ARRA: Grand Opportunities

National Human Genome Research Institute

National Institutes of Health
U.S. Department of Health and Human Services


Recovery Act Limited Competition for NIH Grants: Research and Research Infrastructure "Grand Opportunities" (RC2)

Under the American Recovery and Reinvestment Act (ARRA), the NIH has established a new program entitled Research and Research Infrastructure "Grand Opportunities," hereafter called the "GO" grants program (RFA-OD-09-004). The purpose of the GO grants program is to support high-impact ideas that lend themselves to short-term, non-renewable funding, and may lay the foundation for new fields of investigation. Under the GO program, the NHGRI will support short-term, large-scale research projects that will accelerate critical breakthroughs, early and applied research on cutting-edge technologies, and new approaches to improve the synergy and interactions among multi and interdisciplinary research teams. The research supported by the "GO" grants program should have high short-term impact, and a high likelihood of enabling growth and investment in biomedical research and development, public health, and health care delivery.

This initiative is one of several being offered by NHGRI to help fulfill the goals of the American Recovery and Reinvestment Act (ARRA) to stimulate the economy through support of biomedical and behavioral research. Additional information about the Recovery Act and related NIH opportunities is available through the Office of Extramural Research at Grant Funding Opportunities Supported by the American Recovery & Reinvestment Act of 2009 (ARRA) [grants.nih.gov].

Areas of Scientific Priority

NHGRI will accept grant applications under the GO program that address any aspect of the Institute's mission. However, the following scientific areas are considered of particularly high interest by NHGRI to achieve the ARRA goals of creating or retaining jobs and accelerating the progress of genomic science. Requests that address the following areas will be given priority for funding (note that these are not listed in priority order; see below for additional information about each topic):

Applicants interested in submitting a proposal for a GO grant requesting $500,000 or more in direct costs in any year are required to submit a letter of intent and obtain the approval of the appropriate Program Director (identified below). Although not required, this is strongly encouraged for smaller applications as well. Applicants are encouraged to contact the Program Director early in their planning process.

The NHGRI intends to commit $20 million in each of FY 2009 and 2010 to fund up to 25 awards, contingent upon the submission of a sufficient number of scientifically meritorious applications and the availability of funds.

Enhancing the ENCODE and modENCODE Projects

The aims of the ENCODE (Encyclopedia of DNA Elements) Project are to apply high-throughput, cost-efficient approaches to generate a catalog of functional elements in the human genome. A corresponding project, the model organism ENCODE (modENCODE) Project, has the same goal in the widely used model organisms, the fruit fly (D. melanogaster) and roundworm (C. elegans). The ENCODE and modENCODE efforts are organized as open consortia and each includes investigators with diverse backgrounds and expertise in the production and analysis of data (The ENCODE Project).

The value of these two research consortia could be greatly enhanced by efforts to increase the breadth, depth, quality, and utility of the data. Under the GO Program, NHGRI is particularly interested in proposals for projects that address the following issues:

a. Expansion of production efforts to create a more comprehensive and higher quality catalog of functional elements, by increasing the number of methods being used to identify sequence-based functional elements across the entire human, worm, or fly genomes. Proposed methods must identify new sets of functional elements that are not available from the experimental approaches currently being employed in these projects. Proposed methods must be able to be applied in an efficient, cost-effective, and high-throughput manner to the entire genome immediately so that significant progress can be made in the two years of funding.

b. Enhancement of the value of the human ENCODE Project by conducting a parallel effort on the mouse genome. Applicants are strongly encouraged to propose the generation of data from a limited set of mouse tissues, stem cells, and developmental stages that will allow a direct comparison to the existing human data to allow comparative analysis and to aid in the evaluation of the quality of the human ENCODE data. Proposed methods to identify functional elements in the mouse genome must be able to be applied in an efficient, cost-effective, and high-throughput manner to the entire mouse genome immediately so that significant progress can be made in the two years of funding, Proposals to study functional elements in mouse are limited to $750,000 total costs per year.

c. Expansion of efforts to experimentally validate the biological function of the elements that have been defined biochemically in ENCODE or modENCODE using high-throughput approaches. Proposals for biological validation are limited to functional elements currently being defined by the ENCODE/modENCODE Projects; applicants must describe how they will interact with the appropriate Consortium to identify elements for validation and to report back on results.

d. Adaptation and testing of new high-throughput methods for tagging proteins of interest for the ENCODE/modENCODE Projects, with the objective of allowing the replacement of protein-specific antibodies in order to accelerate the speed and significantly reduce the costs of individual projects. The proposed tagging approach must be able to generate data of the same quality as those that can be obtained using protein-specific antibodies, and should allow the detection of a wide range of types of proteins including different families of DNA-binding, RNA-binding, and chromosomal proteins.

e. Development and implementation of a modENCODE Data Analysis Center (DAC) to support, facilitate, and enhance integrative analyses of the data from the modENCODE Project. The DAC is intended to work with the members of the modENCODE Consortium to coordinate the activities of the individual informatics groups of the Consortium members and the modENCODE Data Coordination Center (DCC); to work with the modENCODE Analysis Working Group to identify integrative analyses that should be carried out with the modENCODE data; to perform all necessary data transformations and analyses with modENCODE data; and to provide the final modENCODE integrative analysis data sets to the broad scientific community. These activities will enhance the modENCODE Consortium's ability to provide a valuable final product for the larger biomedical research community's use in studying functional sequence elements in the C. elegans and D. melanogaster genomes. Proposals for the modENCODE DAC are limited to $1,250,000 total costs per year.

The P.I.s of funded awards will be expected to apply for membership in either the ENCODE or modENCODE Consortium, as appropriate. These projects have been designated as "community resource projects", as defined by a meeting on data release held in Fort Lauderdale, Florida, in January 2003 (Sharing Data from Large-scale Biological Research Projects: A System of Tripartite ResponsibilityPDF file). Each P.I. will be expected to make any data and resultant analyses freely available to the scientific community in a timely manner and in accordance with the ENCODE/modENCODE data release policy (See: ENCODE-modENCODE Consortia Data Release Policy). Timeliness is particularly important because NHGRI anticipates that there will be considerable interest from the research community in accessing these data.

Letters of intent should be sent to:

Peter Good, Ph.D.
NHGRI
5635 Fishers Lane, Suite 4076
Bethesda, MD 20892-9305
goodp@mail.nih.gov

Or

Elise Feingold, Ph.D.
NHGRI
5635 Fishers Lane, Suite 4076
Bethesda, MD 20892-9305
feingole@mail.nih.gov

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Development and Application of Statistical and Computational Data Analysis Methods for DNA Sequence, Variation, GWAS, Genomic Function, Chemical Biology and Related Genomic Data Sets

New technologies are producing extremely large DNA sequence, genetic variation, and genomic function datasets. By combining these with additional genomic, phenotypic, and environmental data, investigators will be able to address broader and more complex biomedical questions than they currently can. However, to take advantage of these opportunities, new statistical and computational methods are needed to process, analyze, integrate and interpret vast amounts of data of different types.

NHGRI is highly interested in proposals to develop new analytical methods and software for processing and using DNA sequence and genotype data, and for using these data in combination with datasets of epigenomic, expression, proteomic, other genomic, phenotypic, and environmental information. In addition, proposals may aim to apply the methods being developed to analyze large datasets of relevance to the NHGRI's mission. Applicants are strongly encouraged to make the methods and software freely available to the entire scientific community.

The methods may be developed for use with data from humans, model organisms, or other organisms, as appropriate for NHGRI programmatic interests. Applicants are encouraged to develop and evaluate the methods using broadly available datasets relevant to NHGRI interests from public databases, such as those in the Short Read Archive, dbSNP, dbGaP, GEO and PubChem.

Appropriate topics include, but are not limited, to the following:

a. Methods for processing raw sequence data: Methods need to be improved to calibrate error rates and assess the quality of sequence reads, their assembly into complete de novo genomes or in complex regions, the alignment of resequencing data to reference sequences, the calling of SNPs and structural variants, the identification of somatic mutations, and the quantification and characterization of RNA transcripts. Specific methods are needed for complex or repetitive regions of the genome and for highly rearranged genomes such as in tumors.

b. Methods for processing genotype data: Methods need to be improved to call genotypes for SNPs and structural variants, to correct for subtle biases in typing, to combine data across samples, to impute untyped variants based on other genotype data, and to integrate genotype, array Comparative Genomic Hybridization (aCGH) and sequence variation data.

c. Methods for comparative genomic analysis: Methods need to be improved to compare the genomes of multiple species, and to use comparative genomic data to find functional genomic elements and to study genome organization.

d. Methods for sequence analysis of metagenomic samples: Methods need to be improved to assemble the genomic or partial genomic sequences in mixed samples from individual people to compare the composition of microbial communities across time, body sites, individuals, and disease states, and to make inferences about community composition, processes, and function. Applicants may propose to start to develop the methods using data obtained from non-human samples if the appropriate data obtained from human samples are not available initially, but should transfer the development to data obtained human samples during the two-year award period.

e. Methods for population genetic analysis: Methods need to be developed to elucidate population processes using the large amount of individual variation data across the genome and in genomic regions. These may include estimating mutation and recombination rates, detecting natural selection, and inferring population history, structure and organization.

f. Methods for analysis of genome-wide association (GWA) studies: Methods need to be improved to relate genetic variation and environmental exposures to phenotypes in studies of diseases and other traits. These include analyses of haplotypes and multiple markers, quantitative as well as qualitative traits, multiple covariates, longitudinal phenotypes and age interactions, family linkage data, heritability estimation, multiple population groups and admixed groups, and meta-analysis, as well as prediction models based on single genes and gene-gene and gene-environment interactions. Because GWA studies identify chromosome regions associated with a disease or trait but not the specific variants that causally contribute to the disease risk or phenotype, methods need to be improved to design and analyze follow-up sequencing and genotyping studies to further localize the sets of variants that will require functional studies to identify the causal variants, such as methods to detect an excess of rare variants.

g. Methods for integration with functional genomic datasets: Methods need to be developed to integrate sequence and variation data with expression, epigenetic, proteomic, and related genomic datasets, as well as with phenotype and environmental data; to use these data to map the variants that affect these processes; and to provide insight into which genes and variants affect basic biological processes or causally contribute to disease risk or other traits and how they do so.

h. Methods for dealing with statistical issues: Methods need to be improved to design studies and assess the statistical significance of findings. Sampling issues, artifacts of data production, and genomic and population properties such as linkage disequilibrium, need to be taken into account.

i. Methods for analysis of small molecule chemical biology data: Methods need to be improved to analyze small molecule chemical biology data, including integrating multiple assay results for high and low-throughput screening data, especially for difficult assays (multiplexed, phenotypic, or protein-protein interactions). Controlled vocabulary development is needed, and could focus on a few assay types (e.g., high-content screening, ion channel, or reporter gene assays). Applicants should provide details on building required partnerships with the chemical biology community. Approaches could include incorporating results from public domain high or low throughput assays and leveraging pathway, structure, and activity data from publications and patents. Tools developed should be chemist- and biologist-friendly to enable building structure-activity relationships and should enable prioritization of medicinal chemistry plans.

j. Software development, maintenance, and evaluation: Robust software that is easy to use and widely available is needed to allow the wider biomedical research community to take maximum advantage of the data that are now rapidly being deposited into public databases by community resource projects and other data-generation efforts. Applicants are encouraged to aim to write robust, modular software for the methods proposed for development or for existing methods for which such robust software is not available. The proposal should include plans to develop good documentation, with example datasets. Since this FOA is for only two years, software must be released in a manner that allows access by the community even after the project period of the awards. Also see "Software Dissemination Plans" below.

In all cases, funding will be provided for methods and software development and their application (if included in the proposal), but not for additional sequencing or genotyping, other experimental data collection or validation, or sample collection. Analysis of datasets without methods development will not be supported. Proposals are limited to $500,000 direct costs per year for two years.

Letters of intent should be sent to:

Lisa D. Brooks, Ph.D.
NHGRI
5635 Fishers Lane, Suite 4076
Bethesda, MD 20892-9305
lisa.brooks@nih.gov

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Development of a Software Pipeline for Sequence Data for Quality Checking, Alignment and Variant-Calling.

New platforms for sequencing have vastly increased sequence data production. In the next two years, sequencing centers are expected to deposit at least 200 Terabases of human sequence data into public repositories such as the Short Read Archive (SRA) at the NIH National Center for Biotechnology Information (NCBI). This large amount of data must be processed through a software analysis pipeline to extract the information needed by the broad research community to address many important biomedical problems. Currently, such data processing steps for various large-scale resequencing projects are being done piecemeal. A standard modular pipeline needs to be developed and passed to NCBI for implementation, to assess data quality and call variants. Such a centrally implemented pipeline would allow data from all the sequencing centers and from all sequencing platforms to be dealt with in a uniform manner.

NHGRI is very interested in proposals to develop such a standard software pipeline and to work with NCBI to implement the pipeline. Components of the pipeline should include taking in data from the Short-Read Archive, identifying any problems with the data that need to be resolved with submitters, aligning the data with the human reference sequence, re-calibrating error rates, estimating error rates, and calling SNPs and structural variants in each sample (for tumor genomes, somatic variants should be identified as well as germline variants). Applicants should address all sequencing platforms that are currently in production use and generating public data. The proposed methods and formats should be standard ones in the field, as defined by the 1000 Genomes Project, TSP, and similar projects. The software produced should be broadly available; see the section on Software Dissemination Plans, below.

Proposals are limited to $1 million total costs per year for two years.

Letters of intent should be sent to:

Lisa D. Brooks, Ph.D.
NHGRI
5635 Fishers Lane, Suite 4076
Bethesda, MD 20892-9305
lisa.brooks@nih.gov

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Development of a Data Analysis and Coordination Center for Cancer Genomes

The NHGRI funds large-scale genome sequencing capacity at three Genome Sequencing Centers (GSCs) located in the U.S. (See: The NHGRI Genome Sequencing Program (GSP)). This program undertakes projects to provide critical genomic information that can be of significant value to the scientific community in areas of broad scientific interest. The Cancer Genome Atlas is a project within this program that catalog genetic alterations in tumor genomes. Recently, new sequencing platforms for high throughput sequencing (next-gen sequencers) have dramatically increased sequence data production at a corresponding reduction in cost, such that it is now possible to consider sequencing many tumor genomes for these projects. In the next two years, a combination of whole genome, gene-targeted, and transcriptome sequencing will generate a total output of >25 Terabases of sequence for these cancer genome projects that will be deposited into public repositories such as the Short Read Archive (SRA) at the NIH National Center for Biotechnology Information (NCBI).

NHGRI is interested in proposals to develop a cancer genome Data Analysis and Coordination Center (DACC). The DACC will be expected to:

a. collaborate with the funded Genome Sequencing Centers (GSCs) to establish a working environment for analysis of cancer genome sequence data;

b. interact with the funded GSCs along with other members of the TSP and TCGA projects;

c. track raw data submitted to public data repositories and store processed cancer data as necessary;

d. help define quality assurance methods and ensure that data from the GSCs meets those standards;

e. establish informatics pipelines for the analysis of next-gen sequence data to identify transcriptome alterations and potential tumor-specific splicing variants in tumors;

f. evaluate new analysis methods that become available from the GSCs or other researchers to improve on components of the informatics pipeline;

g. organize data analysis workshops that will bring together the GSCs and experts in data analysis for face-to-face interactions needed to promote better data analysis;

h. plan and implement regular conference calls with the GSCs and other members of the TSP and TCGA projects to coordinate analyses of the cancer genome data;

i. interact with the TCGA Data Coordination Center (DCC) and other informatics resources to disseminate results;

j. in collaboration with GSCs, perform analyses to compare different methods for the analysis of cancer genome data;

Applicants should describe a broad plan for how the DACC will accomplish the goal of facilitating the NIH's cancer genome analysis programs, including how the specific activities listed above will be accomplished. The DACC should use robust, modular, and flexible data management and retrieval tools capable of handling all of the cancer genome data that will be stored. Where possible, the DACC should use existing software. The utilization and, where appropriate, extension or creation of new open source software is encouraged. The DACC is expected to be able to work with the GSCs to establish the exact data types and formats that will be transferred to the DACC along with developing procedures to check and track the quality of incoming data.

The NHGRI Large-Scale Sequencing program has been designated a "community resource project,' as defined by a meeting on data release held in Fort Lauderdale, Florida, in January 2003 ( Sharing Data from Large-scale Biological Research Projects: A System of Tripartite ResponsibilityPDF file). The DACC will be expected to make any data and resultant analyses freely available to the scientific community in a timely manner. Timeliness is particularly important because NHGRI anticipates that there will be considerable interest from the research community in accessing these data.

Proposals are limited to $1 million total costs per year for two years.

Letters of intent should be sent to:

Peter Good, Ph.D.
NHGRI
5635 Fishers Lane, Suite 4076
Bethesda, MD 20892-9305
301-496-7531
peter.good@nih.gov

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Development or Improvement of Ultra-low-cost DNA Sequencing Technologies

In 2008, the accelerating rate and dramatic decrease in cost of DNA sequencing produced stunning insights into cancer genes and pathways, and hominid and mammalian variation and origins. This was enabled largely by technologies that supersede those used for the initial human genome project. The potential to achieve advances such as these were envisioned when, in 2004, NHGRI launched an ambitious program to rapidly reduce sequencing costs, initially from about $10M per genome to $100,000, and subsequently to $1,000. New sequencing technologies currently permeating biomedical research labs produce orders of magnitude more data much more quickly and at a fraction of the cost, and continue to undergo rapid improvements. The goal of full human genome sequencing for $100,000 is likely to be achieved this year, meeting the original schedule. Importantly, these same technologies can also be used for other genomic analyses such as transcriptome, transcriptional regulatory site, and small RNA studies.

As powerful as they are, these technologies have shortcomings. The read length and quality are not as good as those of the previous (Sanger/capillary) generation technology. They also remain too expensive to apply to whole genome sequencing of large numbers of samples, so for many studies (e.g., of large numbers of tumors, or in their emerging use in genome-wide association studies) only a fraction of the genome (e.g., exons) is analyzed. However, we now know that non-exonic and non-protein coding regions, and longer-range copy number and genomic structural variation, play important roles in complex disease.

A subsequent generation of technologies is emerging, fueled by the convergence of expertise and capabilities from fields as diverse as nanoscience, optical and electronic sensors, biochemical engineering and high throughput data processing. These technologies offer the potential of producing (1) additional orders of magnitude of cost reduction to $1,000, so that whole-genome sequencing for very large numbers of samples would be realistic, and (2) long continuous sequence reads to greatly simplify the downstream bioinformatics to analyze genome sequence in the context of structural and copy number variation. Similar to the second generation technologies, these methods also promise utility for transcriptome and other sequence-related analyses, potentially including the direct determination of epigenomic marks on the genome.

Breakthroughs in understanding the underlying science and implementing it as robust technology are on the horizon. NHGRI seeks projects that, in two years, would demonstrate (1) feasibility of novel sequencing concepts or (2) how to achieve substantial (a) decrease in sequencing cost with increases in (b) readlength and (c) data quality.

Proposals are limited to $1,500,000 total costs per year for two years.

Letters of intent should be sent to:

Jeffery A. Schloss, Ph.D.
NHGRI
5635 Fishers Lane, Suite 4076
Bethesda, MD 20892-9305
schlossj@exchange.nih.gov

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Cellular Responses to Perturbations

A sufficiently large catalog of cellular responses to perturbations would enable the discovery of novel biological relationships between perturbagens, genes, and cellular states. These relationships would provide insight into several significant areas of research including drug discovery and systems biology.

NHGRI seeks proposals for high-throughput data generation and integrative computational analysis of cellular signatures generated in response to a diverse set of perturbing agents, such as small molecules and siRNA. Recognizing the two-year time span for ARRA funding, the purpose of this initiative is to provide the basis for NHGRI to assess the feasibility and value of such approaches for obtaining important insight into human biology and etiology of disease, as well as to establish optimal methods to conduct such studies (selection of perturbation agents, cell lines, experimental and computational approaches). Proposals leveraging other community resource projects such as Molecular Libraries, GTEx, or ENCODE to enhance systematic searches for novel biological relationships would be particularly welcome. The proposals should address the range of activities needed to establish this activity, including developing an efficient data production pipeline, creating standardized approaches to experimental design, sharing of data and protocols, integrating analytical methods, assessing data quality, validating high-throughput findings, and developing standard database structures and end user interfaces.

NHGRI is open to a broad range of specific applications (drug discovery; benchmarking effects of novel compounds, disease models); cell types; perturbagens (small molecules, RNAi, genetic variation), and signatures (transcriptional, proteomic, cell phenotype, etc.) as long as the approach can be justified in terms of providing both direct and general insight into the feasibility and utility of a large catalog of cellular signatures.

Proposals are limited to $1 million total costs per year for two years.

Letters of intent should be sent to:

Dr. Ajay

NHGRI
5635 Fishers Lane, Suite 4076
Bethesda, MD 20892-9305
301-496-7531
ajaydr@mail.nih.gov

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ARRA Medical Sequencing Discovery Projects

The NHGRI encourages proposals for moderate-scale (each $1-2 million total costs per year), integrated research projects that will bring next-generation sequencing technology to bear on high-impact human genetic disease research. NHGRI will provide two years of support through the GO grant program for this purpose.

This initiative seeks highly creative research applications from groups that propose to form an integrated team of human geneticists, experts who have experience with next-generation sequencing technologies, and analysts who can bring bioinformatics tools to bear on interpreting the complex and large data sets from next-generation sequencing to address a significant, specific question (or set of closely related questions) about the genetic underpinnings of a human disease or medically relevant human trait.

Because ARRA Medical Sequencing Discovery Projects will focus on the use of next-generation sequencing methods to address a specific biomedical question, the genetic analysis of the disease or trait to be studied must be sufficiently advanced that sequencing is the next critical step to identifying and understanding the genetic susceptibility or cause of the disease or trait. The proposed project must be sufficiently well-defined that significant progress can be made with two-year, ARRA funds; for this reason, the ARRA Discovery Project program will not support new sequencing groups or infrastructure for the long term. Rather, this program is intended to encourage rapid progress in medical genetics/genomics, to disperse knowledge about how to do sequencing projects, and to nucleate creative integrated research teams applying cutting-edge genomic sequencing to medical genetics discovery.

Background: NHGRI has funded large-scale genomic sequencing centers since the time it first undertook the sequencing of the entire human genome and the genomes of many model organisms. NHGRI continues to support this model of funding to pursue those genomics-based projects that require a scale not possible in smaller laboratories, and the NHGRI-supported large-scale centers are using "next-generation" sequencing technologies to focus on projects that require ever-larger sequence data sets (e.g., large population studies). At the same time, however, these technologies are making cost-efficient sequencing possible on a significant but lower scale by smaller, well-integrated research groups organized to address specific biomedical questions. Therefore, this ARRA initiative is specifically intended to encourage such efforts.

This initiative is based on several points that were raised by attendees of a March 23-24, 2009 NHGRI planning workshop on "The Future of Large-scale Genomic Sequencing":

  1. There are long-term advantages to biomedical science, including more rapid discovery of the underpinnings of genetic disease, if knowledge about how to produce and use sequence data is more widely dispersed. NHGRI can contribute by fostering the dissemination of the skills and tools for doing genomics-based projects that are now concentrated in the larger centers.

  2. Excessively concentrating sequencing in a few large centers can result in missed opportunities for diverse and creative thinking about important medical genetics projects. There is a concern that larger sequencing centers are not set up to give adequate attention to small, yet compelling, projects.

  3. Smaller, focused sequencing projects should be driven by the biology of the disease being studied and therefore addressed by an integrated team of investigators working together to develop a creative approach to finding and understanding the genomic underpinnings of the disease phenotype.

  4. NHGRI should be a positive force for the dispersal of knowledge regarding implementation of next-generation sequencing. Such efforts will be more effective if they also gain the support of the categorical disease institutes at NIH.

Guidance for Applicants: It is expected that the applications will include information regarding the following:

  1. Appropriate biological, sequencing and analytical expertise in the applicant group to design a creative integrated project. It is particularly important that the group include experts in analysis of next-generation sequence data sets, including all steps from read mapping and quality assessment (including validation) of data to interpretation.

  2. Available appropriate samples, consented for release of molecular and clinical data through a controlled access database (dbGaP). Applicants should provide sufficient information on the samples and consents to convince reviewers that the availability of appropriate samples will not delay the research project.

  3. Project design issues, such as the number and type of samples needed to provide adequate power to detect relevant variants, the number and type of variants that are expected to be found, and how these will be analyzed towards the ultimate goal of finding disease/trait-related variants.

  4. A well-reasoned research plan that will convince reviewers that the proposed sequencing will (a) make a difference in the understanding of the disease or trait proposed for study, and (b) can be done efficiently, at a quality that is high enough to allow reliable conclusions, and at a reasonable cost. The plan may include infrastructure for data validation by, for example, genotyping.

  5. A statement that the data generated by the ARRA Medical Sequencing Discovery Project will be rapidly submitted to an appropriate open-access or controlled-access database, in accord with NHGRI Data Release policies.

  6. Follow-up studies. Extensive functional analyses will not be supported in the ARRA Medical Sequencing Discovery Projects. However, the proposal should document the applicant's expertise and ability to perform follow-up studies to interpret the functional significance of discovered variants, in a way that gives the reviewers a sense of how the results of this ARRA-funded project will later lead to important medical discoveries.

NHGRI seeks to fund as many ARRA Medical Sequencing Discovery Projects as possible within the available funds. Therefore, an important review criterion will be the efficiency and creativity with which the applicant has proposed to use the funds towards addressing the scientific problem that motivates the application. While groups may request up to $2 million, funding of projects requesting the full amount will be the exception because NHGRI's priority is to maximize the number of projects funded.

The type of biomedical genetics studies to be addressed by applicants has not been specified here, to encourage submission of a full range of topics. NHGRI recognizes that the overall budget and two-year funding period may limit the questions that can be addressed. However, creative project choice and design, plus rapidly falling sequencing costs, should allow for a range of effective responses to this solicitation.

Because one of the intents of this initiative is to disperse knowledge of, and use of, significant sequencing capacity, NHGRI will not consider applications from existing large-scale sequencing centers (e.g., ones now receiving funding of $10M/year or more from all sources) to be responsive. In making funding decisions, the goal of dispersal of expertise will be considered along with other factors such as the significance of the problem chosen, the creativity of approach, and the research plan.

As the objective of this program is to use ARRA funds to support projects that can be completed within two years, applications for renewals of these awards will not, as a rule, be accepted by NHGRI. The ARRA Discovery Center projects should be designed so that future studies would be appropriate for support by the relevant categorical disease institute and not by NHGRI.

Because of the potentially complex nature of these projects, prospective applicants are strongly encouraged to discuss their ideas with NHGRI program staff prior to submission. Prospective applicants are also strongly encouraged to submit a letter of intent to allow NHGRI staff to estimate the potential review workload and plan the review.

NHGRI expects that there will be substantial interest in this opportunity. Among the applications that receive superior review priority scores, NHGRI will give funding priority to cases in which co-funding for the sequencing is available; however, a commitment for an additional funding source is not a requirement for application.

Letters of intent should be sent to:

Jane Peterson
NHGRI
5635 Fishers Lane, Suite 4076
Bethesda, MD 20892-9305
petersoj@mail.nih.gov


Software Dissemination Plans: For NHGRI, applications that propose to develop software must include a dissemination plan, with appropriate timelines. This should be included in a separate heading in the Research Design and Methods section. There is no prescribed single license for software produced through grants supported through this FOA, although open-source software sharing is strongly encouraged. NHGRI has goals for software dissemination, and reviewers will be instructed to evaluate the dissemination plan relative to these goals:

a. The software, including the source code, should be freely available to the broad research community.

b. The terms of software availability should permit the dissemination and commercialization of enhanced or customized versions of the software, and incorporation of the software or pieces of it into other software packages.

c. To preserve utility to the community, the software should be transferable so that another group could continue development if the original investigators were unwilling or unable to do so.

d. The terms of software availability should allow researchers to modify the source code and to share modifications with other colleagues. An awardee should be responsible for creating the original and subsequent "official" versions of a piece of software.

e. To further enhance the potential impact of their software, applicants may consider proposing a plan to manage and disseminate the improvements or customizations of their tools and resources by others. This proposal may include a plan to incorporate the enhancements into the "official" core software, may involve the creation of an infrastructure for plug-ins, or may describe some other solution.

Key Dates

Release/Posted Date: March 20, 2009
Opening Date: April 27, 2009 (Earliest date an application may be submitted to Grants.gov)
Letters of Intent Receipt Date(s): April 27, 2009
NOTE: On-time submission requires that applications be successfully submitted to Grants.gov no later than 5:00 p.m. local time (of the applicant institution/organization).
Application Due Date(s): May 27, 2009
Peer Review Date(s): June/July 2009
Council Review Date(s): August 2009
Earliest Anticipated Start Date(s): September 30, 2009
Expiration Date: May 28, 2009

Contacts

Investigators are strongly encouraged to send an e-mail to the appropriate staff member succinctly describing the nature of the inquiry; a telephone call can then be subsequently arranged if necessary.

For scientific inquiries, investigators should contact the Program Director(s) identified above for the particular priority area. For inquiries about projects in other areas of the NHGRI's interest, investigators should contact:

Mark Guyer
Director, Division of Extramural Research (DER)
National Human Genome Research Institute, NIH
5635 Fishers Lane, Ste. 4130 MSC 9306
Bethesda, MD 20892-9306
Courier services should use: Rockville, Maryland 20852
E-mail: guyerm@exchange.nih.gov

For fiscal inquiries, awardees should contact:
Cheryl Chick
Chief Grants Management Officer
Grants Administration Branch, DER
National Human Genome Research Institute, NIH
5635 Fishers Lane, Ste. 4076, MSC 9306
Bethesda, MD 20892-9306
Courier services should use:
Rockville, Maryland 20852
E-mail: ChickC@mail.nih.gov

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Last Reviewed: October 1, 2012