Opportunity Information: Apply for RFA MH 16 300
Apply for RFA MH 16 300
- The National Institutes of Health in the health sector is offering a public funding opportunity titled "Psychiatric Gene Networks Solving the Molecular Puzzle of Psychiatric Disorders (R01)" and is now available to receive applicants.
- Interested and eligible applicants and submit their applications by referencing the CFDA number(s): 93.242 Mental Health Research Grants.
- This funding opportunity was created on Mar 25, 2015 and posted on Mar 25, 2015.
- Applicants must submit their applications by Jun 29, 2015. (Agency may still review applications by suitable applicants for the remaining/unused allocated funding in 2026.)
- The funding agency has allocated a total of $2,000,000.00 to eligible and selected applicants.
- Eligible applicants include: Independent school districts Public and State controlled institutions of higher education Small businesses Special district governments For profit organizations other than small businesses State governments City or township governments Nonprofits having a 501(c)(3) status with the IRS, other than institutions of higher education Nonprofits that do not have a 501(c)(3) status with the IRS, other than institutions of higher education Others (see text field entitled Additional Information on Eligibility for clarification) Native American tribal organizations (other than Federally recognized tribal governments) Private institutions of higher education Public housing authorities/Indian housing authorities County governments Native American tribal governments (Federally recognized).
- Other Eligible Applicants include the following Alaska Native and Native Hawaiian Serving Institutions Asian American Native American Pacific Islander Serving Institutions (AANAPISISs) Eligible Agencies of the Federal Government Faith based or Community based Organizations Hispanic serving Institutions Historically Black Colleges and Universities (HBCUs) Indian/Native American Tribal Governments (Other than Federally Recognized) Non domestic (non U.S.) Entities (Foreign Organizations) Regional Organizations Tribally Controlled Colleges and Universities (TCCUs) U.S. Territory or Possession.
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Opportunity Summary:
Psychiatric Gene Networks Solving the Molecular Puzzle of Psychiatric Disorders (R01) is a National Institutes of Health (NIH) funding opportunity that supported research aimed at clarifying how groups of genes work together in networks and biological pathways to influence risk for severe mental illnesses. Rather than focusing on single genes in isolation, the program emphasized a systems-level view of psychiatric disease biology, encouraging investigators to map and analyze complex molecular interactions that can help explain why psychiatric disorders arise and how genetic susceptibility translates into changes in brain-relevant function.
The central goal of the announcement was to solicit projects that combine computational and functional approaches to identify gene networks and pathways associated with psychiatric disorder risk, and then begin to evaluate whether those predicted networks truly matter biologically. Applicants were expected to leverage existing, diverse, multi-scale datasets, meaning data that can span multiple biological levels (for example, genomic variation, gene expression, epigenetic marks, proteomic patterns, cellular phenotypes, brain circuitry measures, or clinical features). The intent was to push beyond descriptive associations and toward models that propose testable mechanisms, using modern bioinformatics, computational predictive modeling, and systems biology methods to prioritize candidate genes, infer network structure, and highlight convergent pathways that might be shared across disorders or distinguish specific clinical syndromes.
A key expectation was that computational predictions would not stand alone. The FOA called for replication of model-derived findings in independent datasets, which is an explicit push for robustness and generalizability rather than results driven by a single cohort or platform. In addition, the program required that predictions be confirmed using biological measures from existing and/or newly generated experimental data. In practical terms, this meant applicants needed a plan to connect computationally inferred networks to biological evidence, such as validating predicted pathway activity with molecular assays, testing network perturbations in cell or animal models, or using other experimental strategies that can support (or refute) a proposed causal relationship between molecular network behavior and disease risk.
From an administrative standpoint, the opportunity used the NIH R01 grant mechanism (a standard research project grant) under Funding Opportunity Number RFA-MH-16-300, categorized as discretionary funding and aligned with health research activities under CFDA 93.242 (Mental Health Research Grants). The announcement indicated an estimated total funding level of about $2,000,000 and specified that no cost sharing or matching was required. Key dates included a posted and creation date of March 25, 2015, an original and current closing date of June 29, 2015, and an archive date of July 30, 2015.
Eligibility was broad and included many types of U.S.-based organizations such as public and private institutions of higher education, nonprofit organizations with or without 501(c)(3) status, small businesses and other for-profit organizations, and various levels of government (state, county, city/township, special district). The eligibility language also explicitly included several categories of mission-driven and minority-serving institutions and organizations, such as HBCUs, Hispanic-serving institutions, tribally controlled colleges and universities, Alaska Native and Native Hawaiian serving institutions, and AANAPISI institutions, along with faith-based and community-based organizations where appropriate. In addition, non-U.S. entities (foreign organizations) and regional organizations were listed as eligible, as were U.S. territories or possessions and eligible federal agencies, reflecting NIH's capacity to support research teams with specialized expertise or unique datasets regardless of geography, when consistent with program rules.
Overall, the opportunity can be summarized as a call for rigorous, data-driven psychiatric genetics and systems biology research that integrates large and diverse datasets, builds and tests predictive models of gene network function, and follows through with replication and biological confirmation. The emphasis throughout is on moving from genetic signal to mechanistic understanding by identifying molecular networks and pathways that plausibly contribute to psychiatric disorder risk and by taking concrete steps toward verifying whether those networks are functionally involved in disease-related biology.
Frequently Asked Questions (FAQs)
What is the "Psychiatric Gene Networks: Solving the Molecular Puzzle of Psychiatric Disorders (R01)" opportunity?
It is a National Institutes of Health (NIH) funding opportunity that supported research focused on understanding how groups of genes work together in networks and biological pathways to influence risk for severe psychiatric disorders. The emphasis was on a systems-level view of disease biology rather than studying single genes in isolation.
What was the main scientific goal of this funding opportunity?
The central goal was to solicit projects that combine computational and functional approaches to identify gene networks and pathways associated with psychiatric disorder risk, and then begin evaluating whether those predicted networks have real biological relevance.
What makes this program different from single-gene psychiatric genetics studies?
Instead of focusing on one gene at a time, the program emphasized mapping and analyzing complex molecular interactions. The idea was to clarify how genetic susceptibility translates into changes in brain-relevant function through gene networks and convergent biological pathways.
What types of approaches were encouraged?
The announcement encouraged integrating computational methods (such as bioinformatics, predictive modeling, and systems biology) with functional or biological follow-up work. Applicants were expected to build models that propose testable mechanisms, not just report associations.
What kinds of datasets were applicants expected to use?
Applicants were expected to leverage existing, diverse, multi-scale datasets spanning multiple biological levels. Examples included genomic variation, gene expression, epigenetic marks, proteomic patterns, cellular phenotypes, brain circuitry measures, and/or clinical features.
Were applicants expected to go beyond descriptive findings?
Yes. The intent was to push beyond descriptive associations toward models that propose testable mechanisms. Projects were expected to prioritize candidate genes, infer network structure, and identify convergent pathways that might be shared across disorders or distinguish specific syndromes.
Was replication required?
Yes. The FOA called for replication of model-derived findings in independent datasets to emphasize robustness and generalizability, rather than results driven by a single cohort, dataset, or platform.
Did the FOA require biological validation of computational predictions?
Yes. Computational predictions were not expected to stand alone. Applicants needed plans to confirm predictions using biological measures from existing and/or newly generated experimental data.
What does "biological confirmation" mean in the context of this opportunity?
It refers to connecting computationally inferred networks to biological evidence, such as validating predicted pathway activity with molecular assays, testing network perturbations in cell or animal models, or using other experimental strategies that can support or refute a proposed causal relationship between network behavior and disease risk.
What grant mechanism was used?
The opportunity used the NIH R01 mechanism, which is a standard research project grant.
What is the Funding Opportunity Number (FOA number)?
RFA-MH-16-300.
What was the estimated total funding level?
The announcement indicated an estimated total funding level of about $2,000,000.
Was cost sharing or matching required?
No. The opportunity specified that no cost sharing or matching was required.
What CFDA program was this associated with?
CFDA 93.242 (Mental Health Research Grants).
When was this opportunity posted?
The posted and creation date was March 25, 2015.
What was the application closing date?
The original and current closing date was June 29, 2015.
When was the opportunity archived?
The archive date was July 30, 2015.
Who was eligible to apply?
Eligibility was broad and included many U.S.-based organizations such as public and private institutions of higher education, nonprofit organizations (with or without 501(c)(3) status), small businesses and other for-profit organizations, and various levels of government (state, county, city/township, special district).
Were minority-serving and mission-driven institutions included in eligibility?
Yes. The eligibility language explicitly included categories such as HBCUs, Hispanic-serving institutions, tribally controlled colleges and universities, Alaska Native and Native Hawaiian serving institutions, and AANAPISI institutions. It also included faith-based and community-based organizations where appropriate.
Could non-U.S. organizations apply?
Yes. Non-U.S. entities (foreign organizations) and regional organizations were listed as eligible.
Were U.S. territories or federal agencies eligible?
Yes. U.S. territories or possessions and eligible federal agencies were listed as eligible.
What kinds of research outcomes was NIH trying to enable through this FOA?
The overall goal was to move from genetic signal to mechanistic understanding by identifying plausible molecular networks and pathways contributing to psychiatric disorder risk and taking concrete steps toward verifying whether those networks are functionally involved in disease-related biology.
Was the opportunity focused on one psychiatric disorder or multiple disorders?
The emphasis included identifying pathways that might be shared across disorders or that might distinguish specific clinical syndromes, implying that cross-disorder and disorder-specific network insights were both relevant.
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