Opportunity Information: Apply for RFA HG 24 004
The National Human Genome Research Institute (NHGRI), part of the National Institutes of Health (NIH), is soliciting applications for the "ML/AI Tools to Advance Genomic Translational Research (MAGen) - Development Sites" program (Funding Opportunity Number RFA-HG-24-004). This opportunity supports the creation of a research consortium focused on testing whether machine learning and artificial intelligence methods can be developed and used in practical translational genomics settings to better predict if, when, and how people who carry known pathogenic genetic variants will actually develop disease. The emphasis is on improving accuracy and precision in real-world prediction, not just demonstrating an algorithm in isolation. Clinical trials are not allowed under this announcement.
The scientific purpose centers on a well-known challenge in genomic medicine: two individuals can carry the same pathogenic variant yet show very different outcomes, ranging from early and severe disease to mild symptoms or no apparent disease at all. MAGen aims to address that variability by building ML/AI tools that integrate and learn from existing multimodal datasets. These datasets can include genomic data (such as sequencing and variant annotations) alongside non-genomic factors that may shape disease expression, for example clinical history, laboratory values, imaging, demographics, environmental exposures, lifestyle factors, and social or behavioral determinants of health, depending on what is available in existing resources. A key expected output is a clearer identification of which genomic and non-genomic modifiers are most associated with disease development among variant carriers, and how those modifiers can be used to make predictions that hold up across different settings.
A major requirement embedded in the program concept is cross-validation in genomic translational research contexts. In practice, that means models should not be built only on a single dataset and reported with internal performance metrics; they should be evaluated in ways that demonstrate generalizability and usefulness for translation, such as testing across multiple cohorts, sites, or data sources when feasible. The program is framed as a consortium effort, so awardees should be prepared to collaborate, share approaches, and align on evaluation strategies that make results comparable across participating groups. The funding mechanism is a cooperative agreement (UG3/UH3), which generally indicates substantial NIH scientific and programmatic involvement and typically supports a staged structure: an initial milestone-driven phase focused on feasibility and development (UG3) that can transition to an implementation/expansion phase (UH3) if milestones are met.
In addition to technical development and validation, MAGen explicitly integrates ethical, legal, and social implications (ELSI) work as a core part of the consortium. The opportunity calls for establishing an ELSI Framework to guide the development and potential integration of ML/AI tools into genomic medicine, along with the implementation of ELSI research projects. This focus reflects common risks and concerns when applying AI in health and genomics, including fairness and bias across ancestry and demographic groups, transparency and explainability of models used in clinical decision-making, accountability for errors, privacy and data governance for sensitive genomic and health information, and how predictions are communicated to patients and families in ways that are understandable and do not cause avoidable harm. Applicants should expect that the program will value responsible AI practices as much as predictive performance.
Eligibility is broad and includes many organization types: state, county, and local governments; special district governments; independent school districts; public and state-controlled institutions of higher education; private institutions of higher education; federally recognized Native American tribal governments; Native American tribal organizations that are not federally recognized tribal governments; public housing authorities/Indian housing authorities; nonprofit organizations with and without 501(c)(3) status (other than institutions of higher education); for-profit organizations (other than small businesses); and small businesses. The announcement also highlights additional eligible applicant categories such as Alaska Native and Native Hawaiian Serving Institutions, Asian American Native American Pacific Islander Serving Institutions (AANAPISI), Hispanic-serving Institutions, Historically Black Colleges and Universities (HBCUs), Tribally Controlled Colleges and Universities (TCCUs), faith-based or community-based organizations, eligible federal agencies, regional organizations, and U.S. territories or possessions. Foreign organizations (non-U.S. entities) are not eligible to apply, and non-domestic components of U.S. organizations are not eligible. However, foreign components, as defined in the NIH Grants Policy Statement, are allowed, meaning a U.S. applicant may include certain foreign activities or collaborations if they meet NIH policy requirements.
From an administrative standpoint, the opportunity is categorized as discretionary funding and uses the NIH cooperative agreement instrument. It falls under health-related NIH activity categories and lists CFDA numbers 93.172 and 93.310. The original application closing date is July 28, 2024, and the listed award ceiling is $1,600,000. The opportunity was created on May 10, 2024. Overall, MAGen is positioned for teams that can combine strong translational genomics expertise, access to high-quality existing multimodal datasets, robust ML/AI development and validation capability, and a serious plan for addressing ELSI issues alongside technical work, all within a collaborative consortium structure.Apply for RFA HG 24 004
- The National Institutes of Health in the health sector is offering a public funding opportunity titled "ML/AI Tools to Advance Genomic Translational Research (MAGen) - Development Sites (UG3/UH3 Clinical Trials Not Allowed)" and is now available to receive applicants.
- Interested and eligible applicants and submit their applications by referencing the CFDA number(s): 93.172, 93.310.
- This funding opportunity was created on 2024-05-10.
- Applicants must submit their applications by 2024-07-28. (Agency may still review applications by suitable applicants for the remaining/unused allocated funding in 2026.)
- Each selected applicant is eligible to receive up to $1,600,000.00 in funding.
- Eligible applicants include: State governments, County governments, City or township governments, Special district governments, Independent school districts, Public and State controlled institutions of higher education, Native American tribal governments (Federally recognized), Public housing authorities/Indian housing authorities, Native American tribal organizations (other than Federally recognized tribal 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, Private institutions of higher education, For-profit organizations other than small businesses, Small businesses, Others.
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Frequently Asked Questions (FAQs)
What is the MAGen - Development Sites funding opportunity?
MAGen - Development Sites is a National Human Genome Research Institute (NHGRI), NIH program titled "ML/AI Tools to Advance Genomic Translational Research (MAGen) - Development Sites" (Funding Opportunity Number RFA-HG-24-004). It solicits applications to develop and test machine learning (ML) and artificial intelligence (AI) tools in practical translational genomics settings.
What is the main goal of this program?
The main goal is to determine whether ML/AI methods can be developed and used in real-world translational genomics to better predict if, when, and how people who carry known pathogenic genetic variants will develop disease. The emphasis is on improving prediction accuracy and precision in real translational contexts, not just demonstrating an algorithm in isolation.
What problem in genomic medicine is MAGen trying to address?
MAGen focuses on the variability in outcomes among people who carry the same pathogenic variant. Two individuals can carry the same variant yet have very different disease outcomes, ranging from early/severe disease to mild symptoms or no apparent disease. MAGen aims to improve prediction of these outcomes by integrating genomic and non-genomic factors.
What kinds of data are expected to be used in MAGen projects?
The program emphasizes using existing multimodal datasets. These may include genomic data (such as sequencing and variant annotations) along with non-genomic factors that may influence disease expression, depending on what is available in existing resources.
What non-genomic factors might be included in the models?
Examples described in the opportunity include clinical history, laboratory values, imaging, demographics, environmental exposures, lifestyle factors, and social or behavioral determinants of health, depending on availability in existing datasets.
What are the expected scientific outputs of MAGen?
A key expected output is clearer identification of which genomic and non-genomic modifiers are most associated with disease development among pathogenic variant carriers, and how those modifiers can be used to make predictions that hold up across different settings.
Is this opportunity focused on algorithm development only?
No. The opportunity specifically emphasizes real-world translational utility. It calls for testing and evaluation that demonstrate generalizability and usefulness for translation, rather than only reporting internal performance metrics from a single dataset.
What does cross-validation mean in the context of this program?
In this program, cross-validation is framed as evaluation in translational genomics contexts that demonstrate generalizability. This can include testing across multiple cohorts, sites, or data sources when feasible, rather than limiting evaluation to one dataset with internal validation.
Does MAGen require collaboration across awardees?
Yes. MAGen is framed as a research consortium. Awardees should be prepared to collaborate, share approaches, and align on evaluation strategies so that results are comparable across participating groups.
What funding mechanism does MAGen use?
The mechanism is a cooperative agreement using the UG3/UH3 structure, indicating substantial NIH scientific and programmatic involvement and a staged, milestone-driven approach.
What does UG3/UH3 imply for project structure?
UG3/UH3 typically supports a staged structure: an initial milestone-driven phase focused on feasibility and development (UG3) that can transition to an implementation or expansion phase (UH3) if milestones are met.
Are clinical trials allowed under this announcement?
No. Clinical trials are not allowed under this funding opportunity.
How are ethical, legal, and social implications (ELSI) addressed in MAGen?
ELSI is an explicit core component of the consortium. The opportunity calls for establishing an ELSI Framework to guide development and potential integration of ML/AI tools into genomic medicine, along with implementation of ELSI research projects.
What kinds of ELSI issues does the opportunity highlight for AI in genomics?
The opportunity highlights common concerns including fairness and bias across ancestry and demographic groups, transparency and explainability for clinical decision-making, accountability for errors, privacy and data governance for sensitive genomic/health data, and communication of predictions to patients and families in ways that are understandable and avoid avoidable harm.
Does the program value responsible AI practices?
Yes. The description indicates the program will value responsible AI practices alongside predictive performance.
Who is eligible to apply?
Eligibility is broad and includes state, county, and local governments; special district governments; independent school districts; public and state-controlled institutions of higher education; private institutions of higher education; federally recognized Native American tribal governments; certain Native American tribal organizations; public housing authorities/Indian housing authorities; nonprofit organizations with and without 501(c)(3) status (other than institutions of higher education); for-profit organizations (other than small businesses); and small businesses.
Are there additional eligible applicant categories specifically mentioned?
Yes. The opportunity highlights additional categories including Alaska Native and Native Hawaiian Serving Institutions, Asian American Native American Pacific Islander Serving Institutions (AANAPISI), Hispanic-serving Institutions, Historically Black Colleges and Universities (HBCUs), Tribally Controlled Colleges and Universities (TCCUs), faith-based or community-based organizations, eligible federal agencies, regional organizations, and U.S. territories or possessions.
Are foreign organizations eligible to apply?
No. Foreign organizations (non-U.S. entities) are not eligible to apply.
Can a U.S. organization include non-domestic components?
No. Non-domestic components of U.S. organizations are not eligible.
Are foreign components allowed at all?
Yes. Foreign components, as defined in the NIH Grants Policy Statement, are allowed. This means a U.S. applicant may include certain foreign activities or collaborations if they meet NIH policy requirements.
Which NIH institute is sponsoring this opportunity?
The opportunity is sponsored by the National Human Genome Research Institute (NHGRI), which is part of the National Institutes of Health (NIH).
What is the Funding Opportunity Number?
The Funding Opportunity Number is RFA-HG-24-004.
Is this funding considered discretionary?
Yes. The opportunity is categorized as discretionary funding.
What are the listed CFDA numbers?
The opportunity lists CFDA numbers 93.172 and 93.310.
What is the application closing date?
The original application closing date is July 28, 2024.
What is the award ceiling for this opportunity?
The listed award ceiling is $1,600,000.
When was this opportunity created?
The opportunity was created on May 10, 2024.
What types of teams are a strong fit for MAGen?
The opportunity is positioned for teams that can combine translational genomics expertise, access to high-quality existing multimodal datasets, robust ML/AI development and validation capability, and a serious plan to address ELSI issues, all within a collaborative consortium structure.
Is the program focused on existing datasets or generating new datasets?
The description emphasizes integrating and learning from existing multimodal datasets and resources, rather than focusing on generating new datasets as the central activity.
What does the program mean by "real-world prediction"?
Based on the description, "real-world prediction" means developing and evaluating ML/AI tools in practical translational genomics contexts, with attention to generalizability across cohorts, sites, or data sources, rather than reporting results that only apply within a single dataset.
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