| dc.description.abstract | The improper regulation of gene expression is a main cause of complex disease. Non-coding single nucleotide variants influence disease risk by altering gene regulatory elements, such as promoters and enhancers. In addition to single nucleotide variants, larger structural variants can also disrupt proper gene regulation and lead to disease by changing both individual regulatory elements and the three-dimensional chromatin architecture. Despite numerous examples of enhancers that are mutated in disease, predicting whether mutations in a given enhancer will influence a phenotype is still a difficult task. This dissertation addresses two substantial gaps in our understanding of gene regulation caused by our limited ability to identify and interpret gene regulatory elements. First, many approaches are commonly used to identify enhancer sequences on a genome-wide scale, but no gold-standard set or strategy exists. This dissertation demonstrates substantial disagreement between enhancer identification strategies in common use and shows that these differences complicate their use in biomedical research. Second, strategies for interpreting enhancer mutations consider enhancers in isolation, despite evidence that redundancy in some mammalian enhancer landscapes buffers the phenotypic effects of enhancer loss on the expression of genes. This dissertation introduces a framework for defining and interpreting enhancers in their regulatory “landscapes” that accounts for the many context-dependent enhancer elements that influence a gene’s expression. We show that features of the enhancer landscape, including the number and tissue-specificity of enhancer elements, are associated with gene-level attributes and the likelihood of enhancer turnover. We also provide evidence of selection against structural variants that putatively alter regulatory elements and three-dimensional chromatin architecture. In summary, this work highlights key limitations in our understanding of enhancer annotations, defines an integrative model of gene regulatory landscapes, and quantifies the impact of genetic variation in the context of enhancer landscapes. Consistent and specific definitions of the spectrum of enhancer activity and regulatory interactions are necessary for further progress in enhancer identification and accurate interpretation of the effects of genetic variants on disease risk. | |
