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Purpose of Review <p>Genome-wide association studies (GWAS) have significantly advanced osteoporosis research by identifying genetic loci associated with bone mineral density (BMD) and fracture risk. However, disparities persist due to the underrepresentation of non-European populations, limiting the applicability of polygenic risk scores (PRS). This review examines recent advancements in osteoporosis genetics, highlights existing disparities, and explores strategies for more inclusive research.</p> Recent Findings <p>European-focused GWAS have identified key loci for osteoporosis, including <i>WNT</i> signaling (<i>SOST</i>, <i>LRP5</i>) and <i>RUNX2</i> transcriptional regulation. However, fewer than 40% of these variants can be replicated in Asian and African populations. Emerging studies in non-European groups reveal population-specific loci, sex-specific associations, and gene-environment interactions. Advances in machine learning (ML)-assisted GWAS and multi-omics integration are improving genetic discovery.</p> Summary <p>Expanding GWAS in diverse populations, integrating multi-omics data, refining ML-based risk models, and standardizing biobank data are essential for equitable osteoporosis research. Future efforts must prioritize clinical translation to enhance personalized osteoporosis prevention and treatment.</p>

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Bridging Genomic Research Disparities in Osteoporosis GWAS: Insights for Diverse Populations

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年5月24日

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  • Qing Wu,
  • Jingyuan Dai,
  • Jianing Liu,
  • Lang Wu

摘要

Purpose of Review

Genome-wide association studies (GWAS) have significantly advanced osteoporosis research by identifying genetic loci associated with bone mineral density (BMD) and fracture risk. However, disparities persist due to the underrepresentation of non-European populations, limiting the applicability of polygenic risk scores (PRS). This review examines recent advancements in osteoporosis genetics, highlights existing disparities, and explores strategies for more inclusive research.

Recent Findings

European-focused GWAS have identified key loci for osteoporosis, including WNT signaling (SOST, LRP5) and RUNX2 transcriptional regulation. However, fewer than 40% of these variants can be replicated in Asian and African populations. Emerging studies in non-European groups reveal population-specific loci, sex-specific associations, and gene-environment interactions. Advances in machine learning (ML)-assisted GWAS and multi-omics integration are improving genetic discovery.

Summary

Expanding GWAS in diverse populations, integrating multi-omics data, refining ML-based risk models, and standardizing biobank data are essential for equitable osteoporosis research. Future efforts must prioritize clinical translation to enhance personalized osteoporosis prevention and treatment.