Can genetic pleiotropy replicate common clinical constellations of
cardiovascular disease and risk?
Authors Gottesman O, Drill E, Lotay V, Bottinger E, Peter I
Submitted By Erwin Bottinger on 3/7/2013
Status Published
Journal PLoS ONE
Year 2012
Date Published 9/1/2012
Volume : Pages 7 : e46419
PubMed Reference 23029515
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.


Investigators with authorship
NameInstitution
Erwin BottingerMount Sinai School of Medicine

Complications