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Priorities for Strategy 1.2

Updated: September, 2017

Identify the genomic and non-genomic factors associated with mental illnesses

NIMH invites research focused on the discovery and functional characterization of gene variants and other genomic elements that contribute to the risk of mental illnesses and related traits. Investigators are encouraged to apply unbiased, genome-scale approaches to adequately powered case-control, family, and population based designs in diverse populations to identify genetic risk factors.  Non-genomic factors that have a direct impact on the development or function of the brain and affect mental illness risk should be identified by unbiased, phenome-scale approaches in robust epidemiological designs. Investigators are also encouraged to expand the breadth and depth of phenotyping and include non-diagnostic, but clinically relevant, phenotypes such as cognitive, affective, and social processes (dimensional phenotypes).

Investigators are encouraged to follow up on human genetic findings and leverage insights from NIMH’s large, existing efforts in genome-wide studies across genomic scales, including next-generation sequencing and epigenomics Investigators are urged not to make ‘models’ but to elucidate basic processes affected by disease risk factors, genetic or otherwise. The specific biological or mechanistic question should dictate the appropriate experimental system.  Investigators are encouraged to develop and use single-cell and cell-type specific data generation and analyses.

Research Priorities

  1. Define genomic variations associated with mental illnesses and determine the biological consequences of these variations.

    Priority areas include:

      1. Identifying pathogenic genetic variation across the allelic frequency spectrum in large, well-powered datasets and across illnesses.
      2. Continuing to expand the collection of DNA samples and their use in gene discovery efforts from:
        1. Patients, first-degree relatives, and controls from non-Caucasian and ancestral populations, as well as populations with rare genetic syndromes. 
        2. Patients and first-degree relatives for mental illnesses that are under-represented in the current portfolio (autism spectrum disorder, attention deficit/hyperactivity disorder, eating disorders, suicide, post-traumatic stress, major depressive disorder, obsessive-compulsive disorder, and Tourette syndrome).
    1. Determining the functional role of variants with statistically significant associations from appropriately powered genome-wide studies for mental illnesses through computational approaches and experimental validation using novel experimental systems and assays.
    2. Identifying and examining underlying genomic networks/pathways that may identify primary targets for mechanistic studies and novel treatments. 
    3. Exploring the somatic genetic variation in human brain cells to identify potential associations with mental illnesses.
    4. Using comparative genomics and evolutionary biology to identify genomic elements in humans and other species that are critical for shared and unique neurobiological functions relevant to mental health.
    5. Developing and validating cost-effective, high-throughput assays to assess the function of novel risk variants. Generating multi-omic brain data, including single-cell and cell-type specific data, in order to functionally annotate putative causal variants at different molecular scales.
    6. Develop novel convergent approaches to connect genes to molecules to networks.
    7. Statistically rigorous, well-powered, unbiased, genome-wide approaches to select genomic elements for follow-up studies will be prioritized over approaches where “candidate genes” are selected or evaluated based solely on biological plausibility.

  2. Define the molecular mechanisms that determine how experience has enduring effects on gene expression, brain function, and behavior.

    Priority areas include:

    1. Identifying genetic and genomic factors that alter gene expression and/or molecular networks, important for cognitive, affective, and social processing.
    2. Developing, validating, and using appropriate experimental systems to examine the basic neurobiological processes affected by non-genetic risk factors contributing to risk for mental illnesses.
    3. Using genome-wide analyses followed by in-depth functional studies to investigate the joint effects of DNA sequence variants and environment on consequent genomic regulatory changes and cellular outcomes that underlie relevant phenotypes.
    4. Deepening the characterization of genotype-phenotype relationships by using dimensional phenotypes.

  3. Delineate environmental and biological factors altering genomic risk for mental illnesses.

    Priority areas include:

    1. Determining basic mechanisms whereby gene transcriptional and translational regulation contribute to normal brain processes relevant to mental illnesses.
    2. Understanding the interplay between genetic and non-genetic risk and how they mediate dysfunction and/or psychopathology.
    3. Delineating genetic and environmental influences on susceptibility to mental illnesses through population-based epidemiological studies.
    4. Utilizing novel approaches for stratifying clinical populations by genetic or phenotypic factors (e.g., deep phenotyping of individuals with a genetically defined risk) to investigate risk or resilience for mental illnesses.

  4. Develop analytical tools for multi-scale data integration.

    Priority areas include:

    1. Developing novel computational methods and tools for data integration, data mining, and gene discovery through analyzing large datasets across molecular hierarchies (e.g., DNA sequence, RNA expression, transcription, translation, and post-translational regulation) and across mental illnesses and dimensional phenotypes.
    2. Developing robust statistical methods for large-scale, well-powered genetic association studies of diverse phenotypes such as brain structure, function, and connectivity.
    3. Developing innovative computational methods to elucidate the functional role of genetic variants associated with mental illnesses in appropriately powered studies.
    4. Developing novel computational approaches to systematically evaluate known genetic variants and associated (or convergent) pathways (or mechanisms) linked to disease susceptibility in order to identify targets for novel treatments.
    5. Developing computational approaches to better model multiple simultaneous systems-level patterns across multiple brain areas, impacting mood, cognition and social processes.    
    6. Developing innovative computational methods to integrate multi-scale data across cell-types to identify gene and molecular networks and connect these to brain structure, function, and connectivity.
    7. Developing novel computational methods to elucidate the relationships between genes and regulatory functional elements and their effects on molecular networks.
    8. Developing novel computational methods to elucidate the role of functional elements in spatio-temporal regulation of the genes/gene networks that drive critical neurobiological processes involved in the pathophysiology of psychiatric disorders.
    9. Developing and applying innovative computational methodologies, such as machine learning and artificial intelligence, to elucidate the functional role of genetic variants associated with mental illnesses in appropriately powered studies.