Developing novel theory and methods for understanding the genetic architecture of complex human traits (R21 Clinical Trial Not Allowed)
This NIH R21 funds development of novel theory and methods for understanding the genetic architecture of complex human traits, using interdisciplinary approaches, simulations, and existing large-sc…
- Deadline
- Jan 7, 2027
- Posted
- Nov 15, 2024
- Award amount
- Amount not specified
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In plain English
This NIH R21 funds development of novel theory and methods for understanding the genetic architecture of complex human traits, using interdisciplinary approaches, simulations, and existing large-scale datasets; no clinical trial is allowed. Eligible applicants include universities, research institutions, tribal and community organizations, and foreign organizations. It is aimed at research teams with methodological expertise. No specific award amount is stated.
AI-generated summary to help you decide quickly — verify the official eligibility rules before applying.
Who can apply
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.
About this grant
The goal of this NOFO is to support R21 applications for novel theory and methods development that better delineate how genetic and non-genetic factors contribute to complex trait variation across individuals, families, and populations. Approaches should be interdisciplinary across the natural and social sciences, account for interdependencies across scales of biological, social, and ecological organization, and make extensive use of theory, simulations, and validation using available large-scale datasets
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