Analysis of putative cis-regulatory elements regulating blood pressure variation

Area of Science:

• Genetics
• Cardiovascular Biology
• Genomic Epidemiology

Background:

• Genome-wide association studies (GWAS) have identified numerous loci linked to blood pressure (BP) traits.
• Previous research indicated an enrichment of these BP loci within expression quantitative trait loci (eQTLs) in aorta and tibial arteries.
• Identifying the specific genes and regulatory mechanisms underlying these associations remains a challenge.

Purpose of the Study:

• To fine-map known blood pressure loci and discover novel genes by identifying putative cis-regulatory elements (CREs) in relevant tissues.
• To develop and apply a method integrating CREs, gene expression, and association statistics for gene discovery.
• To investigate tissue-specific regulatory impacts on blood pressure traits.

Main Methods:

• Construction of CRE maps using open chromatin data from heart, aorta, tibial arteries, and kidney cell types.
• Quantification of variant regulatory impact using deltaSVM functional scores.
• Aggregation of variants near expressed genes and application of the group-wise sequence kernel association test (GSKAS) weighted by deltaSVM scores.
• Testing for associations with BP traits and tissue-specific expression, including a positive control analysis with known QT interval genes.

Main Results:

• Identification of novel candidate genes associated with BP traits, including MTHFR, C10orf32, CSK, NOV, ULK4, SDCCAG8, SCAMP5, RPP25, HDGFRP3, VPS37B, and PPCDC.
• Demonstration of a heart-specific effect for known QT interval genes (SCN5A, NOS1AP), validating the method's tissue-specificity.
• The developed method effectively prioritizes variants and genes for functional follow-up using tissue-specific regulatory information.

Conclusions:

• The study successfully identifies novel genes and regulatory regions implicated in blood pressure regulation.
• The integration of CREs, eQTLs, and advanced statistical methods provides a powerful framework for gene discovery in complex traits.
• This approach enhances the functional interpretation of GWAS findings and facilitates targeted experimental validation for blood pressure-related genes.