Dissecting the functional effects of non-coding gene regulatory elements

Abstract

One of the most beautiful and challenging aspects in biology is deciphering the complexity of the genome, and how it functions or dysfunctions. It is this intricate complexity that is dependent on developmental stages, time of the day, and tissue types that allows for the proper development of an organism comprising of different tissue types with different functions. Amongst the many complexities, I focused on the gene-regulatory functions of the non-coding genome and its relation to diseases including disease risk, severity, and progression. Over the last few decades, there has been an increase in the research of genetic causes underlying several complex, common multifactorial diseases including metabolic and cardiovascular diseases. While these studies have identified genetic risk loci, they have not directly identified the genetic mechanisms behind what causes those diseases. Identifying genetic mechanisms for complex traits has been challenging because most of the variants are located outside of protein-coding regions, and determining the effects of such non-coding variants remains difficult. Previous studies that explore the genetic mechanism of complex diseases have underscored the need to develop new methods to study non-coding regions and to systematically identify the effects of non-coding variants towards a disease.In this dissertation, I evaluate the hypothesis that non-coding regulatory elements can contribute to disease-relevant traits by altering gene expression levels. I will specifically focus on a common complex disease, polycystic ovary syndrome (PCOS) which is the most prevalent endocrine disorder among menstruating people. Family- and twin-studies have demonstrated a genetic basis to PCOS. Previous studies have identified non-coding genetic variation associated with PCOS risk across populations with different ancestries. However, the functional follow up of these risk loci has been limited. Therefore, there is a gap in addressing the functional effects of genetic variants and regulatory elements impacting PCOS phenotypes. We identified gene regulatory mechanisms that help explain genetic association with PCOS in several loci using high throughput reporter assays, CRISPR-based epigenome editing, and genetic association analysis. To develop approaches to study regulatory elements, I implemented reporter assays at three different scales to create a framework for the regulatory elements across PCOS risk loci. I also implemented experimental approaches that measured changes in gene expression at single cell levels to identify target genes of regulatory elements identified by the reporter assays. Specifically, we identified regulatory elements across PCOS genetic risk loci in cell models of steroidogenesis, H295R cells and COV434 cells. We then identified regulatory elements that controlled the expression of the gene DENND1A, which altered the levels of testosterone produced by the cell models upon perturbation. Lastly, we quantified the regulatory effects of allele-specific genetic variants from a population of PCOS cases and controls. Taken together, we have identified regulatory elements that could contribute to PCOS pathogenesis. More broadly, my results demonstrate the strengths of combining experimental and statistical approaches to identify molecular mechanisms of genetic risk loci contributing to disease pathogenesis.