Interpreting Variation in Human Non Coding Genomic Regions Using Computational Approaches and Experimental Assessment (R01) | Apply Online

Opportunity Summary:

The NIH Funding Opportunity Announcement RFA-HG-13-013, titled "Interpreting Variation in Human Non Coding Genomic Regions Using Computational Approaches and Experimental Assessment (R01)," supports research projects that aim to make sense of genetic variation in the parts of the human genome that do not code for proteins. Even though these regions do not produce proteins directly, they contain many regulatory elements and other functional sequences that can influence how genes are turned on or off, when and where they act, and how biological pathways behave. The central purpose of this FOA is to spur the development of genuinely innovative computational methods that can interpret whole-genome sequencing data in a way that helps researchers pinpoint which noncoding variants are most likely to have functional consequences tied to disease risk or other observable traits.

A key theme of the opportunity is integration. Applicants are expected to build approaches that combine whole-genome sequence information with other relevant data types, including (but not limited to) datasets describing genome function, phenotypes, patterns of genetic variation across populations, and other genomic or biological features. The practical goal is to move beyond simply listing large numbers of noncoding variants and instead identify, prioritize, or substantially narrow the set of candidate variants that may be causal or mechanistically important. In other words, the FOA targets tools and frameworks that can help translate noncoding variation into testable hypotheses about function and trait association, improving researchers' ability to connect DNA sequence differences to biological impact.

Another major requirement is evaluation through experimental evidence. The FOA emphasizes that the accuracy of the computational approaches should be assessed using experimental data, meaning applicants should have a plan to validate, benchmark, or otherwise ground-truth predictions against laboratory-derived results. This could include using existing experimental datasets or generating new experimental measurements to test whether computationally prioritized variants actually affect regulatory activity, gene expression, cellular phenotypes, or other functional readouts. The point is not only to create sophisticated prediction models, but also to demonstrate, with experimental support, that the models meaningfully distinguish functional or trait-relevant variants from background variation.

This is an R01 grant mechanism under the NIH, categorized as a discretionary grant and tied to health and education-related research activities. It is linked to multiple CFDA program areas, including human genome research and several disease-focused areas such as cancer and drug abuse/addiction research programs, reflecting the broad relevance of noncoding variant interpretation across biomedical fields. The posted date for the announcement was September 30, 2013, with key dates including an original closing date of January 21, 2015, and a current closing date listed as May 21, 2015, with an archive date of June 21, 2015. The listed award ceiling is $500,000, and there is no cost sharing or matching requirement, which means applicants are not required to contribute non-federal funds as a condition of the award.

Eligibility is broad and includes many organization types: small businesses; for-profit organizations (other than small businesses); nonprofits with or without 501(c)(3) status; public and private institutions of higher education; and a range of government entities (state, county, city/township, special districts, independent school districts, public housing authorities). The FOA also explicitly includes a wide array of additional eligible applicants, such as historically underrepresented and mission-specific institutions (for example HBCUs, Hispanic-serving institutions, AANAPISI institutions, tribal colleges and universities, Alaska Native and Native Hawaiian serving institutions), faith-based or community-based organizations, and eligible federal agencies. Importantly, non-U.S. entities are eligible: foreign organizations and foreign institutions can apply, non-U.S. components of U.S. organizations are eligible, and foreign components (as defined by NIH policy) are allowed. This design signals NIH interest in encouraging broad participation and drawing on global expertise in computational genomics and functional genomics.

Overall, this opportunity is aimed at advancing the state of the art in noncoding variant interpretation by funding research that couples computational innovation with experimental assessment. The intended outcome is a set of methods that can more reliably prioritize noncoding variants from whole-genome data, using integrated functional and phenotypic evidence, so that researchers can more efficiently identify variants likely to influence organismal function, disease susceptibility, or other important traits. For full details, NIH provided an official announcement page (http://grants.nih.gov/grants/guide/rfa-files/RFA-HG-13-013.html) and contact support through the NIH Office of Extramural Research webmaster for access or technical issues.

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