ABSTRACT Genome-wide association studies have uncovered thousands of disease-associated variants that localize to noncoding genomic regions and influence disease risk through altered cis (local) and trans (distal) gene regulation. Though cis regulatory variation has been extensively characterized across many cell and tissue types, trans regulatory effects are more challenging to ascertain. Trans regulation is partly mediated by transcription factors (TFs), a class of proteins that have widespread influence on gene regulatory networks. Regulatory elements containing TF binding sites are enriched for complex disease heritability. Interestingly, many early onset, highly penetrant monogenic diseases are caused by coding variation in the same TFs that are implicated in complex diseases. Together, this suggests that a shared set of regulatory networks underlies a subset of common, complex diseases and rare, monogenic diseases. The goal of this project is to uncover regulatory networks controlled by monogenic disease-associated TFs and decipher their involvement in processes that drive complex disease phenotypes. In Aim 1 (K99), I will perform a low-throughput arrayed CRISPR screen targeting 13 monogenic diabetes TFs, use detailed genomic profiles (RNA-seq for gene expression, ATAC-seq for chromatin accessibility, and CUT&Tag for histone modifications) with integrative machine learning approaches to construct regulatory networks, and incorporate human genetics data to infer how genetic variation propagates through layers of regulatory information. The focus of Aim 2 (K99) is to develop a novel method to jointly characterize the effects of coding variants in transcription factors and noncoding variants in their cognate motifs to construct a comprehensive regulatory interface map. After transitioning to the R00 independent phase, I will apply the approaches developed in Aims 1 and 2 to examine the influence of cell type and stimulation on variant effects and network structure in Aim 3. Collectively, these aims will reveal the fundamental gene regulatory networks underlying phenotypes shared by common polygenic and rare monogenic diseases and provide insight into mechanisms by which disease-associated genetic variation exerts its influence. The specific methods and frameworks developed will be broadly applicable to other comparable complex and monogenic diseases beyond those explored here. To achieve these research objectives, my mentors Dr. Stephen Parker (human genetics and genomics) and Jacob Kitzman (genomic technologies) and I have outlined a comprehensive training plan. To aid with this plan, we have assembled a world-class mentorship committee with diverse expertise in large-scale functional genomics screens (Drs. Melina Claussnitzer and Anna Gloyn), machine learning (Dr. Anshul Kundaje), and human genetics (Drs. Michael Boehnke and Karen Mohlke). I will also receive training in state-of-the-art human organoid culture systems from collaborator Dr. J...