Multi-omics approach unveils novel therapeutic targets for osteoporosis: integrated analysis of Asian and European gene-tissue expression consortium data

Abstract

BACKGROUND: With the acceleration of China's aging population, the number of osteoporosis patients has been increasing significantly. Recent advancements in genome-wide association studies and single-cell transcriptomic sequencing have empowered researchers to identify novel osteoporosis-associated genes through integrative multi-omics analyses. OBJECTIVE: To identify potential therapeutic targets for osteoporosis using summary data-based Mendelian randomization approaches that integrate genome-wide association studies and transcriptomic data from Asian and European populations. METHODS: By integrating cis-expression quantitative trait loci (cis-eQTL) and protein quantitative trait loci (pQTL) datasets from multiple tissues (blood and muscle-bone) with osteoporosis genome-wide association study data (the 2021 European population osteoporosis GWAS data from FinnGen and the 2020 East Asian population GWAS from Biobank Japan), we employed summary data-based Mendelian randomization (SMR) to identify osteoporosis-associated genes. Colocalization analysis, single-cell sequencing, and enrichment analysis were performed for further validation. All data were obtained from published studies or publicly available databases with ethical approval and informed consent. RESULTS AND CONCLUSION: SMR analysis identified 64 genes significantly associated with osteoporosis (after removing duplicates), among which HLA-DQA1, HLA-DQA2, HLA-DQB1, HLA-DQB2, and HLA-DRB5 were validated in both outcome datasets. Colocalization analysis provided evidence for HLA-DQA2 and HLA-DQB1 (posterior probability PPH4 > 0.8). Plasma levels of HLA-DQA2 were associated with reduced osteoporosis risk. Single-cell analysis revealed increased abundance of dendritic cells, B cells, macrophages, and neutrophils in the osteoporotic immune microenvironment. Enrichment analysis showed that identified genes were enriched in MHC class II antigen presentation pathway. This study identified several previously unreported osteoporosis-associated genes through bioinformatics integration of Asian and European GWAS data, warranting further exploration as potential therapeutic targets.

1. Introduction

Osteoporosis is a common skeletal disease, and its prevalence continues to rise with the aging global population [1-2]. Over the past decade, genome-wide association studies (GWAS) have identified over 1,000 genetic loci associated with bone mineral density [3-5]. Recent advances in multi-omics research have enabled the integration of expression quantitative trait loci (eQTL) and protein quantitative trait loci (pQTL) data with summary data-based Mendelian randomization (SMR) analysis, using single nucleotide polymorphisms (SNPs) as instrumental variables to reveal the genetic mechanisms of gene expression regulation and the association between protein expression differences and disease. This approach helps uncover novel risk genes and pathogenic mechanisms, and facilitates the development of therapeutic drugs targeting these genes. It has been widely applied in the study of diseases and disease-related quantitative traits [6-7].

A recent study using transcriptome-wide association studies combined with eQTL colocalization analysis identified risk genes for heel bone mineral density across multiple tissues [8]. However, due to the limited availability of large-scale osteoporosis datasets, no study has yet integrated human tissue eQTL and plasma pQTL data with osteoporosis GWAS data from Chinese populations. This study aims to fill this gap by integrating data from Asian and European populations to identify novel therapeutic targets for osteoporosis.