This Notice encourages applications for research projects that identify, validate and/or functionally characterize loci, genetic variations and haplotypes that are associated with vulnerability to addiction and that potentially inform the likelihood of responsiveness to treatment. Data may be collected from the general population, special populations, recent admixed populations, and/or electronic medical records (EMR). Secondary data analysis of data collected from the general population, special populations, recent admixed populations, and/or animal models is also appropriate for this notice. Investigators are encouraged to include functional characterization, gene x gene interactions, gene x environment interactions, and gene x environment x development interactions.

Background and Research Objectives

The success of identifying genetic loci for nicotine and alcohol as well as for other complex genetic disorders has thus far been the result of the collection of large samples of people of European descent. Identification of genetic variants for cannabis and opioids are beginning to be discovered, but much larger samples are required to increase power to discover new variants and replicate these preliminary findings. Genome Wide Association Studies (GWAS) for cocaine and methamphetamine are currently underpowered for detecting genetic loci. Thus, applicants are encouraged to recruit new cohorts to increase sample size for Genome Wide Association Studies (GWAS) and whole genome wide sequencing studies for Opioid Use Disorder, Amphetamine Use Disorder, Cocaine Use Disorder, and Cannabis Use Disorder. Exposed controls are also needed for these studies. There are opportunities to increase sample sizes for genetic analysis of substance use disorder using electronic health records (EHR) in health care systems and biorepositories where participants are already genotyped.

Recruitment of individuals of non-European ancestry suffering from these disorders is also encouraged to assist in the identification of causal variants, ancestry specific variants, and to understand the role of genetic background in substance use phenotypes.

Applicants are encouraged to integrate functional genetic studies such as global and single cell gene expression analysis, expressed QTL analyses, epigenetic modifications, and higher order chromatin modifications in relevant tissues with genetic mapping studies. Functional genetic studies increase power by reducing the number of multiple comparisons and contribute to the functional significance of individual GWAS hits. Improved statistical methods are needed to layer these types of functional studies on GWAS data.

To augment underpowered GWAS studies of SUD, investigators are encouraged to explore the feasibility of integrating genetic data derived from genetic studies of substance use disorder-relevant phenotypes and behaviors in model organisms.

Given the complex phenotypes associated with substance use disorders and their comorbid conditions, we encourage the use of innovative genetic and statistical methods that incorporate epistasis, gene x gene, gene x environment and genetic pathway interactions to capture the etiology of these complex behaviors. When possible, we encourage the use of standard phenotypic measures when applicable to facilitate comparison of results among different studies. We also seek studies that balance detailed phenotypic documentation with sample size and feasibility.

Phenotype definition of case and control (exposed and unexposed) individuals is a central issue in the analysis of complex traits and is an essential component to applications responding to this Notice. Investigators are encouraged to use existing instruments that have been used by the NIDA Genetics Consortium, http://nidagenetics.org/ and browse through the study information links to access the instruments. In the case of using electronic health records there is a need to develop algorithms to derive diagnoses for substance use disorders.

Alternative phenotype definitions may better describe the genetic aspects of addiction. Therefore, investigators may propose to use other phenotypic information, such as the presence or absence of biological markers or exhibition of unique individual traits, as well as combinations of these and/or co-morbid conditions.

Examples of broad research topics include, but are not limited to:

Molecular genetics approaches:

• Identify chromosomal loci and/or genes, gene variants, and haplotypes associated substance use disorder with or without co-morbid mental disorders using high-density whole genome scanning, deep sequencing, or whole genome sequencing in human populations. Genetic studies of opioid use disorder (OUD), amphetamine use disorder (AUD), cocaine use disorder (CocUD), and cannabis use disorder (CanUD) are of special interest to this funding opportunity. Large sample sizes with exposed controls are needed for these studies. The use of electronic health records (EHR) in health care systems and biorepositories where participants are already genotyped to increase sample size is highly encouraged
• Use deep sequencing to follow-up GWAS or other approaches that have identified SNPs to identify all variants (common, rare, structural, copy number, etc.) in the associated region
• Identify the role that transposable elements play in SUD
• Discover the role that imprinted loci play in SUD
• Integrate functional genetic studies such as global and single cell gene expression analysis, expressed QTL analyses, epigenetic modifications and Hi-C data in relevant tissues with genetic mapping studies to identify causal variants
• Develop larger human trans and cis QTL, methlylation QTL, histone modification data sets in human brain tissue for SUD
• Create larger Hi-C, Chip-seq, and DNA datasets across different tissues
• Identify how ancestry and genetic background affects genetic associations with SUDs as well as gene regulation
• Incorporate protein-protein interactions into network analysis to improve performance of networks
• Augment underpowered GWAS studies of SUD by integrating genetic data derived from genetic studies of drug abuse in model genetic organisms

Phenotypes:

In addition to encouraging the use core elements for phenotyping SUD (see research strategy section of this Notice), harmonizing demographic information and substance use history applicants may propose to:

• Examine quantitative phenotypes to assess the molecular genetics of drug addiction and addiction vulnerability
• Develop algorithms for diagnoses of SUD in electronic health records
• Develop algorithms, AI, and deep learning applications to identify gene network, epistasis, and genetic modifiers
• Use advanced analytical methods such as principal-components analysis, discriminant analysis, artificial neural networks, spectral analysis of EEG, neuroimaging for drug activity/re-activity, and/or pharmacokinetic/ pharmacodynamic genetic profiling may help define groups of phenotypes with a higher heritability for complex traits such as drug response phenotypes and treatment response phenotypes
• Analyze family studies to identify biomarkers, endophenotypes, and sub clinical phenotypes associated with addictive disorders with or without co-morbid conditions, and environmental factors and processes that may moderate or mediate individual risk and protection
• Correlae drug response profiles with intermediate phenotypes (e.g., brain imaging, learning and memory, executive function)
• Research incorporating the evaluation of environmental exposures or psychosocial stressors with the molecular approaches
• Studies analyzing the combined effects of gene variants from a gene family or pathway shown to be involved in addiction and/or co-morbid mental disorders

Statistical genetics and computational approaches:

• Application of machine learning and artificial intelligence to identify causal variants of SUD
• Computational approaches incorporating other data resources, such as HapMap, 1000 Genomes, 4D genome, Epigenomics, dbGaP, FUMA, MAGMA-C, etc are encouraged, especially as they relate to understanding relationships among genes and environment, genes and phenotype, and systems biology of addiction
• Structuring software to make it more modular and interoperable for application of different algorithms to integrate ChIP-seq for histone modification, DNA methylation, Hi-C, RNA-seq, splice variants, and structural variants together. Simple tools to merge ChIP-seq for histone modification, methylation, Hi-C, RNA-seq, splice variants, and structural variants are lacking. Integration of Hi-C contact with H3K27ac to predict gene expression with higher accuracy
• Develop algorithms, AI, and deep learning applications to identify gene network, epistasis, and genetic modifiers
• Develop data models that aggregate data and map studies onto each other
• Develop algorithms for research questions and integrative analysis across different data types is needed
• Create methods to access to raw primary data is needed without loss of de-identification, while maintaining likability of the same individuals across data types (e.g., genotypes and RNAseq) whose data are stored in different archival platforms (e.g., dbGaP and GEO)
• Research on statistical analyses of large data sets examining the genetics of addiction with or without co-morbid mental disorders, as well as use of innovative analytical approaches such as admixture designs and Bayesian methods; secondary data analyses are also of interest

Gene-Environment Interactions:

Applicants interested in studying gene-environment interactions should refer to (NOT-DA-19-038) Notice of Special Interest (NOSI): Gene-Environment Interplay in Substance Use Disorders (R01, R21)

Application and Submission Information

This notice applies to due dates on or after June 5, 2020 and subsequent receipt dates through September 8, 2023.

Submit applications for this initiative using one of the following funding opportunity announcements (FOAs) or any reissues of these announcement through the expiration date of this notice.

• PA-19-055 - NIH Research Project Grant (Parent R01 Clinical Trial Required)
• PA-19-056 - NIH Research Project Grant (Parent R01 Clinical Trial Not Allowed)
• PA-19-091 NIH Research Project Grant (Parent R01 Basic Experimental Studies with Humans Required
• PAR-19-259 NIDA Research Center of Excellence Grant Program (P50 Clinical Trial Optional
• PAR-19-345 NIDA Program Project Grant Applications (P01 Clinical Trial Optional)

For funding consideration, applicants must include NOT-DA-20-030 (without quotation marks) in the Agency Routing Identifier field (box 4B) of the SF424 R&R form. Applications without this information in box 4B will not be considered for this initiative.

All instructions in the SF424 (R&R) Application Guide and the funding opportunity announcement used for submission must be followed, with the following additions:

Research Strategy:

When preparing an application, researchers are strongly encouraged to present a rationale that carefully balances important substantive, methodological, and budgetary issues.

Applicants are encourage to use the phenotypes and phenotypes assessments for substance use in the Phenx Tool Kit to enable harmonization with other studies to increase power. https://www.phenxtoolkit.org/search/results?searchTerm=substance+abuse&searchtype=smartsearch and harmonize with previously funded studies (https://nidagenetics.org/)

Resource Sharing Plan: Individuals are required to comply with the instructions for the Resource Sharing Plans as provided in the SF424 (R&R) Application Guide, with the following modification:

• All applications, regardless of the amount of direct costs requested for any one year, should address a Data Sharing Plan.
• Detailed plans for data sharing acknowledging a willingness to adhere to the genomic data sharing policies that have been indicated in NOT-OD-14-124 are expected to be addressed for all human genetics studies. Data should be assigned a DOI and research resources a research resource identification number. Additional information and a model data sharing plan can be found at: https://www.drugabuse.gov/about-nida/organization/divisions/division-neuroscience-behavior-dnb/genetics-epigenetics-developmental-neuroscience-branch-gedn/faqs-related-to-nid

Applications nonresponsive to terms of this NOSI will be not be considered for the NOSI initiative.

Please direct all inquiries to the contacts in Section VII of the listed funding opportunity announcements with the following additions/substitutions:

Scientific/Research Contact(s)

Jonathan D. Pollock, Ph.D.
National Institute on Drug Abuse (NIDA)
Telephone: 301-435-1309
Email: pollock@mail.nih.gov