Integrating genome-wide association and transcriptome prediction model identifies novel target genes for osteoporosis

被引:4
|
作者
Zhu, M. [1 ]
Yin, P. [1 ]
Hu, F. [1 ]
Jiang, J. [1 ]
Yin, L. [1 ]
Li, Y. [2 ]
Wang, S. [1 ]
机构
[1] Chinese Acad Sci, Shenzhen Inst Adv Technol, Guangdong Hong Kong Macao Joint Lab Human Machine, Shenzhen, Peoples R China
[2] AnLan AI, Shenzhen, Peoples R China
基金
中国国家自然科学基金;
关键词
Osteoporosis; Transcriptome-wide association study; Colocalization method; Gene expression; BONE-MINERAL DENSITY; SIGNALING PATHWAY; EQTL SIGNALS; COLOCALIZATION; VARIANTS; TISSUE; GWAS; DIFFERENTIATION; METAANALYSIS; OSTEOBLAST;
D O I
10.1007/s00198-021-06024-z
中图分类号
R5 [内科学];
学科分类号
1002 ; 100201 ;
摘要
In this study, we integrated large-scale GWAS summary data and used the predicted transcriptome-wide association study method to discover novel genes associated with osteoporosis. We identified 204 candidate genes, which provide novel clues for understanding the genetic mechanism of osteoporosis and indicate potential therapeutic targets. Introduction Osteoporosis is a highly polygenetic disease characterized by low bone mass and deterioration of the bone microarchitecture. Our objective was to discover novel candidate genes associated with osteoporosis. Methods To identify potential causal genes of the associated loci, we investigated trait-gene expression associations using the transcriptome-wide association study (TWAS) method. This method directly imputes gene expression effects from genome-wide association study (GWAS) data using a statistical prediction model trained on GTEx reference transcriptome data. We then performed a colocalization analysis to evaluate the posterior probability of biological patterns: associations characterized by a single causal variant or multiple distinct causal variants. Finally, a functional enrichment analysis of gene sets was performed using the VarElect and CluePedia tools, which assess the causal relationships between genes and a disease and search for potential gene's functional pathways. The osteoporosis-associated genes were further confirmed based on the differentially expressed genes profiled from mRNA expression data of bone tissue. Results Our analysis identified 204 candidate genes, including 154 genes that have been previously associated with osteoporosis, 50 genes that have not been previously discovered. A biological function analysis found that 20 of the candidate genes were directly associated with osteoporosis. Further analysis of multiple gene expression profiles showed that 15 genes were differentially expressed in patients with osteoporosis. Among these, SLC11A2, MAP2K5, NFATC4, and HSP90B1 were enriched in four pathways, namely, mineral absorption pathway, MAPK signaling pathway, Wnt signaling pathway, and PI3K-Akt signaling pathway, which indicates a causal relationship with the occurrence of osteoporosis. Conclusions We demonstrated that transcriptome fine-mapping identifies more osteoporosis-related genes and provides key insight into the development of novel targeted therapeutics for the treatment of osteoporosis.
引用
收藏
页码:2493 / 2503
页数:11
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