A Large-Scale Multi-omics Polygenic Risk Score Analysis Identified Candidate Biomarkers Associated with Heel Bone...

Area of Science:

• Genetics and Genomics
• Metabolomics
• Proteomics

Background:

• Bone mineral density (BMD) is a key indicator for osteoporosis (OP).
• Quantitative trait loci (QTLs) data integration offers novel insights into BMD regulation.
• Understanding genetic and molecular factors influencing hBMD is crucial for OP prevention and treatment.

Purpose of the Study:

• To identify novel candidate genes and biomarkers associated with heel bone mineral density (hBMD) using multi-omics data.
• To investigate the potential causal relationships between identified multi-omics targets and hBMD.
• To explore the molecular underpinnings of osteoporosis through integrated omics analysis.

Main Methods:

• Utilized multi-omics QTLs data from the INTERVAL cohort and UK Biobank (UKB).
• Calculated polygenic risk scores (PRS) for RNAs, proteins, and metabolites.
• Employed generalized linear models, integrated omics analysis (MetaboAnalyst 6.0), and Mendelian randomization (MR) for association and causal inference.

Main Results:

• Identified 195 hBMD-associated genes (e.g., WNT16) and 180 proteins (e.g., COL1A1).
• Discovered 21 associated metabolites (e.g., total cholesterol), with replication in UKB.
• Integrated analysis highlighted glycerophospholipid metabolism, and MR identified novel causal biomarkers like PLPP3 for hBMD.

Conclusions:

• Identified novel candidate biomarkers (PLPP3, RGMB, RNF128) for hBMD.
• Provided genetic evidence for the causal roles of these biomarkers in bone mineral regulation.
• Findings contribute to understanding the molecular basis of osteoporosis and potential therapeutic targets.