Genetic effects on gene expression across cell types, tissues and biological contexts

Abstract

The human body consists of many tissues (e.g. brain, blood, skin or fat) which in turn are made of many different component cell types (e.g. neurons, monocytes, fibroblasts or adipocytes). The identities and functions of different cell types are defined by the different sets of genes that they express. Similarly, genetic differences between individuals can alter gene expression levels and in turn influence one’s risk of developing various complex diseases. Specific genetic variants associated with gene expression levels are referred to as expression quantitative trait loci (eQTLs). While multiple studies have demonstrated that the eQTL effect sizes vary between cell types and tissues, the magnitude of this variation has remained unclear. Although small studies focusing on purified cell types have generally reported large differences in eQTL effect sizes between cell types, the largest analysis of gene expression across 49 human tissues by the GTEx project found a high level of eQTL sharing between tissues. Furthermore, different analytical choices have made it difficult to compare results from different studies. Fortunately, the eQTL Catalogue project has recently released uniformly processed eQTL summary statistics from 19 individual studies. In this thesis, we used the eQTL Catalogue summary statistics to estimate the sharing of eQTLs across up to 46 individual cell types and tissues. Consistent with previous reports, we find high levels of eQTL sharing between tissues. In contrast, there was much less sharing between purified cell types. This suggests that high tissue-level sharing is driven by sharing of cell types between tissues and averaging of effect sizes across many different component cell types. This was further supported by factor analysis, which revealed that eQTL effect sizes in tissues were comprised of multiple shared and cell-type-specific components. Finally we tried use the cell-type-specific eQTL components to interpret complex disease associations, but did not find compelling evidence for specific enrichments. Our results indicate that much larger datasets from purified cell types are needed to completely interpret eQTL signals detected in complex tissues.