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Review
. 2013 May 6;368(1620):20120362.
doi: 10.1098/rstb.2012.0362. Print 2013.

Expression quantitative trait loci: present and future

Alexandra C Nica  1 , Emmanouil T Dermitzakis
Affiliations
Review

Expression quantitative trait loci: present and future

Alexandra C Nica et al. Philos Trans R Soc Lond B Biol Sci. .

Abstract

The last few years have seen the development of large efforts for the analysis of genome function, especially in the context of genome variation. One of the most prominent directions has been the extensive set of studies on expression quantitative trait loci (eQTLs), namely, the discovery of genetic variants that explain variation in gene expression levels. Such studies have offered promise not just for the characterization of functional sequence variation but also for the understanding of basic processes of gene regulation and interpretation of genome-wide association studies. In this review, we discuss some of the key directions of eQTL research and its implications.

Keywords: expression quantitative trait loci; genetics; regulation.

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Figures

Figure 1.
Figure 1.
A typical eQTL; many SNPs tested against levels of expression measured by a probe or by other means. The panel below illustrates the difference in distributions of expression values stratified by the SNP genotype of the most significant SNP. (Online version in colour.)
Figure 2.
Figure 2.
Correlation between genotype and expression levels in two populations as indicated by a boxplot. The same SNP is highly significantly correlated in (a) population 1 but not correlated in (b) population 2. This is mainly due the population minor allele frequency of the SNP. Box width is proportional to sample size for the corresponding genotypic category. (Online version in colour.)
Figure 3.
Figure 3.
The same regulatory regions and variant could be an eQTL for gene 2 in (a) tissue 1 and for gene 1 in (b) tissue 2, suggesting that limited interrogation of tissues would be misleading for the biological signal underlying disease. (Online version in colour.)
Figure 4.
Figure 4.
Network relationships based on cis and trans eQTLs. Inference of network effects based on genetic effects allow for the determination of the direction of the effect through the network, which refines the network relationships much more finely. (Online version in colour.)
See this image and copyright information in PMC

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