Association of low‐frequency genetic variants in regulatory regions with nonsyndromic orofacial clefts
Bibliographic record
Abstract
Genome-wide scans have shown that common risk alleles for orofacial clefts (OFC) tend to be located in noncoding regulatory elements and cumulatively explain only part of the heritability of OFCs. Low-frequency variants may account for some of the "missing" heritability. Therefore, we scanned low-frequency variants located within putative craniofacial enhancers to identify novel OFC risk variants and implicate new regulatory elements in OFC pathogenesis. Analyses were performed in a multiethnic sample of 1,995 cases of cleft lip with or without cleft palate (CL/P), 221 cases with cleft palate (CP) only, and 1,576 unaffected controls. One hundred and nineteen putative craniofacial enhancers identified from ChIP-Seq studies in craniofacial tissues or cell lines contained multiple low-frequency (0.01-1%) variants, which we genotyped in participants using a custom Illumina panel. Two complementary statistical approaches, sequence kernel association test and combined multivariate and collapsing, were used to test association of the aggregated low-frequency variants across each enhancer region with CL/P and CP. We discovered a significant association between CP and a branchial arch enhancer near FOXP1 (mm60; p-value = .0002). Additionally, we observed a suggestive association between CL/P and a forebrain enhancer near FOXE1 (hs1717; p-value = .001). These findings suggest that low-frequency variants in craniofacial enhancer regions contribute to the complex etiology of nonsyndromic OFCs.
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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 | 0.000 | 0.002 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".