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NIDDK Offers $15M for Type 1 Diabetes Genes Studies

NEW YORK (GenomeWeb News) – The National Institute of Diabetes and Digestive and Kidney Diseases expects to spend $15 million in 2012 to fund research that seeks to determine the functions of human leukocyte antigen (HLA) genes and non-HLA genes and how they are involved in type 1 diabetes.

The grants will provide between $1.25 million and $5 million to fund between three and five research projects into the role and function of HLA molecules in the disease process, and to understand the contribution of many genes that have been identified that may be related to T1D and other autoimmune diseases.

Genome-wide SNP typing by the Type 1 Diabetes Genetics Consortium and collaborators has provided support for roughly 50 genes or gene regions that significantly affect the risk of T1D, and efforts to fill in the T1D heritability picture are underway. NIDDK believes that the missing heritability for this type of diabetes lies in unmapped common variants, rare variants, structural polymorphisms, gene-gene interactions, or gene-environment interactions. It said more work is needed to identify causal genes and variants for allele-specific expression and genotype-to-phenotype studies.

NIDDK plans to fund a range of research endeavors, such as human studies that use DNA samples from well-characterized individuals to correlate a gene variant with a particular phenotype or endophenotype, and studies that will use genetic methodologies to identify causative genes or epigenetic variants of interest.

The research also could include studies that compare wild type and gene variant function, and which identify the most promising molecular targets for treating T1D, as well as identifying non-coding RNAs that are relevant to gene function. Other studies might seek to identify epigenomic features associated with diabetes or to validate epigenetic regulation in the context of diabetes.

System-level research approaches that use bioinformatic resources, gene expression, epigenomic, proteomic, and metabolomic databases can be mined to generate testable hypotheses concerning the function of candidate genes and groups of genes.

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