Immune cell-specific gene expression and its causal role in osteoporosis and bone mineral density: Insights from single-cell eQTL and GWAS data integration
Bibliographic record
Abstract
Osteoporosis (OP) is a common metabolic bone disease, with genetic and immune system factors playing crucial roles in its pathogenesis. With the advancement of single-cell RNA sequencing (scRNA-seq), gene expression regulation at the immune cell subtype level has been more deeply explored. In this study, we integrated single-cell expression quantitative trait loci data with genome-wide association study data to systematically investigate the causal relationships between immune cell-specific gene expression and OP risk/ bone mineral density (BMD). Through summary-data-based Mendelian randomization, two-sample Mendelian randomization, Steiger directionality tests, and colocalization analysis, we identified 7 genes in specific immune cell types that are associated with OP/BMD phenotypes, including GLTPD1, NPRL3, NCR3, HBQ1, POU5F1, CDC42, and C10orf32. Specifically, GLTPD1, NPRL3, NCR3, HBQ1, and POU5F1 showed significant causal effects on OP risk, CDC42 was associated with total-body BMD in the 0 to 15 age group, and C10orf32 showed significant causal effects with total-body BMD in the >60 age group. Our findings provide new insights into the role of the immune system in bone metabolism and offer important theoretical support for further research on immune-mediated treatment strategies for OP.
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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 | 0.009 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".