AI Insight
Researchers used machine learning and transcriptomic analysis to identify OCEL1 as a gene associated with low bone mineral density from an initial pool of 2,568 genes. Using data from multiple gene expression cohorts and advanced statistical modeling that accounts for shared genetic regulation, they found that OCEL1 expression showed a positive association with heel bone mineral density when controlling for neighboring genes in the same chromosomal region. The study employed multiple validation approaches including support vector machines, LASSO regression, and XGBoost algorithms to narrow candidates to three genes, with OCEL1 showing the most consistent directional effects across independent datasets.
Why it matters
Identifying specific genes like OCEL1 that influence bone mineral density could help develop targeted therapies for osteoporosis and bone fragility conditions. The multi-step validation approach strengthens confidence that OCEL1 represents a genuine biological signal rather than a statistical artifact, potentially opening new avenues for understanding bone metabolism and immune regulation in skeletal health.
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⚠️ Preprint – Noch nicht peer-reviewed
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Introduction Low bone mineral density (BMD) is associated with altered bone remodeling and osteoimmune regulation. We aimed to prioritize transcript-level signals supported across human expression cohorts and evaluate their attribution within local cis-regulatory architecture. Materials and Methods Nominal differential-expression signals from GSE56815 were restricted with a predefined 25-term MSigDB thematic library. Features measurable in GSE56815 and GSE2208 underwent linear SVM ranking, LASSO, and XGBoost selection, followed by cross-cohort transcriptomic assessment. OCEL1 was examined in a four-exposure locus-aware CisMRBEEX model with NR2F6, MRPL34, BABAM1, eQTLGen cis-eQTLs, heel eBMD GWAS statistics, and UKBB337K LD. Results The expression screen identified 2,568 genes at nominal P<0.05; thematic restriction retained 190 candidates and 107 common features entered machine learning. Linear SVM, LASSO, and XGBoost retained 24, 18, and 9 genes and converged on CPNE1, EZR, and OCEL1. Only OCEL1 showed concordant expression direction with P<0.05 in both cohorts. In the primary 268-variant four-exposure model, OCEL1 was positively associated with heel eBMD ({beta}=0.010293, SE=0.004107, 95% CI 0.002244-0.018342, P=0.01220, PIP=0.4001; conditional coefficient on the standardized analysis scale); BABAM1 retained an oppositely directed component. The no-palindromic reconstruction remained positive with wider uncertainty ({beta}=0.007652, P=0.06739). Conclusion Convergent transcriptomic evidence prioritized OCEL1, whose conditional genetic-expression component was positively associated with heel eBMD after local shared cis regulation was modeled. Keywords: low bone mineral density; OCEL1; transcriptomics; machine learning; cis-Mendelian randomization