Our Science

The NIDA Core Center of Excellence in Omics, Systems Genetics, and the Addictome (OSGA Center) builds and empowers a community of substance use disorder (SUD) researchers who use big data approaches to establish causal linkages between molecular and cellular traits and risk of substance use and misuse, risk of relapse, and treatment efficacy. The Center is organized around three themes: community, data, and tools.

Overall Center Aims

Community

Train current SUD researchers, recruit new researchers, and build communities around omics, systems genetics, and computational modeling through hands-on webinars, dynamic workbooks, the pilot grant program, Building LeGenDS, IROC, and the Addiction Genetics and Epigenetics Data Jamboree.

Data

Expand and optimize foundational rat omics datasets. Genome, transcriptome, and proteome resources for the NIH Heterogeneous Stock (HS) and the Hybrid Rat Diversity Panel (HRDP) are available now, with new proteomics, single-nuclei transcriptomics, RNA modification, and DNA methylation data being added.

The Cores and Their Specific Aims

Tools

Provide cutting-edge statistical and computational tools for the latest omics technologies, multi-omic integration, modeling of SUD, and FAIR curation and sharing of data and models.

Administrative (Admin) Core

Administrative oversight, community management, training, and stewardship of the Center's data resources.

  • Aim 1 - Community: Manage the within-center community of staff, supported grants, and pilot grants, and engage the broader NIDA genetics community through cross-core meetings, an annual all-hands meeting, and evaluation of outreach and training. Expand online training through monthly webinars, dynamic workbooks, and microlectures.

  • Aim 2 - Data: Update and maintain omics databases on foundational rat populations, adding knowledge of translation, splicing, cell-type and brain-region specificity, network connectivity, and genetic influence on RNA expression, protein levels, and methylation. Serve as the clearinghouse for all OSGA-generated data, deposited in appropriate public repositories including the NeMO Archive.

  • Aim 3 - Tools: Develop tools that promote outreach and data sharing with other NIDA centers and major data resources, including the theAddictome website as a hub for the NIDA genetics community.

Pilot Core

Funding and mentoring for early career investigators and new collaborations entering systems genetics.

  • Aim 1: Restart the collaborative two-lab pilot grant program for early career addiction researchers working across two or more laboratories or institutions. Awards provide up to $20,000 per year for two years and support novel addictome-related omics data generation, novel statistical methods and bioinformatics tools, and use of the OSGA mini-panel of inbred rat strains.

  • Aim 2: Evaluate pilot applications on scientific merit, strength of the hypothesis, and potential to build durable collaborations. Review criteria emphasize collaborative multi-lab projects, open-access rat addictome data and tools, and novel technologies and systems genetics methods.

  • Aim 3: Provide continued feedback and follow-up, including quarterly calls and center meeting presentations, to prepare pilot recipients for independent NIH applications in addiction research. Expected outcome: four pilot awards totaling $40,000 each over two years.

Systems Analytics and Modeling (SAM) Core

Research support core for downstream data analysis, genetic mapping, and multi-omic integration.

  • Aim 1 - Community: Develop the pipeline of data scientists in genetics and SUD research through the Building a Legacy for Genetics and Data Science (LeGenDS) program, a 10-week research internship with a tangible research product, plus open office hours for students and scientists at any career stage.

  • Aim 2 - Data: Generate a foundational, well-curated rat proteomics dataset covering 10 inbred strains and 20 F1 crosses (partial diallel), both sexes, and three brain regions (nucleus accumbens, medial prefrontal cortex, amygdala), with analysis tools for proteomics and diallel designs.

  • Aim 3 - Tools: Provide broad access to new statistical and computational methods, including a pangenome-aware linear mixed model implementation for QTL mapping in HRDP and HS populations, fast matrix factorization and matrix linear models for multi-omic integration, and interactive notebooks for non-programmers.

Single Molecule AI-assisted RaT omics (SMART) Core

Research support core generating long-read genomic, epigenomic, and transcriptomic resources.

  • Aim 1 - Community: Enhance access to rat genomic resources through the International Rat Omics Consortium (IROC), the forum that has guided rat reference genome upgrades from Rnor_6.0 to mRatBN7.2 and GRCr8, keeping the NIDA community current on rat genomics and pangenomics.

  • Aim 2 - Data: Generate brain region-specific single-molecule long-read sequencing data from rat strains with contrasting addiction phenotypes, including whole-genome CpG methylation profiles, direct RNA sequencing, and long-read single-nuclei RNA-seq from nucleus accumbens, prefrontal cortex, and amygdala.

  • Aim 3 - Tools: Analyze sequencing data with conventional and AI-assisted methods, including a fine-tuned single-cell Generative Pre-trained Transformer (scGPT) model for cell-type annotation and variant-effect prediction and retrieval-augmented generation with large language models to summarize gene knowledge and generate SUD hypotheses.