Connecting intermediate phenotypes to disease using multi-omics in heart failure.

Area of Science:

• Genetics
• Cardiovascular Medicine
• Bioinformatics

Background:

• Heart failure (HF) affects 1-3% of the global population, posing a significant health burden.
• Cardiac magnetic resonance imaging (MRI) measures left ventricle (LV) structure and function to track HF progression.
• Genome-wide association studies (GWAS) identify HF risk variants but lack tissue-specific and mechanistic details.

Purpose of the Study:

• To integrate transcriptome-wide and proteome-wide association studies (TWAS and PWAS) with MRI-derived cardiac measures and HF data.
• To identify genetically regulated gene expression and protein abundance changes related to HF precursors and all-cause HF.
• To explore shared genetic and molecular pathways underlying HF development using multi-omics approaches.

Main Methods:

• Combined TWAS and PWAS with MRI data (LV ejection fraction, end-diastolic volume, end-systolic volume) and all-cause HF data.
• Utilized gene-set enrichment analysis and protein-protein interaction networks to identify implicated pathways.
• Investigated overlaps between gene and protein associations from MRI measures and HF.

Main Results:

• Identified significant gene and protein overlaps between LV ejection fraction and end-systolic volume measures.
• Found that many overlaps from TWAS/PWAS using MRI data are shared with all-cause HF.
• Implicated several putative HF-relevant pathways associated with the identified genes and proteins.

Conclusions:

• Multi-omics approaches enhance the understanding of genetic contributions to HF.
• This study provides novel insights into the relationship between cardiac structural/functional changes and HF.
• The findings highlight potential molecular targets for HF research and intervention.