Rare Variants of IFIH1, a Gene Implicated in Antiviral Responses, Protect against Type 1 Diabetes

By S. Nejentsev, N. Walker, D. Riches, M. Egholm, John A. Todd. Science, 2009

1,029 citations recorded by the source corpus.

Genome-wide association studies (GWASs) are regularly used to map genomic regions contributing to common human diseases, but they often do not identify the precise causative genes and sequence variants. To identify causative type 1 diabetes (T1D) variants, we resequenced exons and splice sites of 10 candidate genes in pools of DNA from 480 patients and 480 controls and tested their disease association in over 30,000 participants. We discovered four rare variants that lowered T1D risk independently of each other (odds ratio = 0.51 to 0.74; P = 1.3 × 10–3 to 2.1 × 10–16) in IFIH1 (interferon induced with helicase C domain 1), a gene located in a region previously associated with T1D by GWASs. These variants are predicted to alter the expression and structure of IFIH1 [MDA5 (melanoma differentiation-associated protein 5)], a cytoplasmic helicase that mediates induction of interferon response to viral RNA. This finding firmly establishes the role of IFIH1 in T1D and demonstrates that resequencing studies can pinpoint disease-causing genes in genomic regions initially identified by GWASs.

Subfields: Genetics, Immunology

View Paper: https://www.semanticscholar.org/paper/9288d828fa29f9be5dd86f1b1705fab3c7abd473

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Citations in the corpus

Rare and Common Variants: Twenty arguments (2011, background): “Initial resequencing of genes that have been identified by GWAS”
Pattern recognition receptors and inflammation. (2010, background): “Interestingly, mutations in MDA5 that disrupt its signaling ability correlate with resistance to type I diabetes, although the mechanism for the contribution of MDA5 to this disease remains unclear (Nejentsev et al., 2009).”
The genetics of Alzheimer disease: back to the future. (2010, background): “, 2010), but also to ‘‘resolve’’ the complex patterns that typically emerge from GWAS approaches (Dickson et al., 2010; Johansen et al., 2010; Nejentsev et al., 2009), althoughmost of these findings still await functional genetic confirmation and characterization.”
Deep resequencing of GWAS loci identifies independent rare variants associated with inflammatory bowel disease (2011, background): “Targeted sequencing of pooled samples allows researchers to efficiently and cost-effectively capture all variation in a limited target region that has been selectively amplified in multiple DNA sample”
Uncovering the roles of rare variants in common disease through whole-genome sequencing (2010, background): “Even when rare variants in the same regions as GWA signals have been identifie”
Pooled association tests for rare variants in exon-resequencing studies. (2010, methodology): “Statistical tests were applied to previously associated gene sets: ANGPTL3 (MIM 604774), ANGPTL4 (MIM 605910), ANGPTL5 (MIM 607666) for triglyceride levels, IFIH1 (MIM 606951) for type 1 diabetes, and a set of 21 genes for obesity.(11,13,14) For each statistical test, the number of permutations was ”
A genome-wide association study identifies new psoriasis susceptibility loci and an interaction between HLA-C and ERAP1 (2010, background): “, and both rare and common IFIH1 variants are associated with type 1 diabete”
Multiple common variants for celiac disease influencing immune gene expression (2010, positive): “Taken together with the recent observation that rare loss-of-function mutations in the enteroviral response gene IFIH1 are protective against type 1 diabete”
Type 1 diabetes: etiology, immunology, and therapeutic strategies. (2011, result): “variants in T1D is in line with this hypothesis (304).”
Optimal unified approach for rare-variant association testing with application to small-sample case-control whole-exome sequencing studies. (2012, methodology): “When 10% of the rare variants were causal, c = log(7), which gives an odds ratio equal to 7 for a variant with MAF = 0.”
A survey of tools for variant analysis of next-generation genome sequencing data (2013, methodology): “Finally, NGS can also be used to identify trait loci by re-sequencing candidate genes in a large number of patients and controls as demonstrated for Type 1 diabetes [41].”
Rare Variants Create Synthetic Genome-Wide Associations (2010, background): “Second, it is now known that rare variants contribute to common diseases, and that cases that carry the rare high-penetrant contributors to disease often have ‘‘typical’’ clinical presentations [19–21].”