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Atherosclerosis. 2011 Dec;219(2):698-703. doi: 10.1016/j.atherosclerosis.2011.08.044. Epub 2011 Sep 6.

Look beyond one's own nose: combination of information from publicly available sources reveals an association of GATA4 polymorphisms with plasma triglycerides.

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Division of Genetic Epidemiology, Department of Medical Genetics, Molecular and Clinical Pharmacology, Innsbruck Medical University, Innsbruck, Austria.



GATA4iKO mice exhibit impeded triglyceride absorption from intestine and decreased plasma triglyceride levels. Data in humans are lacking. We hypothesized that triglyceride levels might also be regulated by polymorphisms in the GATA4 gene in humans. We used publicly available data from different sources to evaluate this hypothesis. Our approach is a more often applicable advance to uncover associations and their functional implications which would have been otherwise missed by standard genome-wide association studies (GWAS).


We used the publicly available GWAS results from 137 SNPs in the GATA4 region for triglyceride levels. We embedded these results into the comprehensive functional genomics data provided in the UCSC Genome Browser including among others information on regulatory elements and interspecies conservation. A concise graphical presentation is proposed together with an R function for automatic data preparation. This process is presented in an educational manner using a screencast to become most useful for other researchers.


We observed several polymorphisms in and around the GATA4 gene which have a significant influence on plasma triglyceride levels with the lowest p-value at SNP rs1466785 (Bonferroni-corrected p-value = 1.76e-5). The bioinformatic evaluation of this locus in publicly available functional genomics data provided converging evidence for the presence of a transcriptional regulator downstream of GATA4.


The combination of different sources of data has revealed an association of GATA4 with triglyceride levels in humans. Our evaluation exemplifies how an integrative analysis including both statistical and biological perspectives can shed new light on available association data and reveals novel candidate genes, which are otherwise hidden in the noisy region below genome-wide significance.

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