Developing novel theory and methods for understanding the genetic architecture of complex human traits (R01 Clinical Trial Not Allowed)
This NIH R01 grant supports development of novel theory and methods to understand how genetic and non-genetic factors shape complex human traits, using interdisciplinary modeling and large-scale da…
- Deadline
- Nov 5, 2026
- Posted
- Nov 15, 2024
- Award amount
- Amount not specified
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In plain English
This NIH R01 grant supports development of novel theory and methods to understand how genetic and non-genetic factors shape complex human traits, using interdisciplinary modeling and large-scale datasets. It is a research grant aimed primarily at universities, research institutions, and similar eligible organizations, including various minority-serving institutions, tribal entities, and foreign organizations. No award amounts are specified. Clinical trials are not allowed under this opportunity, and the eligibility list is research-institution oriented rather than open to nonprofits, small businesses, or individuals broadly.
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 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.
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