Posted: November 11, 2019

Letter of Intent due February 26, 2020; Full Application due March 26, 2020

Potential research areas, include but are not limited to:

  • Human Studies: Functional validation studies should preferably use human DNA samples from phenotypically well-characterized individuals to correlate a gene variant with a phenotype and/or endophenotype.
  • Comparison of wild type and gene variant functions: The molecular alteration associated with a gene variant frequently does not reveal whether the function of a gene is increased, decreased, or leads to unexpected functional consequences. Most genes have different levels of expression in different tissues. It is important to evaluate genetic changes in multiple cell types relevant to T1D. Approaches that can address these issues will help to identify the most promising molecular targets for therapeutic interventions to prevent and/or reverse T1D.
  • Identification of causal genes/genetic variants: Studies exploiting genetic methodologies in concert with other methods, such as functional genomics, in silico mapping data, gene expression profiling, to identify causative genes or epigenetic/genetic variants and then unravel mechanisms of these genes are appropriate for this program announcement.
  • Non-coding RNAs and regulatory elements: Studies to identify non-coding RNAs, transcription factors relevant to diabetes and relate function to genes/variants in non-coding RNAs, microRNAs, gene regulatory elements, gene copy number, or other putative non-protein coding regions of the genome are appropriate.
  • Epigenetics and Epigenomics: Identification of cell type-specific epigenomic features associated with diabetes as well as functional validation of epigenetic mechanisms of gene regulation in the context of diabetes are relevant.
  • Systems-level approaches: Bioinformatic resources (i.e., interactome, gene expression, epigenomic, proteomic, metabolomic, and anatomical databases) can be mined to generate testable hypotheses concerning the function of candidate genes and groups of genes and build a framework to understand the contribution of interacting networks based on causal effector transcripts to disease heterogeneity.
  • Computational Approaches: Use of novel computational methods to integrate functional data and other data types to help to understand which variants are causal for a phenotype;
  • Integrative analyses of multidimensional datasets such as genetics, genomics, transcriptomics, proteomics, metabolomics, and/or phenomics to identify key signatures, specific cell subsets, and/or biological pathways for further functional validation
  • Animal studies/Model organisms: Animal studies and model organisms can be included, if they are complementary to the proposed human research and address mechanistic or therapeutic questions that cannot be addressed directly in humans.
  • Limited Fine mapping and sequencing of a targeted human genomic region(s), including coding and non-coding;
  • High Throughput approaches: The use of high-throughput approaches such as CRISPR modification of cell models for rapid phenotype screening and/or regulatory mapping

Below are types of projects that are not intended to be supported by this RFA:

  • Clinical trials are not responsive to this FOA;
  • Initial discovery Genome Wide Associated Studies (GWAS) or sequencing efforts;
  • Simple replication of initial genomic findings.

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