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

Abstract

Genome-wide association studies (GWAS) are widely 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 re-sequenced exons and splice sites of ten candidate genes in pools of DNA from 480 patients and 480 controls and tested their disease association in over 30,000 subjects. We discovered four rare variants that lowered T1D risk independently of each other (OR = 0.51-0.74; P = 1.3×10 -3 -2.1×10 -16 ) in IFIH1, a gene located in a region previously associated with T1D by GWAS. These variants are predicted to alter the expression and structure of IFIH1 (MDA5), a cytoplasmic helicase that mediates induction of interferon response to viral RNA. This firmly establishes the role of IFIH1 in T1D and demonstrates that re-sequencing studies can pinpoint disease-causing genes in genomic regions initially identified by GWAS.Genome-wide association studies (GWAS) of common multifactorial diseases have identified dozens of loci harboring disease-causing sequence variants (1, 2). However, because the human genome contains regions of strong linkage disequilibrium, a diseaseassociated locus sometimes encompasses several genes and multiple tightly associated polymorphisms, making it difficult to pinpoint the causal variant by association mapping. Moreover, in many instances, the single nucleotide polymorphisms (SNPs) showing the most significant disease association map to genomic regions with no obvious function, thus providing few clues as to how causal variants affect the disease gene.One way to overcome this limitation is to search for sequence variants that are rare in the population (frequency < 3%) but that reside in exons and other genomic regions of known function to identify polymorphisms that likely alter expression of the gene and/or the function of the protein product (3). If rare disease-associated variants with obvious functional effects are found in a candidate gene that harbors a common disease-associated variant, then the gene is likely to be causal. Recent technological advances in highthroughput sequencing (4) provide an opportunity to re-sequence multiple genetic regions in hundreds of subjects and discover rare sequence variants (5-7). Here we used 454 Sequencing (8) to search for rare variants in ten candidate genes and study their association with type 1 diabetes (T1D), previously known as insulin-dependent diabetes mellitus (IDDM). T1D is a common disorder that develops as a result of a complex interaction of genetic and environmental factors leading to the immune-mediated destruction of the * To whom the correspondence should be addressed. E-mail: sn262@cam.ac.uk. Of the ten genes that we selected, six genes contain common T1D-associated polymorphisms: PTPN22, PTPN2, IFIH1, SH2B3, CLEC16A and IL2RA (10,11,(14)(15)(16)). Europe PMC Funders GroupWe also studied two genes that contain rare mutations causing monogenic syndromes that may include immune-m...

A unified approach for allele frequency estimation, SNP detection and association studies based on pooled sequencing data using EM algorithms

“…We then present the results on the power of SNP detection and association studies. Finally, we present our results on the analysis of the data in [8] using the approaches in the "Materials and Methods" section.…”

“…We apply our approaches to analyze the pooled sequencing data in [8]. First, we conduct SNP calling using both our method EM-SNP and SNVer (parameter setting -bq 20 -a 0 -f 0 -p 1 -t 0) [11], a program that has been shown to outperform several other programs for SNP calling including CRISP, SAMtools, and GATK.…”

“…We want to study if it is possible to estimate the minor allele frequency even if it is lower than the sequencing error rate. We let the read number n = 1000 and 3000, which is around the sequencing depth in [8]. To study the effect of the number of chromosomes, we let K = 50, 100, 200.…”

“…We use our method to study the pooled sequencing data related to type 1 Diabetes dataset (T1D) in [8] and compare the results with current methods for SNP identifiction [11]. The data was generated using DNA samples of 480 T1D patients and 480 healthy controls, arranged in 20 DNA pools, with 48 patients/controls in each pool.…”

“…Several research groups have used pooled sequencing to detect rare variants that are associated with complex traits such as retinitis pigmentosa, diabetes, cancer, and inflammatory bowel disease [5-8]. There are generally two types of pooling designs.…”