Using gene expression to investigate the genetic basis of complex disorders

Abstract

The identification of complex disease susceptibility loci through genome-wide association studies (GWAS) has recently become possible and is now a method of choice for investigating the genetic basis of complex traits. The number of results from such studies is constantly increasing but the challenge lying forward is to identify the biological context in which these statistically significant candidate variants act. Regulatory variation plays an important role in shaping phenotypic differences among individuals and thus is very likely to also influence disease susceptibility. As such, integrating gene expression data and other disease relevant intermediate phenotypes with GWAS results could potentially help prioritize fine-mapping efforts and provide a shortcut to disease biology. Combining these different levels of information in a meaningful way is however not trivial. In the present review, we outline the several approaches that have been explored so far in this sense and their achievements. We also discuss the limitations of the methods and how upcoming technological developments could help circumvent these limitations. Overall, such efforts will be very helpful in understanding initially regulatory effects on disease and disease etiology in general.

Figure 1.

Expression and disease association studies. Current expression studies are performed on a limited number of tissues derived from a small number of individuals compared with disease association studies. The availability of large collections of cell types from a large number of individuals will allow the identification of eQTLs that explain disease effects, such as the one at the top of the panel, when the relevant cell type/tissue is analyzed, as indicated for Tissue C at the bottom of the panel. This may not be successful in less relevant tissues such as in the case of Tissues A and B at the bottom of the panel.

Figure 2.

Local superimposition of whole-genome disease and expression associations. For any given genomic interval, same SNPs will have been independently tested for associations with a disease and transcript levels of a set of genes, respectively. (A) No significant eQTLs detected in the interval for Gene A. (B) Significant eQTLs for Gene B do not also display significant disease associations. (C) The association patterns of disease and Gene C expression fit very well, making it the most likely candidate out of the three for a possible regulatory-mediated disease effect.