Identifying causal serum protein-cardiometabolic trait relationships using whole genome sequencing

Area of Science:

• Genetics
• Proteomics
• Cardiovascular Research

Background:

• Cardiometabolic diseases pose a significant public health challenge.
• Understanding genetic regulation of disease-associated proteins is crucial for dissecting complex biology.

Purpose of the Study:

• To conduct a protein quantitative trait locus (pQTL) analysis of serum proteins linked to cardiometabolic health.
• To identify genetic variants associated with serum protein levels and their causal relationships with cardiometabolic traits.

Main Methods:

• Performed pQTL analysis on 248 serum proteins in 2893 individuals from two Greek cohorts (MANOLIS and Pomak).
• Utilized whole-genome sequencing (WGS) data and meta-analysis to identify pQTL variants.
• Investigated rare variants and variants with increased frequency in the study populations.

Main Results:

• Detected 301 independently associated pQTL variants for 170 proteins.
• Identified 12 rare pQTL variants and 15 variants rare in other populations but enriched in the study cohorts.
• Found causal associations between proteins and cardiometabolic traits, such as Mep1b with high-density lipoprotein (HDL) levels.
• Developed a Mep1b knock-out (KO) mouse model.

Conclusions:

• The study provides insights into the genetic architecture of the serum proteome.
• Identified novel protein-disease relationships relevant to cardiometabolic conditions.
• Demonstrated the utility of isolated populations for pQTL discovery.