The goal of the proposed research is to discover neurobiologically interpretable gene networks for addiction,
specifically for cigarettes and opioids. To achieve this goal, we will apply a new multi-omics, multi-method
framework—Gene Network Identification and Integration (GNetII)—to identify gene networks associated
uniquely with cigarette or opioid outcomes and networks shared across these addictions. GNetII includes
genome-wide epistasis, Explainable Artificial Intelligence, gene network construction, and Lines-of-Evidence
methods. These cornerstone methods will enable integration of large-scale genome-wide association study
(GWAS) data in living subjects, postmortem human brain data (RNA-sequencing, DNA methylation,
chromatin immunoprecipitation sequencing, and variant genotypes) from addiction case and control
decedents (deceased individuals), and public omics data. Cigarette smoking and opioid outcomes are
genetically correlated, and we have parallel GWAS and multi-omics brain data available in two highly relevant
tissues, dorsolateral prefrontal cortex and nucleus accumbens, for both of these addictions.
Cigarettes and opioids are leading causes of preventable morbidity and mortality in the United States. These
addictions affect millions of U.S. adults and youths and are highly heritable (e.g., 54% and 71% for opioid
addiction and nicotine dependence, respectively). GWAS analyses have identified 300+ loci at genome-wide
significance for smoking. GWAS for opioids are farther behind in sample size, but genome-wide significant
loci are emerging. Neurobiological effects of known loci are largely unknown, and more loci and connections
among the loci are still to be discovered. We hypothesize that applying new big data science methods to
large-scale GWAS and gene regulation data in brain tissue will reveal previously undetected relationships
(such as epistatic interactions between genes) and add knowledge of the neurobiology underlying addiction.
We propose the following specific aims:
Aim 1: Integrate multi-omics data to discover cigarette-associated gene networks.
Aim 2: Integrate multi-omics data to discover opioid-associated gene networks.
Aim 3: Integrate multi-omics data to find general addiction-associated gene networks.
For cigarettes, Aims 1 and 3 will leverage GWAS (N=528,259) and multi-omics data in postmortem human
brain from active smoker and nonsmoker decedents (N=262). For opioids, Aims 2 and 3 will use GWAS
(N=49,178) and multi-omics brain data from opioid overdose case and control decedents (N=147). Analyses
will be performed on Summit, the world's fastest supercomputer, which will greatly improve the likelihood of
neurobiologically meaningful discoveries. Our study will capture complex networks across the genome to find
previously unknown genes, as well as help explain the neurobiological underpinnings for the growing number
of genetic loci associated with cigarette or opioid outcomes.