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Could Genomic Biomarkers Help Reduce Incidence of Heart Disease?

Published on November 19, 2019 in Cornerstone Blog · Last updated 5 months 3 weeks ago


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Genomic Biomarkers and Heart Disease

The Findings:

In one of the largest genome-wide studies for low-frequency recurrent copy number variations (CNVs), researchers evaluated 23 cardiovascular disease-related traits such as body mass index, glucose levels, and lipid profiling to determine their associations with these CNVs. Investigators identified 11 genome-wide significant loci that might serve as biomarkers for individuals with elevated risk for cardiovascular disease and related traits.

Why it matters:

With cardiovascular disease the leading cause of death in the United States, biomarkers could help identify individuals who are genetically predisposed to this condition and would benefit from preventive care and early interventions. If individuals determined to be at high risk for cardiovascular disease based on the findings of this study were able to make healthy lifestyle adaptations, these preventive steps may help reduce the incidence of the disease.

Who conducted the study:

Among the study’s co-authors from Children’s Hospital of Philadelphia are first authors Joseph Glessner and Jin Li, PhD, Akshatha Desai, Xiao Chang, and Bert Almoguera, all part of the Center for Applied Genomics (CAG); Patrick Sleiman, an analytical scientist and associate director of the CAG; and senior study scientist, Hakon Hakonarson, MD, PhD, a genomics expert and director of the CAG. Drs. Sleiman and Hakonarson are also part of the Division of Human Genetics at CHOP and the Department of Pediatrics at the Perelman School of Medicine at the University of Pennsylvania. 

How they did it:

The study team analyzed the data of 14,783 individuals from the consortium of Electronic Medical Records and Genomics (eMERGE) Network, ultimately examining 10,619 unrelated subjects of European ancestry who were genotyped with 657,366 markers genome-wide on the Illumina Infinium Quad 660 array. Researchers performed CNV calling based on array marker intensity and evaluated data quality, ancestry stratification, and relatedness to ensure unbiased association discovery. The National Institutes of Health-supported eMERGE network comprises nine study sites with the goal of developing and applying genomic research methods to derive phenotypes from electronic medical records. 

Quick thoughts:

Study authors referenced epidemiologic studies and randomized clinical trials that have provided evidence that coronary heart disease is largely preventable with proper diet and exercise, noting, “Therefore, it is important to identify genetic variants that have a large impact on cardiovascular disease together with their associated intermediate traits, as individuals carrying such disease risk factors may benefit from proper prevention efforts, if detected through early screening procedures.”

What’s next:

While genome-wide association studies (GWAS) have identified numerous genetic variants significantly associated with complex human disease traits, the heritability attributed to these single nucleotide polymorphisms (SNPs) is limited. According to the study authors, the missing piece may partly lie in the contribution of structural variants. In this regard, low frequency CNVs bestow risk of larger effect sizes compared to SNPs, and may explain a large fraction of the missing heritability. This emphasizes the importance of identifying structural variants that impact major disease categories, such as cardiovascular disease and its risk factors, which could complement GWAS findings. 

Where the study was published:

 International Journal of Cardiology

Where to learn more: 

Read more about the eMERGE network and additional applications of GWAS on Cornerstone.