Developing novel theory and methods for understanding the genetic architecture of complex human traits (R01 Clinical Trial Not Allowed)
National Institutes of Health (NIH) · NIH Institute/Center · PAR-25-255
Source: Grants.gov · View original posting ↗
- AwardWhat a single award can be worth — the funder's published per-award amount or floor–ceiling range.
- Amount not listed
- DeadlineFinal application due date.
- Nov 5, 2026
- Letter of intentDue date for the letter of intent (a short pre-application some funders require or request before the full proposal).
- —
- MechanismNIH activity code — the grant type (R01 research project, R21 exploratory, K series career development, F series fellowship, …).
- R01
- DurationMaximum project period for a single award.
- —
- Expected awardsHow many awards the funder anticipates making under this opportunity.
- —
- Funding cycleHow often the program accepts applications (annual, multiple cycles per year, rolling, or one-time).
- Standard NIH dates
- Open dateWhen applications open (or opened).
- Nov 15, 2024
- Total fundingThe overall pool the funder expects to commit across ALL awards under this opportunity — not what one project receives.
- —
- Clinical trialWhether proposed projects must, may, or must not include a clinical trial.
- Not allowed
Funding context
Based on FY2024 NIH (all institutes) R01 applications (5,385 of 33,139 applications awarded).
Institute-specific data wasn’t available — showing the NIH-wide rate for this mechanism.
Payline: NIH discontinued percentile paylines for 2026 under its Unified Funding Strategy ↗ — scores are now weighed in context rather than against a fixed cutoff.
NIH-wide R01, all institutes.
Aggregate historical data by institute, mechanism, and fiscal year — context for planning, not a prediction for this opportunity. Source ↗
Description
The goal of this NOFO is to support applications for novel theory and methods development that enable better understanding of how genetic and non-genetic factors contribute to complex trait variation across individuals, families, and populations. Approaches should be interdisciplinary drawing from the natural and social sciences, account for interdependencies across scales of biological, social, and ecological organization, and make extensive use of theory, modeling, and validation with available large-scale datasets.
Data notes: award amount not published in the source feed; administering NIH institute could not be identified from the opportunity number.