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

Anni Moore1, Rasika Venkatesh1, Michael G Levin2

  • 1Genomics and Computational Biology, University of Pennsylvania Perelman School of Medicine, 3700 Hamilton Walk Philadelphia, PA, 19104, USA.

Insights

This study integrates multi-omics data with cardiac MRI to uncover genetic links to heart failure (HF). Findings reveal shared genetic factors between cardiac structure changes and HF risk, offering new insights into disease mechanisms.

Area of Science:

  • Genetics and Cardiovascular Disease Research
  • Multi-omics Integration in Human Health

Background:

  • Heart failure (HF) affects 1-3% of the global population, necessitating advanced understanding of its complex etiology.
  • Magnetic Resonance Imaging (MRI) provides key metrics for tracking HF progression, including left ventricular (LV) ejection fraction and volumes.
  • Genome-wide association studies (GWAS) identify HF risk variants but lack tissue-specific and mechanistic details.

Purpose of the Study:

  • To integrate transcriptome-wide association studies (TWAS) and proteome-wide association studies (PWAS) with MRI-derived cardiac measures and HF data.
  • To identify genetically regulated gene expression and protein abundance linked to HF precursors and established HF.
  • To elucidate novel biological pathways and mechanisms underlying HF development.

Main Methods:

  • Performed TWAS and PWAS incorporating MRI-derived LV measures (ejection fraction, end-diastolic volume, end-systolic volume) and all-cause HF data.
  • Utilized gene-set enrichment analysis to identify implicated biological pathways.
  • Employed protein-protein interaction networks to explore functional relationships of identified genes and proteins.

Main Results:

  • Identified significant overlaps in genes and proteins associated with LV ejection fraction and LV end-systolic volume.
  • Observed shared genetic and proteomic factors between MRI-derived cardiac measures and all-cause HF.
  • Implicated several putative HF-relevant pathways through multi-omics analyses.

Conclusions:

  • Demonstrated the value of multi-omics approaches for understanding the genetic architecture of HF.
  • Provided novel insights into the relationship between cardiac structural/functional changes and HF pathogenesis.
  • Highlighted potential therapeutic targets by linking genetic factors to HF mechanisms.

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