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

## TL;DR

This study combines genetic data from two platforms to find new genetic factors linked to osteoporosis in Korean postmenopausal women.

## Contribution

The study introduces an integrated approach using dual exome arrays and machine learning to identify novel osteoporosis-related loci.

## Key findings

- 111 overlapping SNPs in 70 genes were identified through cross-platform analysis.
- LD-block analysis revealed 10 blocks with significant SNPs, including four on chromosome 12.
- Candidate genes like CCDC92, DYNC2H1, and NQO1 were highlighted for further validation.

## Abstract

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.