The contribution of genomic research to explaining racial disparities in cardiovascular disease: a systematic review

Am J Epidemiol. 2015 Apr 1;181(7):464-72. doi: 10.1093/aje/kwu319. Epub 2015 Mar 1.


After nearly a decade of genome-wide association studies, no assessment has yet been made of their contribution toward an explanation of the most prominent racial health disparities observed at the population level. We examined populations of African and European ancestry and focused on cardiovascular diseases, which are collectively the largest contributor to the racial mortality gap. We conducted a systematic search for review articles and meta-analyses published in 2007-2013 in which genetic data from both populations were available. We identified 68 articles relevant to this question; however, few reported significant associations in both racial groups, with just 3 variants meeting study-specific significance criteria. For most outcomes, there were too few estimates for quantitative summarization, but when summarization was possible, racial group did not contribute to heterogeneity. Most associations reported from genome-wide searches were small, difficult to replicate, and in no consistent direction that favored one racial group or another. Although the substantial investment in this technology might have produced clinical advances, it has thus far made little or no contribution to our understanding of population-level racial health disparities in cardiovascular disease.

Keywords: cardiovascular disease; continental population groups; genomics; health-care disparities; systematic review.

Publication types

  • Research Support, Non-U.S. Gov't
  • Review
  • Systematic Review

MeSH terms

  • African Continental Ancestry Group / genetics*
  • African Continental Ancestry Group / statistics & numerical data
  • Cardiovascular Diseases / ethnology*
  • Cardiovascular Diseases / genetics*
  • European Continental Ancestry Group / genetics*
  • European Continental Ancestry Group / statistics & numerical data
  • Genomics / methods
  • Genomics / statistics & numerical data*
  • Health Status Disparities*
  • Humans
  • Meta-Analysis as Topic
  • Polymorphism, Single Nucleotide
  • PubMed
  • Review Literature as Topic