Transcriptome-wide association study identifies novel genes associated with bone mineral density and lean body mass in children

Author:

Xu Jiawen,Ma Jun,Zeng Yi,Si Haibo,Wu Yuangang,Zhang Shaoyun,Shen BinORCID

Abstract

Abstract Objective To identify novel candidate genes whose expression is associated with bone mineral density (BMD) and body lean mass (LM) in children. Methods A tissue-specific transcriptome-wide association study (TWAS) was conducted utilizing a large-scale genome-wide association study (GWAS) dataset associated with BMD and LM and involving 10,414 participants. The measurement of BMD and LM phenotypes was made based on total-body dual-energy X-ray absorptiometry (TB-DXA) scans. TWAS was conducted by using FUSION software. Reference panels for muscle skeleton (MS), peripheral blood (NBL) and whole blood (YBL) were used for TWAS analysis. Functional enrichment and protein–protein interaction (PPI) analyses of the genes identified by TWAS were performed by using the online tool Metascape (http://metascape.org). Results For BMD, we identified 174 genes with P < 0.05, such as IKZF1 (P = 1.46 × 10−9) and CHKB (P = 8.31 × 10−7). For LM, we identified 208 genes with P < 0.05, such as COPS5 (P = 3.03 × 10−12) and MRPS33 (P = 5.45 × 10−10). Gene ontology (GO) enrichment analysis of the BMD-associated genes revealed 200 GO terms, such as protein catabolic process (Log P = −5.09) and steroid hormone-mediated signaling pathway (Log P = −3.13). GO enrichment analysis of the LM-associated genes detected 287 GO terms, such as the apoptotic signaling pathway (Log P = −8.08) and lipid storage (Log P = −3.55). Conclusion This study identified several candidate genes for BMD and LM in children, providing novel clues to the genetic mechanisms underlying the development of childhood BMD and LM.

Funder

National Natural Science Foundation of China

Sichuan Province Science and Technology Support Program

Publisher

Springer Science and Business Media LLC

Subject

Endocrinology,Endocrinology, Diabetes and Metabolism

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