Revised August 2015
Substance use disorders (SUDs) are complex conditions that develop over time and are characterized by increasing escalation in use defined by stages of initiation, abuse, and addiction that are often experienced repeatedly due to cycles of withdrawal and relapse. Not all individuals who initiate drug use progress to addiction. Genetic epidemiology suggests that these different stages are influenced by the environment, the age of a person when drug exposure takes place, and their genetic vulnerability. Initiation and dependence share some common genetic factors but unique genetic factors also underlie the different stages of substance use, as well as individual vulnerability to particular substances. Elucidation of genetic factors and epigenetic regulation of gene expression influenced by the environment are essential to understanding the biological basis of substance use disorders. Ultimately, understanding the interplay of genetic and environmental factors that influence the SUD trajectory over the course of human development should contribute greatly to developing effective and personalized prevention and treatment interventions for SUDs.
Priority 1: Improved Methods for Gene Identification
The primary challenge of GxExD research is to determine how to optimally address or detect relatively small genetic effects that each variant contributes to the overall heritability, and then examine their interplay with changing environments, and across human development. Examining all three variables at once in a single study is extraordinarily difficult because of the problem of multiple comparisons and need for extraordinarily large sample sizes; moreover, the ability to use established samples for such studies is hampered by the challenges of data harmonization. Genome wide association studies (GWAS) have been one of the most productive methods for identifying genetic variants associated with disease which is essential for understanding their underlying biology and interplay with environmental factors. When individual markers do not achieve statistical significance in GWAS, polygenic risk scores based on the weighted sum of variants derived from a training set may be useful in predicting risk and interactions with environmental exposures in smaller cohorts and in epidemiological studies where environmental exposure is well measured and can be developmentally stratified. The same strategy with smaller cohorts can be followed once confirmed genetic variants are identified.
- Increase investment in human molecular genetics for large sample genome wide association studies (GWAS) to identify genetic variants that contribute to SUDs.
- Support increased sample sizes for adequately powered GWAS studies. The current standard for genome-wide significance is 5 x 10-8 under the assumption of 1 million independent statistical tests. (The alternative of using a smaller number of candidate SNPs has not been successful for most complex traits).
- Use of novel quantitative and polygenic genetic methods and statistical models to integrate the GWAS efforts with phenotype identification, with examination of environmental effects, and with examination of more complex genetic effects such as developmental changes.
- In parallel to the GWAS efforts, support longitudinal studies in population-based samples and perhaps twin families that collect biospecimens for a variety of genomics to examine the impact of development over the life span.
- Integrate genotype data with post-mortem brain epigenomic and gene expression data from addiction cases and controls. This approach may leverage existing archives of normal brains and resources such as the Genotype-Tissue Expression (GTEx) program but will need to develop data from brains of those with addictions.
- Support studies that utilize new technologies that provide unprecedented detail of SUD genetic variants within a 3D spatial context. For example, use of next generation sequencing in family- based designs may identify rare variants in chromosomal regions shown to be associated with SUDs. Coupling Hi-C technology with next generation sequencing not only reveals the origin of the gene variants, but also the 3D organization of the chromosomal region within the context of the genome.
- Functionally validate and characterize newly identified gene variants using genome editing technologies such as CRISPR/Cas9. This technology enables new opportunities for characterizing the function of newly identified human gene variants by creating animal knockins which can be further explored using in vivo imaging.
- Complete genotyping of samples in NIDA Genetics repository.
- Promote access to large sample sizes containing tens of thousands of subjects with serious forms of addiction, ethnic diversity, and different patterns of comorbidity.
- Support for methods research to design strategies to analyze complex traits which will increase the pay-off of the data being collected.
Priority 2: Epigenetic Approaches
Behavioral models of addiction focus primarily on the behavioral responses elicited by drugs as a way to measure drug reward; however, a more precise knowledge of the biochemical and molecular pathways that produce these behavioral changes is required for understanding the development of addiction. Recent epigenetic studies link drug reward to gene transcription regulation by chemical modification of both DNA and DNA-associated proteins. This has opened a new and exciting avenue for addiction research. Recent advances in epigenome-editing using techniques such as CRISPR/Cas9 provide the necessary tools to influence reward phenotypes by precise in vivo manipulation of epigenetic states at specific loci within distinct neuronal populations.
- Encourage case/control epigenome-wide association studies (EWAS) using postmortem brain tissue. The cell-type specificity of epigenomes necessitates that etiologic studies investigate brain tissues rather than peripheral tissues.
- Validate animal epigenetic findings in human post-mortem brain tissue and determine their similarities and differences. These studies are expected to reveal the role of epigenetic mechanisms in substance abuse and to provide a firm foundation for new epigenetic therapeutics that can target epigenetic enzymes and pathways.
- Genome wide association studies (GWAS) have largely identified variants in non-coding regions. Integration of ‘Omics information (such as DNA modifications, histone modifications, transcriptome, regulatory RNAs) with GWAS, will improve discovery power and greatly assist in interpretation of the functional implications of the GWAS findings, leading to the valid and potentially more powerful methods for identifying causative alleles.
- Encourage studies examining additional regulatory mechanisms of the genome, such as noncoding RNA.
- Further develop non-invasive imaging technology to measure epigenetic changes in the brain.
- Collect peripheral samples from human longitudinal studies (ABCD Study) to investigate dynamic changes over time (epigenomic, non-coding RNA, metabolomic, proteomic, or transcriptomic) associated with the development of SUDs for biomarker development.
- Use of pluripotent stem cells to link molecular information during neuronal development as a complement to postmortem data approaches utilizing tissue collected across the lifespan from both human and animal specimens. Identification of epigenetic changes during development are particularly challenging for human studies. Studies in rodents and non-human primates can provide information on how the epigenome changes with time and environmental exposure.
- Significant investment in brain banks of addicted individuals
- Investment in bioinformatics resources to analyze sequencing and other data arising from epigenetic studies in animals and humans.
- Better technologies to obtain epigenome-wide information from one or very few cells of the same type.
Priority 3: Improved Phenotyping
The heterogeneity of substance use phenotypes and the yet-unidentified human genetic variation contributing to SUDs both offer challenges to GxExD research. Optimizing phenotyping strategies enhances the possibility of identifying genes, relevant environmental risk and protective factors, and the developmental stages associated with SUD behavior.
- Standardize phenotyping across genetic studies as much as possible. This may involve developing a core set of variables to be applied to all NIDA genetic studies. Use of the PhenX tool kit measure may help address this standardization.
- Support efforts that utilize relatively homogeneous or severely affected SUD phenotypes, which may provide greater statistical power for discovery.
- Rather than initially conducting costly in-depth environmental assessments, encourage brief phenotyping strategies to facilitate gathering the large samples necessary to detect small effects.
- Follow up confirmed discoveries from GWAS and animal studies with smaller, longitudinally followed samples that include deep phenotyping of SUD behavior and relevant environmental variables.
- Utilize biomarkers associated with drug use or metabolism.
- Leverage existing resources such as those coming from the ABCD study, uniform electronic patient records, or existing large population cohort studies.
- Access to large samples of genotyped individuals with SUD-relevant phenotypes as well as longitudinally studied, comprehensively assessed samples of children and adolescents that make possible evaluation of gender, ethnicity, and different forms of addiction.
- Improve behavioral measures so that findings can be translated between human and animal models.
Priority 4: Improved Characterization of Environmental Influences
Numerous environmental factors have been found to correlate with elevated risk for initiating substance use and the development of SUDs; however, it is both challenging and important to distinguish which of these factors play a causal role in the trajectories toward SUDs and which are markers of risk, so that malleable environmental factors can be correctly identified and appropriate interventions implemented during sensitive developmental periods. One complicating factor is the role of gene-environment correlation, wherein genetic factors contribute to the likelihood that an individual will be exposed to particular types of environments. Environmental factors with the strongest evidence for a role in SUD trajectories and gene-environment interplay include: prenatal substance exposure, early stress, child maltreatment, peer influences, parental monitoring, and early initiation of substance use.
Studying GxExD currently requires large sample sizes, due to the small effect sizes of available gene variants. To address this need using existing samples, data need to be combined across studies, which is much more challenging and resource-intensive than is often appreciated.
- Enhance power by combining data from multiple studies; approaches include data federation, data harmonization, and integrated data analysis. A less stringent approach would be to combine data based on the construct rather than specific questions.
- For future studies, it may be helpful to use common measures, such as those in the Consensus Measures for Phenotypes and eXposures (PhenX) toolkit; initially brief or proxy measures such as zip code or educational attainment; and new mobile technologies for self-report and for physiological measurements.
- Measuring the level of exposure to environmental variables, and the timing of environmental exposure, are also important.
- Population-based approaches, including twin and national registries, offer advantages including size, representativeness, ability to control for familial processes, and sometimes detailed environmental measures.
- Animal models may be useful to pinpoint salient environmental exposures and critical developmental timing of exposure.
- Investment in new mobile technologies that quantitatively measure the environment and biomarkers for substance abuse.
- Invest in efforts to integrate data across multiple samples and address the challenges inherent in data integration in order to promote large-scale collaborative GxExD studies.
Priority 5: Integration of Animal and Human Studies
Genetic and epigenetic studies in both animals and humans have begun to provide insight for understanding the neurobiological mechanisms associated with risk for and development of substance use disorders (SUDs); however, linking individual findings from animal models with particular phenotype(s) exhibited in humans remains challenging. Unraveling the complex etiology of SUDs requires mechanistic insights provided by animal models that are not possible in human subjects. New approaches, new populations, and new genomics tools are allowing the identification of genetic and epigenetic factors more quickly than ever before. Currently, the field is in the very early stages of identifying genetic and epigenetic factors that are relevant to addiction. Much less is known about the common circuits that exist across model organisms that contribute to risk for and development of SUDs. Moving forward, greater integration of findings generated in both human and animal studies is required to elucidate the common genetic and biochemical pathways involved in SUDs to enable the development of therapies for this largely undertreated population.
- Increase the use of quantitative measures in human studies to enable better integration of human genetic variation with deep insights from animal genetic and genomic studies. The use of the following quantitative continuous traits will be helpful in translating animal and human genetics: amount of drug ingested, frequency of use, length of abstinence; somatic and affective symptoms of drug withdrawal, preference or sensitivity for non-drug rewards, novelty preference or novelty seeking, increased incentive motivation for reward-related stimuli; sensitivity to develop escalation of drug taking; impulsivity, poor cognitive flexibility (e.g., reversal learning, set shifting, etc.); resistance to punishment during drug-seeking persistent responding in the absence of drug; heightened relapse and reinstatement; enhanced stress reactivity; disrupted circadian rhythms. Moreover, traits shown to be heritable in animals (e.g. impulsivity) that more closely underlie the biological mechanism provide increased power and reduce the need for ever increasing sample sizes.
- Map quantitative genetic traits in inbred and outbred rodents to examine the degree to which various traits or phenotypes share a common genetic network.
- Support genetic analysis of complex traits in inbred strains of rodents where environmental variables and timing of exposure can be controlled, providing invaluable insight into GxE.
- Genetically mapping substance abuse-related phenotypes in model organisms may suggest candidate genes to test in human populations and provide powerful insights into the mechanisms of substance abuse.
- Support translation of epigenetic findings gathered from addiction studies in animal models to humans.
There is a critical need to share the data collected by various research teams to synchronize research efforts, reduce duplicative investment, and increase statistical power to generate GxExD findings. Specifically,
- There is a critical need for identifying and sharing of phenotypic data for GxE studies. The dbGaP database includes some phenotypes, but they represent only a small set of the data collected. NIDA needs to think about which phenotypes embody the core characteristics necessary for allowing more investigators to use them across other studies and to conduct replication studies involving GxE interactions.
- Provide incentives to encourage investigators to share data by providing the necessary resources required for preparing data for sharing and offer researchers credit for sharing.
- Data integration (which requires sharing and data harmonization) is necessary to build large datasets to increase our chances of new discoveries and replication of these studies.
- Data must be curated in a way that meets both confidentiality and full access requirements.
GxExD studies require great breadth and depth of knowledge in genetics, development, bioinformatics, epidemiology, digital technology, and research design. These challenges may be met by
- Encouraging collaborations among individuals from different fields (inter-individual, interdisciplinary collaborations rather than intra-individuals interdisciplinary training). Designing a program for single investigators to attain the appropriate breadth and depth of knowledge in all areas would require lengthy training that could be more efficiently achieved through collaborative networks of scientists possessing depth of knowledge in a given discipline.
- Supporting both training and R grants in methods development for the analysis of rich and informative developmental samples with GxE interaction and covariation.
- Support meetings/workshops that train scientists to use resources to analyze GxExD samples.
- Support meetings that encourage interaction between investigators that use animal and human models to analyze GxExD.
- GxExD studies require a combination of methods development, bioinformatics, software development, and training in this methodology and the corresponding software. Methods development funding mechanisms are critically needed to create these vital tools needed to propel the field forward.
- SBIR/STTR funding for small businesses may provide an avenue for accessing software and corresponding training generated by other teams to GxExD studies, making them broadly accessible to the field.
- Increased support for secondary analyses of existing data sets.
Leveraging Technologies and Innovations from other Fields
- Develop nanotechnologies that can target specific cell types to correct aberrant function through genetic or epigenetic modifications.
- Wearable sensors; mobile technology that tracks data collected from sensors and includes geographic location tracking technology; use of social media and web site tracking
- Model the successes achieved in identifying genes involved in schizophrenia for gene identification for other outcomes.
- Harness methods from other fields to protect data confidentiality, handle missing data, model complex data (including methods for causal inferences), secure data dissemination, federation, and integration.
- Adopt advances in sequencing and other genetic technologies: 1) various polygene methods – GCTA, LD-regression, Polygenic Risk scores; 2) advancements in classes of gene annotations (Encode, expression arrays etc.) that can move beyond single variant markers – that may have very low power in the absence of really large samples; and 3) developments in inexpensive data collection.
- Utilize expression, methylation, and connectome databases and iPSCs
Benchmarks to Measure Success
The effectiveness of implementing the research priorities and corresponding approaches outlined in this proposal can be measured as follow:
- Investigator metrics: tracking the number of new projects and peer-reviewed publications addressing each research priority
- Data sharing: tracking number of newly shared data sets and citation counts for each
- Generation of new data: tracking the number of new samples genotyped under NIDA’s existing samples (Smokescreen project); the number of new, replicated genetic discoveries; the increase in consistency of research findings among different labs and across animal models and related human phenotypes; and monitoring the quality of data collection (cooperation rates)
Impact on Public Health
The priorities outlined above represent approaches that are expected to bolster primary addiction research findings from the GxExD field over the next five years. An important and concurrent priority of NIDA should be to focus on building translational bridges between basic GxExD researchers with individuals who can transform that knowledge into more effective prevention and treatment programs. Effective implementation of these programs is key to reducing the burden of SUDs and directly improving public health.
NIDA co-chairs: Jonathan Pollock, PhD and Naimah Weinberg MD
External Scientific Matter Experts: E. Jane Costello, PhD; Danielle Dick, PhD; William Iacono, PhD; Eric Johnson, PhD; Kenneth Kendler, MD; John Rice, PhD; and Gustavo Turecki, MD, PhD
NIDA staff: Maureen Boyle, PhD; Harold Gordon, PhD; Raul Mandler, MD; Michele Rankin, PhD; Joni Rutter, PhD; and John Satterlee, PhD