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Cardiometabolic Disease Risk Loci Share Downstream Regulation

NEW YORK (GenomeWeb) – Gene loci linked to cardiometabolic diseases like hypertension and impaired glucose metabolism share downstream regulatory networks, according to an international team of researchers.

Cardiometabolic diseases are often a harbinger of coronary artery disease, and while genome-wide association studies have uncovered hundred of variants associated with coronary artery disease risk, the researchers noted that these loci only explain a small portion of the expected heritable variance in cardiometabolic disease risk.

As part of the Stockholm-Tartu Atherosclerosis Reverse Networks Engineering Task (STARNET) study, the team genotyped and performed RNA sequencing on vascular and metabolic tissue samples from 600 patients with coronary artery disease. As they reported in Science today, they found that gene expression traits linked with cardiometabolic disease risk SNPs identified by genome-wide association studies were more common in STARNET than unspecific gene-tissue expression studies — indicating that they shared downstream cis- or trans-gene regulation.

"By analyzing gene-expression data from multiple tissues in hundreds of patients with coronary artery disease, we were able to identify disease-causing genes that either were specific to single tissues or acted across multiple tissues in networks to cause cardiometabolic diseases," senior author Johan Björkegren from the Icahn School of Medicine at Mount Sinai and the Karolinska Institute said in a statement.

He and his colleagues genotyped those patients and sequenced RNA obtained from various vascular and metabolic tissue samples that were collected when the patients underwent coronary artery bypass surgery. From this, the researchers identified about 8 million cis-eQTLs, about half of which they said were unique SNP-gene pairs. These cis-eQTLs were enriched for genetic associations that had been uncovered by GWAS for coronary artery disease, cardiometabolic diseases, and Alzheimer's disease, they added.

They also noted that the STARNET-derived cis-eQTLs matched more coronary artery disease and cardiometabolic disease risk loci than cis-eQTLs derived from blood-only samples or from corresponding tissues from healthy individuals.

Björkegren and his colleagues additionally uncovered downstream trans-genes and noted widespread sharing of cis- and trans-gene regulation by GWAS risk loci across tissues and cardiometabolic diseases. For instance, for coronary artery disease, they found that 28 risk loci with at lease one causal interaction had some 50 cis-genes and 1,000 trans-genes, and of those, 26 risk loci, 37 cis-genes, and 994 trans-genes were connected in a coronary artery disease regulation network that stretched across all seven tissue types sampled.

The researchers also reported tissue-specific effects. For example, they found that ABCA8/ABCA5 was linked with 36 downstream trans-genes in visceral abdominal fat and HDL, while EVI5 was linked to 32 trans-genes in visceral abdominal fat and total cholesterol.

Björkegren and his colleagues further noted that two risk SNPs, one for coronary artery disease and one for LDL cholesterol level, regulated PCSK9 in visceral abdominal fat, but not in the liver — a finding they confirmed in an independent gene expression dataset of morbidly obese individuals.

According to the investigators, this indicates that PCSK9 is secreted from visceral abdominal fat into the portal vein, where it then has its effect on hepatic LDL receptor degradation, LDL plasma levels, and coronary artery disease risk. They further reported that among STARNET patients those in the upper percentile of waist-to-hip ratio had higher circulating PCSK9 levels and LDL-to-HDL ratios than those in the lower percentile.

PCSK9, the researchers noted in their statement, is a target for lipid-lowering drugs currently on the market.

They added that they are working with AstraZeneca to use STARNET to refine drug target development. AstraZeneca's Li-Ming Gan, a co-author on the Science paper, said in a statement that the dataset is providing "essential translation information to help us identify new drug targets, as well as informing on existing targets in cardiovascular and metabolic diseases."

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