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Gene-based GWAS analysis for consecutive studies of GEFOS

  • W. Zhu
  • C. Xu
  • J.-G. Zhang
  • H. He
  • K.-H. Wu
  • L. Zhang
  • Y. Zeng
  • Y. Zhou
  • K.-J. Su
  • H.-W. Deng
Original Article
  • 33 Downloads

Abstract

Summary

By integrating the multilevel biological evidence and bioinformatics analyses, the present study represents a systemic endeavor to identify BMD-associated genes and their roles in skeletal metabolism.

Introduction

Single-nucleotide polymorphism (SNP)-based genome-wide association studies (GWASs) have already identified about 100 loci associated with bone mineral density (BMD), but these loci only explain a small proportion of heritability to osteoporosis risk. In the present study, we performed a gene-based analysis of the largest GWASs in the bone field to identify additional BMD-associated genes.

Methods

BMD-associated genes were identified by combining the summary statistic P values of SNPs across individual genes in the two consecutive meta-analyses of GWASs from the Genetic Factors for Osteoporosis (GEFOS) studies. The potential functionality of these genes to bone was partially assessed by differential gene expression analysis. Additionally, the consistency of the identification of potential bone mineral density (BMD)-associated variants were evaluated by estimating the correlation of the P values of the same single-nucleotide polymorphisms (SNPs)/genes between the two consecutive Genetic Factors for Osteoporosis Studies (GEFOS) with largely overlapping samples.

Results

Compared to the SNP-based analysis, the gene-based strategy identified additional BMD-associated genes with genome-wide significance and increased their mutual replication between the two GEFOS datasets. Among these BMD-associated genes, three novel genes (UBTF, AAAS, and C11orf58) were partially validated at the gene expression level. The correlation analysis presented a moderately high between-study consistency of potential BMD-associated variants.

Conclusions

Gene-based analysis as a supplementary strategy to SNP-based genome-wide association studies, when applied here, is shown that it helped identify some novel BMD-associated genes. In addition to its empirically increased statistical power, gene-based analysis also provides a higher testing stability for identification of BMD genes.

Keywords

BMD GEFOS Gene-based analysis Osteoporosis 

Abbreviations

AAAS

Aladin WD repeat nucleoporin

AOGC

Australasian Osteoporosis Genetics Consortium

BMD

Bone mineral density

CCNB1

Cyclin B1

DEGs

Differentially expressed genes

ERF

Erasmus Rucphen Family Study

FA

Forearm

FDR

False discovery rate

FHS

Framingham Heart Study

FN

Femoral neck

GATES

Extended Simes procedure method

GEFOS

Genetic Factors for Osteoporosis Studies

GEO

Gene Expression Omnibus

GO

Gene ontology

GWASs

Genome-wide association studies

HYST

Hybrid set-based test

KGG

Knowledge-based mining system for genome-wide genetic studies

LD

Linkage disequilibrium

LS

Lumbar spine

NHGRI

National Human Genome Research Institute

NUP155

Nucleoporin 155

PPI

Protein-protein interaction

QQ

Quantile-quantile

RANK

Receptor activator of NFKB

RANKL

Receptor activator of NFKB ligand

rRNA

Ribosomal RNA

RS-I

Rotterdam Study-I

SNP

Single-nucleotide polymorphism

TWINSUK

TwinsUK

UBTF

Upstream binding transcription factor

WHO

World Health Organization

Notes

Acknowledgments

Full author lists of the two consortia (GEFOS2 and GEFOS-seq) were available in the Supplementary Acknowledgment.

Funding information

This study was partially supported by and/or benefited from grants from National Institutes of Health [AR069055, U19 AG055373, R01 MH104680, R01AR059781 and P20GM109036], and Edward G. Schlieder Endowment to Tulane University.

Compliance with ethical standards

Conflicts of interest

None.

Supplementary material

198_2018_4654_MOESM1_ESM.pdf (564 kb)
ESM 1 (PDF 563 kb)
198_2018_4654_MOESM2_ESM.pdf (647 kb)
ESM 2 (PDF 647 kb)
198_2018_4654_MOESM3_ESM.pdf (425 kb)
ESM 3 (PDF 424 kb)

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Copyright information

© International Osteoporosis Foundation and National Osteoporosis Foundation 2018

Authors and Affiliations

  • W. Zhu
    • 1
    • 2
  • C. Xu
    • 2
  • J.-G. Zhang
    • 2
  • H. He
    • 2
  • K.-H. Wu
    • 2
  • L. Zhang
    • 2
  • Y. Zeng
    • 2
    • 3
  • Y. Zhou
    • 2
  • K.-J. Su
    • 2
  • H.-W. Deng
    • 1
    • 2
  1. 1.College of Life SciencesHunan Normal UniversityChangshaChina
  2. 2.Center for Bioinformatics and Genomics, School of Public Health and Tropical MedicineTulane UniversityNew OrleansUSA
  3. 3.College of Life Sciences and BioengineeringBeijing Jiaotong UniversityBeijingChina

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