Four rare polymorphisms in IFIH1 (interferon induced with helicase C domain 1), a gene implicated in antiviral responses, lowered the risk for type-1 diabetes independently of each other. This finding firmly establishes the role of IFIH1 in type-1diabetes and demonstrates that resequencing studies using the Genome Sequencer FLX System can pinpoint disease-causing genes in genomic regions, initially identified by genome-wide association studies.
Genome-wide association studies of common multifactorial diseases have identified dozens of loci harboring disease causing sequence variants. However, because the human genome contains regions of strong linkage disequilibrium, a disease-associated 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 alter expression of the gene and/or function of the protein product.
Recent technological advances in high-throughput sequencing such as the 454 Sequencing technology provide an opportunity to resequence multiple genetic regions in hundreds of participants and discover rare sequence variants.
Type-1 diabetes, previously known as insulin-dependent diabetes mellitus (IDDM), 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 insulin-producing pancreatic b cells. To date, 15 loci associated with type-1 diabetes have been identified in the human genome.
In order to discover previously unseen rare type-1 diabetes variants and test their association with type-1 diabetes, Nejentsev et al. resequenced exons and splice sites of ten candidate genes in pools of DNA of individuals with type-1 diabetes and controls with the Genome Sequencer FLX System, and tested their disease association in over 30,000 participants.
Materials and Methods
DNA pools experiment
The DNA concentration was measured in research samples of individuals with type-1 diabetes and controls, followed by ten pooled samples each comprising equal amounts of DNA from 48 individuals with type-1 diabetes and then ten pooled samples each comprising equal amounts of DNA from 48 healthy controls. Thus, altogether the DNA of 480 individuals with type-1 diabetes and 480 controls from Great Britain were resequenced. The authors designed oligonucleotide primers to amplify 144 target regions that covered exons and regulatory sequences of ten selected genes. Of these, six contained common type-1 diabetes-associated polymorphisms such as PTPN22, PTPN2, IFIH1, SH2B3, CLEC16A, and IL2RA. In addition, FOXP3, AIRE, IAN4L1, and KCNJ11 were also studied.
Nejentsev et al. used a script to count the number of reads carrying the three nucleotides A, C, G, T, unknown nucleotides (N) or missing nucleotides (deletions) in each contig position, separately for reads generated from pooled DNA research samples of type-1 diabetes cases or controls. Frequencies of reads carrying nucleotides A, C, G, T or missing nucleotides were calculated. In each contig reads generated from each pooled DNA sample represented 96 chromosomes, which facilitated distinction of true polymorphisms from artifacts. In the pooled samples it was impossible to distinguish rare insertion/deletion polymorphisms from sequencing errors; therefore, only nucleotide substitutions were studied.
Allele frequencies were calculated separately for reads generated from 960 chromosomes of individuals with type-1 diabetes and 960 chromosomes of controls and then the number of chromosomes in the original pools that carry different allelic nucleotides was estimated. To test how read output estimated allele frequency among samples in the DNA pool, the researchers analyzed eight SNPs from the sequenced regions that had been genotyped previously.
A case-control collection consisting of 8,379 individuals with type-1 diabetes and 10,575 controls from Great Britain as well as a family collection including 3,165 TD1-families with one or two affected offspring were studied. Genotyping was done using TaqMan®, and pre-designed assays for the IFIH1 (interferon induced with helicase C domain 1) SNPs rs35667974 and rs35337543 from Applied Biosystems were used. For other SNPs the researchers ordered Assays-by-design.
Results and Discussion
Using the Genome Sequencer FLX System, the authors generated 9.4 million reads with an average length of 250 bases and identified a total of 212 SNPs. Of these, 33 were classified as common because their estimated minor allele frequency (MAF) was >3%, and 179 were categorized as rare because their estimated MAF was <3%. Of the 179 rare SNPs, 156 were previously unseen.
The study group found a good correlation between allele frequency in the individual samples and its estimate in the DNA pools (correlation coefficient r = 0.99), demonstrating that high-throughput sequencing of the DNA pools can be used to accurately measure allele frequencies. As expected, the study group confirmed the previously known association of the common SNPs with type-1diabetes (P = 0.02; 5 x 10-7, χ2 test).
In addition, the researchers discovered four rare polymorphisms in IFIH1 which had stronger protective effects on type - diabetes risk [odds ratio (OR) = 0.51 to 0.74] (Table 1) than does the common nsSNP rs1990760/T946A (OR = 0.86). For example, rare individuals carrying valine at position 923 of the IFIH1 protein have only ~ 50% risk of developing type-1 diabetes compared with those who carry isoleucine.
IFIH1, also known as MDA5 (melanoma differentiation-associated protein 5), is a 1,025–amino acid cytoplasmic protein that recognizes RNA of picornaviruses and mediates immune activation. Infection with enteroviruses, which belong to the picornavirus family, is more common among newly diagnosed individuals with type-1 diabetes and prediabetic subjects than in the general population, and it precedes the appearance of autoantibodies (markers of prediabetes). Upon infection, IFIH1 senses the presence of viral RNA in the cytoplasm, triggers activation of NF-kB and interferon regulatory factor pathways, and induces antiviral interferon-b response.
Nejentsev and co-workers have found that rare alleles of all associated IFIH1 polymorphisms consistently protect from type-1 diabetes, whereas IFIH1 alleles carried by the majority of the population predispose to the disease. This observation suggests that variants that disrupt IFIH1 function in the host antiviral response have been negatively selected rather than positively selected, because they confer protection from type-1 diabetes.
Although the mechanisms by which IFIH1 polymorphisms contribute to type-1 diabetes pathogenesis remain to be explored, the authors noted that one of the protective variants is a non-sense mutation leading to a truncated 626–amino acid protein lacking the C-terminal helicase domain (Figure 1), whereas two other protective variants localize to the conserved splice donor sites and probably disrupt normal splicing of the IFIH1 transcript. This suggests that variants, which are predicted to reduce function of the IFIH1 protein, would decrease the risk of type-1 diabetes, whereas normal IFIH1 function is associated with type-1 diabetes. The study marks one of the first of its kind in discovering a number of protective type-1 diabetes alleles directly from the results of a previously conducted GWAS and demonstrates the utility of targeted resequencing with the Genome Sequencer FLX System for follow-up genome-wide association studies.
This article was summarized for BIOCHEMICA from Nejentsev S et al. (2009) Science 324:387–389.
This article was originally published in Biochemica 3/2009, pages 4-6. ©Springer Medizin Verlag 2009