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Bridging the gap between genetic variants and functional relevance is a principal goal of human genetics. Despite centuries of research, interpreting the biological mechanisms that link variants to phenotypes is a continuous challenge. This goal applies to rare and common variants, although the specific challenges vary depending on the variant’s frequency and effect on gene dosage or protein structure. Deciphering these variants’ modes of action is crucial for a more holistic understanding of genome regulation. This dissertation advances interpretation of rare and common variants across the annotation spectrum, by utilizing functional data derived from population scale RNA-sequencing studies. Thus, three main research questions are addressed: (1) How do rare variants affect gene expression, and can these subtle changes be robustly detected? (2) How do common variants that influence pre-mRNA splicing influence protein structure and human traits? (3) Can joint effects between common splice-regulatory and rare loss-of-function variants be detected through the lens of purifying selection? All three chapters build on knowledge acquired through large-scale transcriptomics and open access data. Chapter 1 evaluates the utility of allele specific expression to prioritize variants with functional effects. Chapter 2 involves quantifying splicing using the common Percent Spliced In (PSI) metric, and performing quantitative trait locus (QTL) mapping. Chapter 3 builds on the known phenomenon of modified penetrance, where common regulatory variants reduce the pathogenicity of rare coding variants. Ultimately, these three studies will contribute to our knowledge of genome regulation, which will be crucial in a future of personalized medicine.

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The reconstruction of gene regulatory networks is one of the milestones of computational system biology. We introduce a new implementation of ARACNe (Algorithm for the Reconstruction of Accurate Cellular Networks) to reverse engineer transcriptional regulatory networks with improved mutual information estimators and significant improvement in performance. In the context of data driven network inference we identify two major confounding biases and introduce solutions to remove some of the discussed biases. First we identify prevalent spatial biases in gene expression studies derived from plate based designs. We investigate the gene expression profiles of a million samples from the LINCS dataset and find that the vast majority (96%) of the tested plates is affected by significant spatial bias. We can show that our proposed method to correct these biases results in a significant improvement of similarity between biological replicates assayed in different plates. Lastly we discuss the effect of CNV on gene expression and its confounding effect on the correlation landscape of genes in the context of cancer samples. We propose a method that removes the variance in gene expression explained by CNV and show that TF target predictions can be significantly improved.

The human pancreatic cancer cell line T3M4, is known to produce transforming growth factor-alpha (TGF-alpha); as well as overexpress the receptor for this ligand, epidermal growth factor (EGF) receptor. TGF-alpha messenger RNA (mRNA) levels were assayed using northern blot, after addition of epidermal growth factor or TGF-alpha. The level of TGF-alpha mRNA was found to increase 2-fold at 2 hours and then return to near basal levels at 10 hours, after treatment with either ligand. Both ligands were also equipotent in a 2 hour dose response assay, with half maximal stimulation seen at 1 nM and maximal stimulation reached at 4 nM. Furthermore, there appeared to be a 2-fold increase in TGF-alpha transcription as determined by nuclear runoff experiments. Induction of TGF-alpha mRNA coupled with the overexpression of the EGF receptor, may result in a potent autocrine cycle; which may be found in other cancers.