Research Papers:

Multiple analyses of large-scale genome-wide association study highlight new risk pathways in lumbar spine bone mineral density

Jinsong Wei, Ming Li, Feng Gao, Rong Zeng, Guiyou Liu _ and Keshen Li

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Oncotarget. 2016; 7:31429-31439. https://doi.org/10.18632/oncotarget.8948

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Jinsong Wei1, Ming Li2, Feng Gao3, Rong Zeng1, Guiyou Liu4, Keshen Li5,6

1Department of Orthopedic Surgery, Affiliated Hospital of Guangdong Medical University, Zhanjiang, China

2Departmentof Endocrinology and Metabolism, The Fourth Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang, China

3Department of Trauma and Emergency Surgeon, The Second Affiliated Hospital, Harbin Medical University, Harbin, China

4Genome Analysis Laboratory, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, China

5Institute of Neurology, Affiliated Hospital of Guangdong Medical University, Zhanjiang, China

6Stroke Center, Neurology & Neurosurgery Division, The Clinical Medicine Research Institute & The First Affiliated Hospital, Jinan University, Guangzhou, China

Correspondence to:

Guiyou Liu, e-mail: [email protected]

Keshen Li, e-mail: [email protected]

Keywords: osteoporosis, bone mineral density, pathway analysis, genome-wide association studies

Received: November 12, 2015     Accepted: March 29, 2016     Published: April 23, 2016


Osteoporosis is a common human complex disease. It is mainly characterized by low bone mineral density (BMD) and low-trauma osteoporotic fractures (OF). Until now, a large proportion of heritability has yet to be explained. The existing large-scale genome-wide association studies (GWAS) provide strong support for the investigation of osteoporosis mechanisms using pathway analysis. Recent findings showed that different risk pathways may be involved in BMD in different tissues. Here, we conducted multiple pathway analyses of a large-scale lumbar spine BMD GWAS dataset (2,468,080 SNPs and 31,800 samples) using two published gene-based analysis software including ProxyGeneLD and the PLINK. Using BMD genes from ProxyGeneLD, we identified 51 significant KEGG pathways with adjusted P<0.01. Using BMD genes from PLINK, we identified 38 significant KEGG pathways with adjusted P<0.01. Interestingly, 33 pathways are shared in both methods. In summary, we not only identified the known risk pathway such as Wnt signaling, in which the top GWAS variants are significantly enriched, but also highlight some new risk pathways. Interestingly, evidence from further supports the involvement of these pathways in MBD.

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