Multi-omics Data Integration for Identifying Osteoporosis Biomarkers and Their Biological Interaction and Causal Mechanisms - Qiu, Chuan; Yu, Fangtang; Su, Kuanjui; Zhao, Qi; Zhang, Lan; Xu, Chao; Hu, Wenxing; Wang, Zun; Zhao, Lanjuan; Tian, Qing; Wang, Yuping; Deng, Hongwen; et al. (iScience, 2020)
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. [Display omitted] •Multi-omics integration revealed 172 osteoporosis biomarkers with complex interaction•Genetic variants have multi-level effects on osteoporosis biomarkers•A substantial proportion of biomarkers enriched in bone-related pathways/activities•Several osteoporosis biomarkers have causal effects on the BMD variation Disease; Genomics; Metabolomics; Transcriptomics