Title: Integration of omics data to understand the biological causes of disease.

Abstract:

Many complex diseases are highly heritable, and recent large genome-wide association studies (GWAS) have identified a number of individual genetic associations. However, there are some limitations on the results obtained from GWAS. First, GWAS have identified only a small fraction of the heritability of common diseases, so the ability to make meaningful predictions is still quite limited. Second, statistically significant signals resulting from such studies still cannot answer important biological questions such us which are the causal variants and how do they lead to disease. To translate these discoveries into the clinic, as risk markers of pharmacological targets, the causal genetic variants must be defined.

That suggests the necessity of incorporating additional information when studying complex diseases etiology. Motivated by the simultaneous progress in understanding the role of regulatory variants in shaping human phenotypic variation, gene expression has become and obvious candidate as an informative intermediate 'DNA phenotype'. Regulatory variants play an important role in shaping the phenotypic differences among individuals and thus it is very likely to influence the susceptibility to disease. As such, integrating gene expression data and other disease relevant intermediate phenotypes with genome-wide association results could potentially help to prioritize fine-mapping efforts and provide a shortcut to disease biology. Here we provide some examples on the use of gene expression data to enhance our understanding of the underlying biology of complex traits.