Type 1 diabetes (T1D) is a complex autoimmune disorder that arises from the action of multiple genetic and environmental risk factors. The Type 1 Diabetes Genetics Consortium (T1DGC) was established in 2001 to assemble the resources necessary for conducting large-scale genetic studies of T1D. The T1DGC recently reported the findings of a genome-wide linkage scan in 2,496 multiplex T1D families containing 2,658 affected sib-pairs (ASPs) with information on over 6,000 SNPs, and a genome-wide association scan (GWAS) meta- analysis of over 800,000 SNPs in 7,698 cases and 9,068 controls. From the linkage scan, evidence was obtained supporting T1D susceptibility genes in 6p21.3 (HLA), 6q, 2q32.3 (CTLA4), 11p15.5 (INS) and two novel regions on chromosome 19. From the GWAS, 27 novel regions were identified and 18 replicated with P <0.01 in an independent set of 4,267 cases, 4,463 controls and 2,319 affected sib-pair (ASP) families. Four additional regions were nominally replicated in these samples. The T1DGC has focused on the role of single nucleotide changes in the genome that may modify risk of T1D;however, it is becoming increasingly apparent that copy-number variation (CNV) contributes to the risk for a number of human diseases, including autism, schizophrenia, osteoporosis, early myocardial infarction, and Crohn's disease. Although CNV is clearly an important source of human genetic variation, there have been no publications evaluating the contribution of CNVs to T1D risk. This DP3 application proposes to characterize the role of CNVs in T1D risk in the T1DGC collection of 2,496 ASP families and replicate the findings in a separate collection of T1D ASPs, trio families, cases and controls. The T1DGC proposes a genome-wide analysis of CNVs that will (a) perform genome-wide typing of common CNVs in 2,496 T1DGC ASP families (familial cases of T1D) using the Agilent 8x60K CNV genotyping chip and conduct statistical genetic analysis to identify the most strongly associated CNVs with T1D;(b) replicate the putative associations above in additional T1D ASP families, trios and cases/controls using locus-specific assays;(c) conduct association testing of rare, large CNVs using an in silico data mining approach from T1DGC GWAS data;and (d) identify the proportion of rare LOF CNVs not identifiable from mining T1DGC GWAS data using a subset of 384 T1DGC GWAS samples using a 1M Agilent CNV chip. Our hypothesis is that CNVs contribute to the genetic risk for T1D. Further, a subset of the CNVs may reside in regions already identified by the T1DGC GWAS meta-analysis and, therefore, increase the likelihood that the candidate gene may have a specific function in regulation, thereby identifying potential therapeutic targets. It is important to target the entire genome to identify potential CNV-influenced T1D risk loci.

Public Health Relevance

Type 1 diabetes (T1D) is a complex autoimmune disorder that arises from the action of multiple genetic and environmental risk factors with significant burden to the public health of the United States through the complications (eye, kidney, heart, nerves) and its associated morbidity and mortality. This research proposes to scan the human genome in order to identify structural variants (copy number variants, CNVs) are associated with risk of T1D. Identification of genetic risk factors for T1D is the first step in risk prediction, intervention and developing disease therapeutics (prevention).

National Institute of Health (NIH)
National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK)
Type 1 Diabetes Targeted Research Award (DP3)
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Special Emphasis Panel (ZDK1-GRB-N (O1))
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Akolkar, Beena
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University of Virginia
Public Health & Prev Medicine
Schools of Medicine
United States
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Zanda, Manuela; Onengut-Gumuscu, Suna; Walker, Neil et al. (2014) A genome-wide assessment of the role of untagged copy number variants in type 1 diabetes. PLoS Genet 10:e1004367
Zanda, Manuela; Onengut, Suna; Walker, Neil et al. (2012) Validity of the family-based association test for copy number variant data in the case of non-linear intensity-genotype relationship. Genet Epidemiol 36:895-8
Norris, Jill M; Rich, Stephen S (2012) Genetics of glucose homeostasis: implications for insulin resistance and metabolic syndrome. Arterioscler Thromb Vasc Biol 32:2091-6
Dauber, Andrew; Yu, Yongguo; Turchin, Michael C et al. (2011) Genome-wide association of copy-number variation reveals an association between short stature and the presence of low-frequency genomic deletions. Am J Hum Genet 89:751-9