Identifying potentially common genes between dyslipidemia and osteoporosis using novel analytical approaches

  • Xu Lin
  • Cheng Peng
  • Jonathan Greenbaum
  • Zhang-Fang Li
  • Ke-Hao Wu
  • Zeng-Xin Ao
  • Tong Zhang
  • Jie Shen
  • Hong-Wen Deng
Original Article

Abstract

Dyslipidemia (DL) is closely related to osteoporosis (OP), while the exact common genetic mechanisms are still largely unknown. We proposed to use novel genetic analysis methods with pleiotropic information to identify potentially novel and/or common genes for the potential shared pathogenesis associated with OP and/or DL. We assessed the pleiotropy between plasma lipid (PL) and femoral neck bone mineral density (FNK BMD). We jointly applied the conditional false discovery rate (cFDR) method and the genetic analysis incorporating pleiotropy and annotation (GPA) method to the summary statistics provided by genome-wide association studies (GWASs) of FNK BMD (n = 49,988) and PL (n = 188,577) to identify potentially novel and/or common genes for BMD/PL. We found strong pleiotropic enrichment between PL and FNK BMD. Two hundred and forty-five PL SNPs were identified as potentially novel SNPs by cFDR and GPA. The corresponding genes were enriched in gene ontology (GO) terms “phospholipid homeostasis” and “chylomicron remnant clearance”. Three SNPs (rs2178950, rs9939318, and rs9368716) might be the pleiotropic ones and the corresponding genes NLRC5 (rs2178950) and TRPS1 (rs9939318) were involved in NF-κB signaling pathway and Wnt signaling pathway as well as inflammation and innate immune processes. Our study validated the pleiotropy between PL and FNK BMD, and corroborated the reliability and high-efficiency of cFDR and GPA methods in further analyses of existing GWASs with summary statistics. We identified potentially common and/or novel genes for PL and/or FNK BMD, which may provide new insight and direction for further research.

Keywords

Dyslipidemia Osteoporosis Pleiotropy cFDR GPA 

Notes

Acknowledgements

Hong-Wen Deng was partially supported by Grants from the National Institutes of Health [U19AG05537301, R01AR057049, R01AR059781, D43TW009107, P20GM109036, R01MH107354, R01MH104680, R01GM109068], the Edward G. Schlieder Endowment fund to Tulane University. We acknowledged Genetic Factors for Osteoporosis (GEFOS-seq Consortium, http://www.gefos.org/) and Global Lipids Genetics Consortium (GLGC, http://csg.sph.umich.edu) for their GWAS summary statistics posted online. We acknowledged Chun-Ping Zeng for his useful suggestions for this study.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

This article does not contain any studies with human participants or animals performed by any of the authors.

Supplementary material

438_2017_1414_MOESM1_ESM.pdf (178 kb)
Supplementary material 1 (PDF 178 KB)
438_2017_1414_MOESM2_ESM.pdf (75 kb)
Supplementary material 2 (PDF 75 KB)

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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  • Xu Lin
    • 1
  • Cheng Peng
    • 2
  • Jonathan Greenbaum
    • 3
  • Zhang-Fang Li
    • 4
  • Ke-Hao Wu
    • 3
  • Zeng-Xin Ao
    • 1
  • Tong Zhang
    • 4
  • Jie Shen
    • 4
  • Hong-Wen Deng
    • 3
    • 5
  1. 1.Southern Medical UniversityGuangzhouPeople’s Republic of China
  2. 2.Department of Geriatrics, National Key Clinical Specialty, Guangzhou First People’s HospitalThe Second Affiliated Hospital of South China University of TechnologyGuangzhouPeople’s Republic of China
  3. 3.Center for Bioinformatics and Genomics, Department of Global Statistics and Data Science, School of Public Health and Tropical MedicineTulane UniversityNew OrleansUSA
  4. 4.Department of Endocrinology and MetabolismThe Third Affiliated Hospital of Southern Medical UniversityGuangzhouPeople’s Republic of China
  5. 5.School of Basic Medical SciencesCentral South UniversityChangshaPeople’s Republic of China

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