ArticleAging2022
Differential gene expression orchestrated by transcription factors in osteoporosis: bioinformatics analysis of associated polymorphism elaborating functional relationships.
Article in Aging, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed, 1 citations in OpenAlex.
- Integrative Single-Cell RNA Sequencing and Machine Learning Reveals Candidate Plasma Protein-Associated Gene Signatures for Osteoporosis: A Preliminary Exploratory in Silico Study.International journal of general medicine · 2026Article
- Integrated single-cell and bulk RNA sequencing analysis reveal immune-related biomarkers in postmenopausal osteoporosis.Heliyon · 2024Article
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Authors and funding
6 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundIdentification 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.
methodsWe 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.
resultsBioinformatic 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
conclusionsWe 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.
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