Multi-omics Data Integration for Identifying Osteoporosis Biomarkers and Their Biological Interaction and Causal Mechanisms

Chuan Qiu; Fangtang Yu; Kuanjui Su; Qi Zhao; Lan Zhang; Chao Xu; Wenxing Hu; Zun Wang; Lanjuan Zhao; Qing Tian; Yuping Wang; Hongwen Deng; Hui Shen

doi:10.1016/j.isci.2020.100847

Multi-omics Data Integration for Identifying Osteoporosis Biomarkers and Their Biological Interaction and Causal Mechanisms

iScience. 2020 Feb 21;23(2):100847. doi: 10.1016/j.isci.2020.100847. Epub 2020 Jan 17.

Authors

Chuan Qiu¹, Fangtang Yu¹, Kuanjui Su¹, Qi Zhao², Lan Zhang¹, Chao Xu³, Wenxing Hu⁴, Zun Wang⁵, Lanjuan Zhao¹, Qing Tian¹, Yuping Wang⁴, Hongwen Deng⁶, Hui Shen⁷

Affiliations

¹ Center for Bioinformatics and Genomics, Department of Biostatistics and Data Science, School of Public Health and Tropical Medicine, Tulane University, New Orleans 70112, LA, USA.
² Department of Preventive Medicine, College of Medicine, University of Tennessee Health Science Center, Memphis 38163, TN, USA.
³ Department of Biostatistics and Epidemiology, University of Oklahoma Health Sciences Center, Oklahoma City 73104, OK, USA.
⁴ Department of Biomedical Engineering, Tulane University, New Orleans 70118, LA, USA.
⁵ Center for Bioinformatics and Genomics, Department of Biostatistics and Data Science, School of Public Health and Tropical Medicine, Tulane University, New Orleans 70112, LA, USA; Xiangya Nursing School, Central South University, Changsha 410013, China.
⁶ Center for Bioinformatics and Genomics, Department of Biostatistics and Data Science, School of Public Health and Tropical Medicine, Tulane University, New Orleans 70112, LA, USA; School of Basic Medical Science, Central South University, Changsha 410013, China.
⁷ Center for Bioinformatics and Genomics, Department of Biostatistics and Data Science, School of Public Health and Tropical Medicine, Tulane University, New Orleans 70112, LA, USA. Electronic address: hshen3@tulane.edu.

Abstract

Osteoporosis is characterized by low bone mineral density (BMD). The advancement of high-throughput technologies and integrative approaches provided an opportunity for deciphering the mechanisms underlying osteoporosis. Here, we generated genomic, transcriptomic, methylomic, and metabolomic datasets from 119 subjects with high (n = 61) and low (n = 58) BMDs. By adopting sparse multiple discriminative canonical correlation analysis, we identified an optimal multi-omics biomarker panel with 74 differentially expressed genes (DEGs), 75 differentially methylated CpG sites (DMCs), and 23 differential metabolic products (DMPs). By linking genetic data, we identified 199 targeted BMD-associated expression/methylation/metabolite quantitative trait loci (eQTLs/meQTLs/metaQTLs). The reconstructed networks/pathways showed extensive biomarker interactions, and a substantial proportion of these biomarkers were enriched in RANK/RANKL, MAPK/TGF-β, and WNT/β-catenin pathways and G-protein-coupled receptor, GTP-binding/GTPase, telomere/mitochondrial activities that are essential for bone metabolism. Five biomarkers (FADS2, ADRA2A, FMN1, RABL2A, SPRY1) revealed causal effects on BMD variation. Our study provided an innovative framework and insights into the pathogenesis of osteoporosis.

Keywords: Disease; Genomics; Metabolomics; Transcriptomics.

Abstract

Grants and funding