Integrated Cross-Platform Analysis Reveals Candidate Variants and Linkage Disequilibrium-Defined Loci Associated with Osteoporosis in Korean Postmenopausal Women.

Background: Osteoporosis is highly prevalent in postmenopausal women, yet genome-wide association studies often miss disease-relevant variants because of incomplete single nucleotide polymorphism (SNP) coverage and platform-specific limitations. We aimed to identify genetic contributors to osteoporosis risk by integrating two exome-based genotyping platforms with multilayer analytic approaches. Methods: We analyzed extreme osteoporosis phenotypes in Korean postmenopausal women from the Korean Genome and Epidemiology Study (KoGES) Ansan-Anseong cohorts using the Illumina Infinium HumanExome BeadChip and the Affymetrix Axiom Exome Array. After standard quality control, single-SNP logistic regression, cross-platform overlap analysis, and three machine-learning models were applied. Predicted functional impact was evaluated using multiple in silico algorithms and conservation scores. Finally, datasets from both platforms were merged, and cross-platform linkage disequilibrium (LD) blocks were defined to identify loci containing SNPs with p < 1 × 10-4. Results: No overlapped SNP reached genome-wide significance, but rs2076212 in PNPLA3 achieved suggestive significance (p < 1 × 10-5) only on the Illumina array. Cross-platform analysis identified 111 overlapping SNPs in 70 genes. Integrated machine-learning, in silico, and conservation evidence prioritized ARMS2, CCDC92, NQO1, ZNF510, PTPRB, and DYNC2H1 as candidate genes. LD-block analysis revealed 10 blocks with at least one SNP at p < 1 × 10-4, including four chromosome 12 loci (NAV2, BICD1, CCDC92, ZNF664) that became apparent only when LD patterns were evaluated jointly across platforms. Conclusions: Combining dual exome arrays with LD-block analysis, machine learning, and functional prediction improved sensitivity for detecting low bone mineral density-related loci and highlighted CCDC92, DYNC2H1, NQO1, and related genes as biologically plausible candidates for future validation.