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Human Genetics

, Volume 138, Issue 2, pp 167–185 | Cite as

Integrative genomic analysis predicts novel functional enhancer-SNPs for bone mineral density

  • Chuan Qiu
  • Hui ShenEmail author
  • Xiaoying Fu
  • Chao Xu
  • Qing Tian
  • Hongwen Deng
Original Investigation

Abstract

Osteoporosis is a skeletal disorder characterized by low bone mineral density (BMD) and deterioration of bone microarchitecture. To identify novel genetic loci underlying osteoporosis, an effective strategy is to focus on scanning of variants with high potential functional impacts. Enhancers play a crucial role in regulating cell-type-specific transcription. Therefore, single-nucleotide polymorphisms (SNPs) located in enhancers (enhancer-SNPs) may represent strong candidate functional variants. Here, we performed a targeted analysis for potential functional enhancer-SNPs that may affect gene expression and biological processes in bone-related cells, specifically, osteoblasts, and peripheral blood monocytes (PBMs), using five independent cohorts (n = 5905) and the genetics factors for osteoporosis summary statistics, followed by comprehensive integrative genomic analyses of chromatin states, transcription, and metabolites. We identified 15 novel enhancer-SNPs associated with femoral neck and lumbar spine BMD, including 5 SNPs mapped to novel genes (e.g., rs10840343 and rs10770081 in IGF2 gene) and 10 novel SNPs mapped to known BMD-associated genes (e.g., rs2941742 in ESR1 gene, and rs10249092 and rs4342522 in SHFM1 gene). Interestingly, enhancer-SNPs rs10249092 and rs4342522 in SHFM1 were tightly linked, but annotated to different enhancers in PBMs and osteoblasts, respectively, suggesting that even tightly linked SNPs may regulate the same target gene and contribute to the phenotype variation in cell-type-specific manners. Importantly, ten enhancer-SNPs may also regulate BMD variation by affecting the serum metabolite levels. Our findings revealed novel susceptibility loci that may regulate BMD variation and provided intriguing insights into the genetic mechanisms of osteoporosis.

Notes

Acknowledgements

This study was partially supported or benefited by grants from the National Institutes of Health [R01AR059781, P20GM109036, R01MH107354, R01MH104680, R01GM109068, R01AR069055, and U19AG055373], the Franklin D. Dickson/Missouri Endowment, and the Edward G. Schlieder Endowment and the Drs. W. C. Tsai and P. T. Kung Professorship in Biostatistics from Tulane University. The Women’s Health Initiative (WHI) program is funded by the National Heart, Lung, and Blood Institute, National Institutes of Health, U.S. Department of Health and Human Services through contracts N01WH22110, 24152, 32100-2, 32105-6, 32108-9, 32111-13, 32115, 32118-32119, 32122, 42107-26, 42129-32, and 44221. This manuscript was not prepared in collaboration with the investigators of the WHI, has not been reviewed and/or approved by, and does not necessarily reflect the opinions of the WHI investigators or the NHLBI. WHI Population Architecture Using Genomics and Epidemiology (PAGE) funded through the NHGRI Population Architecture Using Genomics and Epidemiology (PAGE) network (Grant number U01 HG004790). Assistance with phenotype harmonization, SNP selection, data cleaning, meta-analyses, data management and dissemination, and general study coordination was provided by the PAGE Coordinating Center [U01HG004801-01]. The data sets used for the analyses described in this manuscript were obtained from dbGaP at http://www.ncbi.nlm.nih.gov/sites/entrez?db=gap through dbGaP accession phs000200.v6.p2.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no competing interests.

Supplementary material

439_2019_1971_MOESM1_ESM.docx (62 kb)
Supplementary material 1 (DOCX 61 KB)
439_2019_1971_MOESM2_ESM.xlsx (13.1 mb)
Supplementary material 2 (XLSX 13451 KB)

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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  • Chuan Qiu
    • 1
  • Hui Shen
    • 1
    Email author
  • Xiaoying Fu
    • 1
  • Chao Xu
    • 1
    • 2
  • Qing Tian
    • 1
  • Hongwen Deng
    • 1
    • 3
  1. 1.Department of Global Biostatistics and Data Science, Center for Bioinformatics and Genomics, School of Public Health and Tropical MedicineTulane UniversityNew OrleansUSA
  2. 2.Department of Biostatistics and EpidemiologyUniversity of Oklahoma Health Sciences CenterOklahoma CityUSA
  3. 3.School of Basic Medical ScienceCentral South UniversityChangshaChina

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