[HTML][HTML] Overlap of expression quantitative trait loci (eQTL) in human brain and blood

M McKenzie, AK Henders, A Caracella, NR Wray… - BMC medical …, 2014 - Springer
BMC medical genomics, 2014Springer
Background Expression quantitative trait loci (eQTL) are genomic regions regulating RNA
transcript expression levels. Genome-wide Association Studies (GWAS) have identified
many variants, often in non-coding regions, with unknown functions and eQTL provide a
possible mechanism by which these variants may influence observable phenotypes. Limited
access and availability of tissues such as brain has led to the use of blood as a substitute for
eQTL analyses. Methods Here, we evaluate the overlap of eQTL reported in published …
Background
Expression quantitative trait loci (eQTL) are genomic regions regulating RNA transcript expression levels. Genome-wide Association Studies (GWAS) have identified many variants, often in non-coding regions, with unknown functions and eQTL provide a possible mechanism by which these variants may influence observable phenotypes. Limited access and availability of tissues such as brain has led to the use of blood as a substitute for eQTL analyses.
Methods
Here, we evaluate the overlap of eQTL reported in published studies conducted in blood and brain tissues to assess the utility of blood as an alternative to brain tissue in the study of neurological and psychiatric conditions. Expression QTL results from eight published brain studies were compared to blood eQTL identified in from a meta-analysis involving 5,311 individuals. We accounted for differences in SNP platforms and study design by using SNP proxies in high linkage disequilibrium with reported eQTL. The degree of overlap between studies was calculated by ascertaining if an eQTL identified in one study was also identified in the other study.
Results
The percentage of eQTL overlapping for brain and blood expression after adjusting for differences in sample size ranged from 13 - 23% (mean 19.2%). Amongst pairs of brain studies eQTL overlap ranged from 0 - 35%, with higher degrees of overlap found for studies using expression data collected from the same brain region.
Conclusion
Our results suggest that whenever possible tissue specific to the pathophysiology of the disease being studied should be used for transcription analysis.
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