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

<b>Background:</b> 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. <b>Methods:</b> 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 <i>p</i> < 1 × 10^-4. <b>Results:</b> No overlapped SNP reached genome-wide significance, but rs2076212 in <i>PNPLA3</i> achieved suggestive significance (<i>p</i> < 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 <i>ARMS2</i>, <i>CCDC92</i>, <i>NQO1</i>, <i>ZNF510</i>, <i>PTPRB</i>, and <i>DYNC2H1</i> as candidate genes. LD-block analysis revealed 10 blocks with at least one SNP at <i>p</i> < 1 × 10^-4, including four chromosome 12 loci (<i>NAV2</i>, <i>BICD1</i>, <i>CCDC92</i>, <i>ZNF664</i>) that became apparent only when LD patterns were evaluated jointly across platforms. <b>Conclusions:</b> 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 <i>CCDC92</i>, <i>DYNC2H1</i>, <i>NQO1</i>, and related genes as biologically plausible candidates for future validation.