Statistical methods for gene regulatory analysis of substance use disorder

Project Summary Substance use disorder (SUD) presents a pressing global health issue with devastating consequences, including physical and mental health risks, as well as substantial economic burdens. Despite extensive research efforts, the lack of efficient therapies underscores the critical need for a more comprehensive system-level understanding of the underlying mechanisms driving addiction. SUD is a complex trait influenced by genotypes, cellular environments, and drug exposure. However, existing studies often focus on only one or two of these factors, leading to a limited understanding of the full picture. We propose to leverage GRNs as an integrative framework that bridges genotypes, cellular environments, and drug exposure, enabling a more holistic and system-level comprehension of addiction mechanisms. The objective of this research project is to develop cutting-edge statistical methods and bioinformatics tools that will enable a systems-level analysis of SUD. By integrating diverse data types, such as genome-wide association studies (GWAS) and single-cell multiome data from both Drosophila and human brain samples of addicted individuals and controls, we aim to identify crucial genotype-affected addiction-causing cells (“affected cells”), driver regulators, and gene regulatory networks (GRNs) associated with SUD. Aim 1 of the research will focus on developing a novel method to identify "affected cells" that serve as a link between genotypes and SUD. Leveraging data from GWAS, we will compare the expression of SUD-associated genes in addicted individuals and controls, allowing us to pinpoint cellular populations affected by addiction- related genetic variations. This integrative approach will shed light on the cellular mechanisms that contribute to addiction susceptibility. In Aim 2, we will advance state-of-the-art methods to elucidate the causal reasons behind differentially expressed genes observed in addicted individuals compared to controls. By investigating the role of driver regulators responsible for addiction, we will identify key GRNs governing addictive behaviors. This integrated analysis will provide a comprehensive view of the regulatory landscape underlying SUD. The outcomes of this project are poised to advance understanding of addiction mechanism significantly. Successful implementation will result in the development of broadly applicable novel methodologies, offering a new perspective on understanding complex traits, including SUD, from a systems biology viewpoint. Specifically, we aim to unravel how driver regulators respond to substances and influence genes and chromatin through GRNs, gradually leading to addiction. This enhanced system-level understanding of addiction biology will have far-reaching implications, paving the way for the development of new therapeutics aimed at modulating or reversing the transcriptional or regulatory actions contributing to SUD.