Research Paper Volume 14, Issue 12 pp 5163—5176
Differential gene expression orchestrated by transcription factors in osteoporosis: bioinformatics analysis of associated polymorphism elaborating functional relationships
- 1 Department of Orthopedics, Tri-Service General Hospital and National Defense Medical Center, Taipei, Taiwan, R.O.C.
- 2 School of Public Health, National Defense Medical Center, Taipei, Taiwan, R.O.C.
- 3 Division of Rheumatology, Immunology and Allergy, Department of Internal Medicine, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan, R.O.C.
- 4 Graduate Institute of Life Sciences, National Defense Medical Center, Taipei, Taiwan, R.O.C.
Received: November 1, 2021 Accepted: May 19, 2022 Published: June 21, 2022https://doi.org/10.18632/aging.204136
How to Cite
Copyright: © 2022 Wang et al. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Background: Identification of candidate SNPs from transcription factors (TFs) is a novel concept, while systematic large-scale studies on these SNPs are scarce.
Purpose: This study aimed to identify the SNPs of six TF binding sites (TFBSs) and examine the association between candidate SNPs and osteoporosis.
Methods: We used the Taiwan BioBank database; University of California, Santa Cruz, reference genome; and a chromatin immunoprecipitation sequencing database to detect 14 SNPs at the potential binding sites of six TFs. Moreover, we performed a case–control study and genotyped 109 patients with osteoporosis (T-score ≤ −2.5 evaluated by dual-energy X-ray absorptiometry) and 262 healthy individuals (T-score ≥ −1) at Tri-Service General Hospital from 2015 to 2019. Furthermore, we used the expression quantitative trait loci (eQTL) from the Genotype-Tissue Expression database to identify downstream gene expression as a criterion for the function of candidate SNPs.
Results: Bioinformatic analysis identified 14 SNPs of TFBSs influencing osteoporosis. Of these SNPs, the rs130347 CC + TC genotype had 0.57 times higher risk than the TT genotype (OR = 0.57, p = 0.031). Validation of eQTL analysis revealed that rs130347 T allele influences mRNA expression of downstream A4GALT in whole blood (p = 0.0041) and skeletal tissues (p = 0.011).
Conclusions: We successfully identified the unique osteoporosis locus rs130347 in the Taiwanese and functionally validated this finding. In the future, this strategy can be expanded to other diseases to identify susceptible loci and achieve personalized precision medicine.