Wu Qing, Dai Jingyuan, Liu Jianing, Wu Lang
Department of Biomedical Informatics, College of Medicine, The Ohio State University, 250 Lincoln Tower, 1800 Cannon Drive, Columbus, OH, 43210, USA.
Pacific Center for Genome Research, University of Hawai'i at Mānoa, Honolulu, HI, USA.
Curr Osteoporos Rep. 2025 May 24;23(1):24. doi: 10.1007/s11914-025-00917-2.
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.
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. 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.
全基因组关联研究（GWAS）通过识别与骨密度（BMD）和骨折风险相关的基因位点，显著推动了骨质疏松症研究。然而，由于非欧洲人群代表性不足，差异仍然存在，限制了多基因风险评分（PRS）的适用性。本综述探讨了骨质疏松症遗传学的最新进展，强调了现有的差异，并探索了更具包容性研究的策略。
以欧洲人群为重点的GWAS已经确定了骨质疏松症的关键基因位点，包括WNT信号通路（SOST、LRP5）和RUNX2转录调控。然而，这些变异中只有不到40%能在亚洲和非洲人群中得到复制。非欧洲人群的新兴研究揭示了特定人群的基因位点、性别特异性关联以及基因-环境相互作用。机器学习（ML）辅助的GWAS和多组学整合方面的进展正在改善基因发现。在不同人群中扩大GWAS、整合多组学数据、完善基于ML的风险模型以及规范生物样本库数据对于公平的骨质疏松症研究至关重要。未来的工作必须优先考虑临床转化，以加强个性化的骨质疏松症预防和治疗。
