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
Affiliations
Chuan Qiu
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
Fangtang Yu
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
Kuanjui Su
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
Qi Zhao
Department of Preventive Medicine, College of Medicine, University of Tennessee Health Science Center, Memphis 38163, TN, USA
Lan Zhang
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
Chao Xu
Department of Biostatistics and Epidemiology, University of Oklahoma Health Sciences Center, Oklahoma City 73104, OK, USA
Wenxing Hu
Department of Biomedical Engineering, Tulane University, New Orleans 70118, LA, USA
Zun Wang
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
Lanjuan Zhao
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
Qing Tian
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
Yuping Wang
Department of Biomedical Engineering, Tulane University, New Orleans 70118, LA, USA
Hongwen Deng
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
Hui Shen
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; Corresponding author
Summary: 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. : Disease; Genomics; Metabolomics; Transcriptomics Subject Areas: Disease, Genomics, Metabolomics, Transcriptomics
