genome-wide association study (GWAS) on dental development (DD) using the Demirjian radiographic method. Radiographic assessments from participants of the Generation R Study (primary study population, N 1 = 2,793; mean age of 9.8 y) were correlated with ~30 million genetic variants while adjusting for age, sex, and genomic principal components (proxy for population stratification). Variants associated with DD at genome-wide significant level ( P < 5 × 10 −8 ) mapped to 16q12.2 ( IRX5) (lead variant rs3922616, B = 0.16; P = 2.2 × 10 −8 ). We used Fisher’s combined probability tests weighted by sample size to perform a meta-analysis ( N = 14,805) combining radiographic DD at a mean age of 9.8 y from Generation R with data from a previous GWAS ( N 2 = 12,012) on number of teeth (NT) in infants used as proxy of DD at a mean age of 9.8 y (including the ALSPAC and NFBC1966). This GWAS meta-analysis revealed 3 novel loci mapping to 7p15.3 ( IGF2BP3: P = 3.2 × 10 −8 ), 14q13.3 ( PAX9: P = 1.9 × 10 −8 ), and 16q12.2 ( IRX5: P = 1.2 × 10 −9 ) and validated 8 previously reported NT loci. A polygenic allele score constructed from these 11 loci was associated with radiographic DD in an independent Generation R set of children ( N = 703; B = 0.05, P = 0.004). Furthermore, profiling of the identified genes across an atlas of murine and human stem cells observed expression in the cells involved in the formation of bone and/or dental tissues (>0.3 frequency per kilobase of transcript per million mapped reads), likely reflecting functional specialization. Our findings provide biological insight into the polygenic architecture of the pediatric dental maturation process.
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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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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.
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.
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".