Developing novel theory and methods for understanding the genetic architecture of complex human traits (R01 Clinical Trial Not Allowed)
Funding
Not specified
Deadline
--
Days
--
Hrs
--
Min
--
Sec
Nov 05, 2026
Posted Nov 15, 2024 (462 days ago)
Closes Nov 5, 2026 (in 257 days)
Grant Details
Opportunity Number
PAR-25-255
CFDA / ALN
93.172, 93.242, 93.399
Opportunity Category
Discretionary (D)
Funding Category
ED, HL
Funding Instrument
Grant (G)
Cost Sharing
No Cost Sharing (No)
Eligibility
State governments (00)
County governments (01)
City or township governments (02)
Special district governments (04)
Independent school districts (05)
Public and State controlled institutions of higher education (06)
Native American tribal governments (Federally recognized) (07)
Public housing authorities / Indian housing authorities (08)
Native American tribal organizations (11)
Nonprofits having a 501(c)(3) status with the IRS (12)
Nonprofits without 501(c)(3) status (13)
Private institutions of higher education (20)
For-profit organizations other than small businesses (22)
Small businesses (23)
Others (25)
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.
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.
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