Multi-omics Gene Network Identification (Project 4)

项目总结/文摘

PROJECT SUMMARY/ABSTRACT The goal of Project 4 is to discover neurobiologically interpretable gene networks that underlie opioid addiction (OA) but are otherwise missed by traditional statistical approaches. To achieve this goal, we will apply our multi-omics, multi-method framework—Gene Network Identification and Integration (GNetII)—to identify OA-associated gene networks, by capitalizing on P50 data across human and rodent models. GNetII includes genome-wide epistasis, explainable artificial intelligence, gene network construction, and lines-of- evidence (LOE) methods. These cornerstone methods enable integration of multiple levels of individual-level data, and we will specifically integrate large-scale genome-wide association study (GWAS) data in 220,722 living subjects from Project 1 and the Synergy Core, gene regulation data (RNA-sequencing, DNA methylation, chromatin immunoprecipitation sequencing, and variant genotypes) from multiple addiction- relevant brain tissues (including prefrontal cortex, nucleus accumbens, and amygdala) from 641 OA case and control decedents (deceased individuals) from Project 2, experimental mouse and rat model results from Project 3, and additional public omics data. OA is a leading cause of preventable morbidity and mortality in the United States, afflicting an unprecedented number of U.S. adults and youth. OA is highly heritable (~54%). Yet, few genetic loci have been conclusively identified for OA and related outcomes, and common genetic variant-based heritability explains only 17% of the variance in OA. We hypothesize that epistasis (i.e., interaction of variants or genes) contributes to the missing heritability. Applying big data science methods to large-scale GWAS, gene regulation data in brain tissue, and cross-species data will reveal previously undetected relationships and add knowledge of the neurobiology underlying addiction. We propose the following specific aims: Aim 1: Build OA-associated gene networks via genome-wide epistasis. Aim 2: Build multi-omics networks using postmortem human brain data. Aim 3: Integrate networks across species to find OA-associated gene networks with multiple LOE. Analyses will be performed on the powerful high performance computing architectures at the Oak Ridge National Laboratory which will greatly improve the likelihood of neurobiologically meaningful discoveries. Our study will capture complex networks across the genome to find previously unknown genes and will help explain the neurobiological underpinnings for the genetic loci that emerge from across the P50 and the broader field.