Meta-analysis of genome-wide studies identifies <i>MEF2C</i> SNPs associated with bone mineral density at forearm
Bibliographic record
Abstract
BACKGROUND: Forearm fractures affect 1.7 million individuals worldwide each year and most occur earlier in life than hip fractures. While the heritability of forearm bone mineral density (BMD) and fracture is high, their genetic determinants are largely unknown. AIM: To identify genetic variants associated with forearm BMD and forearm fractures. METHODS: BMD at distal radius, measured by dual-energy x-ray absorptiometry, was tested for association with common genetic variants. We conducted a meta-analysis of genome-wide association studies for BMD in 5866 subjects of European descent and then selected the variants for replication in 715 Mexican American samples. Gene-based association was carried out to supplement the single-nucleotide polymorphism (SNP) association test. We then tested the BMD-associated SNPs for association with forearm fracture in 2023 cases and 3740 controls. RESULTS: We found that five SNPs in the introns of MEF2C were associated with forearm BMD at a genome-wide significance level (p<5×10(-8)) in meta-analysis (lead SNP, rs11951031[T] -0.20 SDs per allele, p=9.01×10(-9)). The gene-based association test suggested an association between MEF2C and forearm BMD (p=0.003). The association between MEF2C variants and risk of fracture did not achieve statistical significance (SNP rs12521522[A]: OR=1.14 (95% CI 0.92 to 1.35), p=0.14). Meta-analysis also revealed two genome-wide suggestive loci at CTNNA2 and 6q23.2. CONCLUSIONS: These findings demonstrate that variants at MEF2C were associated with forearm BMD, implicating this gene in the determination of BMD at forearm.
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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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.016 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".