Twelve years of GWAS discoveries for osteoporosis and related traits: advances, challenges and applications

作     者：Xiaowei Zhu Weiyang Bai Houfeng Zheng Xiaowei Zhu;Weiyang Bai;Houfeng Zheng

作者机构：Diseases&Population(DaP)Geninfo LabSchool of Life SciencesWestlake University18 Shilongshan RoadHangzhouChina Institute of Basic Medical SciencesWestlake Institute for Advanced Study18 Shilongshan RoadHangzhouChina School of Life SciencesFudan UniversityShanghaiChina Westlake Laboratory of Life Sciences and BiomedicineHangzhouZhejiangChina 

出 版 物：《Bone Research》 (骨研究（英文版）)

年 卷 期：2021年第9卷第3期

页      面：284-302页

核心收录：

学科分类：0710[理学-生物学] 1002[医学-临床医学] 1001[医学-基础医学(可授医学、理学学位)] 100201[医学-内科学(含：心血管病、血液病、呼吸系病、消化系病、内分泌与代谢病、肾病、风湿病、传染病)] 10[医学] 

基　　金：supported by the National Natural Science Foundation of China(81871831 and 32061143019) 

主　　题：osteoporosis GWAS hundreds 

摘      要：Osteoporosis is a common skeletal disease,affecting-200 million people around the *** a complex disease,osteoporosis is influenced by many factors,including diet(*** and protein intake),physical activity,endocrine status,coexisting diseases and genetic *** this review,we first summarize the discovery from genome-wide association studies(GWASs)in the bone field in the last 12 *** date,GWASs and meta-analyses have discovered hundreds of loci that are associated with bone mineral density(BMD),osteoporosis,and osteoporotic ***,the GWAS approach has sometimes been criticized because of the small effect size of the discovered variants and the mystery of missing heritability,these two questions could be partially explained by the newly raised conceptual models,such as omnigenic model and natural ***,we introduce the clinical use of GWAS findings in the bone field,such as the identification of causal clinical risk factors,the development of drug targets and disease *** the fruitful GWAS discoveries in the bone field,most of these GWAS participants were of European descent,and more genetic studies should be carried out in other ethnic populations to benefit disease prediction in the corresponding population.