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Can Genetic Pleiotropy Replicate Common Clinical Constellations of Cardiovascular Disease and Risk?

Overview of attention for article published in PLOS ONE, September 2012
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Title
Can Genetic Pleiotropy Replicate Common Clinical Constellations of Cardiovascular Disease and Risk?
Published in
PLOS ONE, September 2012
DOI 10.1371/journal.pone.0046419
Pubmed ID
Authors

Omri Gottesman, Esther Drill, Vaneet Lotay, Erwin Bottinger, Inga Peter

Abstract

The relationship between obesity, diabetes, hyperlipidemia, hypertension, kidney disease and cardiovascular disease (CVD) is established when looked at from a clinical, epidemiological or pathophysiological perspective. Yet, when viewed from a genetic perspective, there is comparatively little data synthesis that these conditions have an underlying relationship. We sought to investigate the overlap of genetic variants independently associated with each of these commonly co-existing conditions from the NHGRI genome-wide association study (GWAS) catalog, in an attempt to replicate the established notion of shared pathophysiology and risk. We used pathway-based analyses to detect subsets of pleiotropic genes involved in similar biological processes. We identified 107 eligible GWAS studies related to CVD and its established comorbidities and risk factors and assigned genes that correspond to the associated signals based on their position. We found 44 positional genes shared across at least two CVD-related phenotypes that independently recreated the established relationship between the six phenotypes, but only if studies representing non-European populations were included. Seven genes revealed pleiotropy across three or more phenotypes, mostly related to lipid transport and metabolism. Yet, many genes had no relationship to each other or to genes with established functional connection. Whilst we successfully reproduced established relationships between CVD risk factors using GWAS findings, interpretation of biological pathways involved in the observed pleiotropy was limited. Further studies linking genetic variation to gene expression, as well as describing novel biological pathways will be needed to take full advantage of GWAS results.

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Geographical breakdown

Country Count As %
Hong Kong 1 2%
United States 1 2%
Canada 1 2%
Unknown 47 94%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 10 20%
Researcher 9 18%
Student > Master 5 10%
Student > Doctoral Student 4 8%
Student > Bachelor 4 8%
Other 14 28%
Unknown 4 8%
Readers by discipline Count As %
Medicine and Dentistry 14 28%
Agricultural and Biological Sciences 13 26%
Biochemistry, Genetics and Molecular Biology 8 16%
Computer Science 6 12%
Mathematics 1 2%
Other 3 6%
Unknown 5 10%