Identification of an novel genetic variant associated with osteoporosis: insights from the Taiwan Biobank Study

Yi-Ching Liaw; Koichi Matsuda; Yung-Po Liaw

doi:10.1093/jbmrpl/ziae028

Identification of an novel genetic variant associated with osteoporosis: insights from the Taiwan Biobank Study

JBMR Plus. 2024 Mar 5;8(5):ziae028. doi: 10.1093/jbmrpl/ziae028. eCollection 2024 May.

Authors

Yi-Ching Liaw^{1

2}, Koichi Matsuda^{1

3}, Yung-Po Liaw^{2

4

5}

Affiliations

¹ Department of Computational Biology and Medical Sciences, Laboratory of Clinical Genome Sequencing, Graduate School of Frontier Sciences, The University of Tokyo, Tokyo 108-8639, Japan.
² Department of Public Health and Institute of Public Health, Chung Shan Medical University, Taichung 40201, Taiwan.
³ Institute of Medical Science, The University of Tokyo, Laboratory of Genome Technology, Human Genome Center, Tokyo 108-8639, Japan.
⁴ Institute of Medicine, Chung Shan Medical University, Taichung 40201, Taiwan.
⁵ Department of Medical Imaging, Chung Shan Medical University Hospital, Taichung 40201, Taiwan.

Abstract

Purpose: The purpose of this study was to identify new independent significant SNPs associated with osteoporosis using data from the Taiwan Biobank (TWBB).

Material and methods: The dataset was divided into discovery (60%) and replication (40%) subsets. Following data quality control, genome-wide association study (GWAS) analysis was performed, adjusting for sex, age, and the top 5 principal components, employing the Scalable and Accurate Implementation of the Generalized mixed model approach. This was followed by a meta-analysis of TWBB1 and TWBB2. The Functional Mapping and Annotation (FUMA) platform was used to identify osteoporosis-associated loci. Manhattan and quantile-quantile plots were generated using the FUMA platform to visualize the results. Independent significant SNPs were selected based on genome-wide significance (P < 5 × 10^-8) and independence from each other (r² < 0.6) within a 1 Mb window. Positional, eQTL(expression quantitative trait locus), and Chromatin interaction mapping were used to map SNPs to genes.

Results: A total of 29 084 individuals (3154 osteoporosis cases and 25 930 controls) were used for GWAS analysis (TWBB1 data), and 18 918 individuals (1917 cases and 17 001 controls) were utilized for replication studies (TWBB2 data). We identified a new independent significant SNP for osteoporosis in TWBB1, with the lead SNP rs76140829 (minor allele frequency = 0.055, P-value = 1.15 × 10^-08). Replication of the association was performed in TWBB2, yielding a P-value of 6.56 × 10^-3. The meta-analysis of TWBB1 and TWBB2 data demonstrated a highly significant association for SNP rs76140829 (P-value = 7.52 × 10^-10). In the positional mapping of rs76140829, 6 genes (HABP2, RP11-481H12.1, RNU7-165P, RP11-139 K1.2, RP11-57H14.3, and RP11-214 N15.5) were identified through chromatin interaction mapping in mesenchymal stem cells.

Conclusions: Our GWAS analysis using the Taiwan Biobank dataset unveils rs76140829 in the VTI1A gene as a key risk variant associated with osteoporosis. This finding expands our understanding of the genetic basis of osteoporosis and highlights the potential regulatory role of this SNP in mesenchymal stem cells.

Keywords: general population studies < epidemiology; genetic research; osteopetrosis < diseases and disorders of/related to bone.