Genome-wide association studies reveal novel locus with sex-/therapy-specific fracture risk effects in childhood cancer survivors
Bibliographic record
Abstract
ABSTRACT Survivors of childhood cancer treated with radiation therapy (RT) and osteotoxic chemotherapies are at increased risk for fractures. However, research focusing on how genetic and clinical susceptibility factors jointly contribute to fracture risk among long-term (≥5 years) survivors of childhood cancer has been limited. To address this gap, we conducted genome-wide association studies of fracture risk in 2,453 participants from the Childhood Cancer Survivor Study (CCSS) using Cox regression models and prioritized sex- and treatment-stratified genetic associations. Replication analyses were conducted in an independent survivor sample from the St. Jude Lifetime Cohort Study (SJLIFE). We identified a genome-wide significant (P<5⨯10 −8 ) fracture risk locus, 16p13.3 ( HAGHL ), among female CCSS survivors (N=1,289) with strong evidence of sex-specific effects (P sex-heterogeneity <7⨯10 −6 ). We found rs1406815 showed the strongest association with fracture risk after replication (HR meta-analysis per risk allele=1.43, P=8.2⨯10 −9 ; N=1,935 women). While the association between rs1406815 and fracture risk was weak among female survivors who did not receive radiation therapy (RT) (HR CCSS =1.22, P=0.11), the association strength increased with greater RT doses to the head or neck (HR CCSS =1.88, P=2.4⨯10 −10 in those with any head/neck RT; HR CCSS =3.79, P=9.1⨯10 −7 in those treated with >36 Gray). In silico bioinformatics analyses suggest these fracture risk alleles regulate HAGHL gene expression and related bone resorption pathways, and are plausibly moderated by head/neck RT. Genetic risk profiles integrating this locus may help identify young female survivors who would benefit from targeted interventions to reduce fracture risk.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".