Genome Scan, Fine-Mapping, and Candidate Gene Analysis of Non-Syndromic Cleft Lip with or without Cleft Palate Reveals Phenotype-Specific Differences in Linkage and Association Results
Bibliographic record
Abstract
OBJECTIVES: Non-syndromic orofacial clefts, i.e. cleft lip (CL) and cleft palate (CP), are among the most common birth defects. The goal of this study was to identify genomic regions and genes for CL with or without CP (CL/P). METHODS: We performed linkage analyses of a 10 cM genome scan in 820 multiplex CL/P families (6,565 individuals). Significant linkage results were followed by association analyses of 1,476 SNPs in candidate genes and regions, utilizing a weighted false discovery rate (wFDR) approach to control for multiple testing and incorporate the genome scan results. RESULTS: Significant (multipoint HLOD >or=3.2) or genome-wide-significant (HLOD >or=4.02) linkage results were found for regions 1q32, 2p13, 3q27-28, 9q21, 12p11, 14q21-24 and 16q24. SNPs in IRF6 (1q32) and in or near FOXE1 (9q21) reached formal genome-wide wFDR-adjusted significance. Further, results were phenotype dependent in that the IRF6 region results were most significant for families in which affected individuals have CL alone, and the FOXE1 region results were most significant in families in which some or all of the affected individuals have CL with CP. CONCLUSIONS: These results highlight the importance of careful phenotypic delineation in large samples of families for genetic analyses of complex, heterogeneous traits such as CL/P.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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".