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SummarySynonymous mutations change the DNA sequence of a gene without affecting the amino acid sequence of the encoded protein. Although emerging evidence suggests that synonymous mutations can impact RNA splicing, translational efficiency, and mRNA stability1, studies in human genetics, mutagenesis screens, and other experiments and evolutionary analyses have repeatedly shown that most synonymous variants are neutral or only weakly deleterious, with some notable exceptions. In their recent article, Shen et al. claim to have disproved these well-established findings. They perform mutagenesis experiments in yeast and conclude that synonymous mutations frequently reduce fitness to the same extent as nonsynonymous mutations2. Based on their findings, the authors state that their results “imply that synonymous mutations are nearly as important as nonsynonymous mutations in causing disease.” An accompanying News and Views argues that “revising our expectations about synonymous mutations should expand our view of the genetic underpinnings of human health”3. Considering potential technical concerns with these experiments4 and a large, coherent body of knowledge establishing the predominant neutrality of synonymous variants, we caution against interpreting this study in the context of human disease.

Genetic variants showing associations with specific biological traits and diseases detected by genome-wide association studies (GWAS) commonly map to non-coding DNA regulatory regions. Many of these regions are located considerable distances away from the genes they regulate and come into their proximity through 3D chromosomal interactions. We previously developed COGS, a statistical pipeline for linking GWAS variants with their putative target genes based on 3D chromosomal interaction data arising from high-resolution assays such as Promoter Capture Hi-C (PCHi-C). Here, we applied COGS to COVID-19 Host Genetic Consortium (HGI) GWAS meta-analysis data on COVID-19 susceptibility and severity using our previously generated PCHi-C results in 17 human primary cell types and SARS-CoV-2-infected lung carcinoma cells. We prioritise 251 genes putatively associated with these traits, including 16 out of 47 genes highlighted by the GWAS meta-analysis authors. The prioritised genes are expressed in a broad array of tissues, including, but not limited to, blood and brain cells, and are enriched for genes involved in the inflammatory response to viral infection. Our prioritised genes and pathways, in conjunction with results from other prioritisation approaches and targeted validation experiments, will aid in the understanding of COVID-19 pathology, paving the way for novel treatments.

Supplemental Digital Content is available in the text., Background: Approximately 25% of patients with pulmonary arterial hypertension (PAH) have been found to harbor rare mutations in disease-causing genes. To identify missing heritability in PAH, we integrated deep phenotyping with whole-genome sequencing data using Bayesian statistics. Methods: We analyzed 13 037 participants enrolled in the NBR study (NIHR BioResource—Rare Diseases), of which 1148 were recruited to the PAH domain. To test for genetic associations between genes and selected phenotypes of pulmonary hypertension, we used the Bayesian rare variant association method BeviMed. Results: Heterozygous, high impact, likely loss-of-function variants in the kinase insert domain receptor (KDR) gene were strongly associated with significantly reduced transfer coefficient for carbon monoxide (posterior probability=0.989) and older age at diagnosis (posterior probability=0.912). We also provide evidence for familial segregation of a rare nonsense KDR variant with these phenotypes. On computed tomographic imaging of the lungs, a range of parenchymal abnormalities were observed in the 5 patients harboring these predicted deleterious variants in KDR. Four additional PAH cases with rare likely loss-of-function variants in KDR were independently identified in the US PAH Biobank cohort with similar phenotypic characteristics. Conclusions: The Bayesian inference approach allowed us to independently validate KDR, which encodes for the VEGFR2 (vascular endothelial growth factor receptor 2), as a novel PAH candidate gene. Furthermore, this approach specifically associated high impact likely loss-of-function variants in the genetically constrained gene with distinct phenotypes. These findings provide evidence for KDR being a clinically actionable PAH gene and further support the central role of the vascular endothelium in the pathobiology of PAH.

BackgroundGenome-wide association studies (GWAS) have identified pervasive sharing of genetic architectures across multiple immune-mediated diseases (IMD). By learning the genetic basis of IMD risk from common diseases, this sharing can be exploited to enable analysis of less frequent IMD where, due to limited sample size, traditional GWAS techniques are challenging.MethodsExploiting ideas from Bayesian genetic fine-mapping, we developed a disease-focused shrinkage approach to allow us to distill genetic risk components from GWAS summary statistics for a set of related diseases. We applied this technique to 13 larger GWAS of common IMD, deriving a reduced dimension “basis” that summarised the multidimensional components of genetic risk. We used independent datasets including the UK Biobank to assess the performance of the basis and characterise individual axes. Finally, we projected summary GWAS data for smaller IMD studies, with less than 1000 cases, to assess whether the approach was able to provide additional insights into genetic architecture of less common IMD or IMD subtypes, where cohort collection is challenging.ResultsWe identified 13 IMD genetic risk components. The projection of independent UK Biobank data demonstrated the IMD specificity and accuracy of the basis even for traits with very limited case-size (e.g. vitiligo, 150 cases). Projection of additional IMD-relevant studies allowed us to add biological interpretation to specific components, e.g. related to raised eosinophil counts in blood and serum concentration of the chemokine CXCL10 (IP-10). On application to 22 rare IMD and IMD subtypes, we were able to not only highlight subtype-discriminating axes (e.g. for juvenile idiopathic arthritis) but also suggest eight novel genetic associations.ConclusionsRequiring only summary-level data, our unsupervised approach allows the genetic architectures across any range of clinically related traits to be characterised in fewer dimensions. This facilitates the analysis of studies with modest sample size by matching shared axes of both genetic and biological risk across a wider disease domain, and provides an evidence base for possible therapeutic repurposing opportunities.

Deriving mechanisms of immune-mediated disease from GWAS data remains a formidable challenge, with attempts to identify causal variants being frequently hampered by strong linkage disequilibrium. To determine whether causal variants could be identified from their functional effects, we adapted a massively parallel reporter assay for use in primary CD4 T cells, the cell type whose regulatory DNA is most enriched for immune-mediated disease SNPs. This enabled the effects of candidate SNPs to be examined in a relevant cellular context and generated testable hypotheses into disease mechanisms. To illustrate the power of this approach, we investigated a locus that has been linked to six immune-mediated diseases but cannot be fine-mapped. By studying the lead expression-modulating SNP, we uncovered an NF-κB-driven regulatory circuit which constrains T-cell activation through the dynamic formation of a super-enhancer that upregulates TNFAIP3 (A20), a key NF-κB inhibitor. In activated T cells, this feedback circuit is disrupted-and super-enhancer formation prevented-by the risk variant at the lead SNP, leading to unrestrained T-cell activation via a molecular mechanism that appears to broadly predispose to human autoimmunity.

Integration of genome-wide association study (GWAS) data has been used to generate new hypotheses of biological mechanism, aetiological relationships between traits, or test causality of one factor for another. However, such approaches have typically been limited to pairwise comparisons of traits. We propose a generally applicable method, that exploits ideas from Bayesian genetic fine mapping to define a “lens” that focuses relevant variants before dimension reduction of a set of related GWAS summary statistics. We applied this technique to immune-mediated diseases, deriving 13 components which summarise the multidimensional patterns of genetic risk. Projection of independent datasets demonstrated the specificity and accuracy of our reduced dimension basis, enabled us to functionally characterise individual components, identify disease-discriminating components and suggest novel associations in rare diseases where classical GWAS approaches are challenging. Our approach summarises the genetic architectures underlying any range of aetiologically-related traits in fewer dimensions, facilitating more nuanced multidimensional comparative analyses.

Contains fulltext : 229571.pdf (Publisher’s version ) (Closed access) BACKGROUND: An increasing number of NFKB1 variants are being identified in patients with heterogeneous immunologic phenotypes. OBJECTIVE: To characterize the clinical and cellular phenotype as well as the management of patients with heterozygous NFKB1 mutations. METHODS: In a worldwide collaborative effort, we evaluated 231 individuals harboring 105 distinct heterozygous NFKB1 variants. To provide evidence for pathogenicity, each variant was assessed in silico; in addition, 32 variants were assessed by functional in vitro testing of nuclear factor of kappa light polypeptide gene enhancer in B cells (NF-κB) signaling. RESULTS: We classified 56 of the 105 distinct NFKB1 variants in 157 individuals from 68 unrelated families as pathogenic. Incomplete clinical penetrance (70%) and age-dependent severity of NFKB1-related phenotypes were observed. The phenotype included hypogammaglobulinemia (88.9%), reduced switched memory B cells (60.3%), and respiratory (83%) and gastrointestinal (28.6%) infections, thus characterizing the disorder as primary immunodeficiency. However, the high frequency of autoimmunity (57.4%), lymphoproliferation (52.4%), noninfectious enteropathy (23.1%), opportunistic infections (15.7%), autoinflammation (29.6%), and malignancy (16.8%) identified NF-κB1-related disease as an inborn error of immunity with immune dysregulation, rather than a mere primary immunodeficiency. Current treatment includes immunoglobulin replacement and immunosuppressive agents. CONCLUSIONS: We present a comprehensive clinical overview of the NF-κB1-related phenotype, which includes immunodeficiency, autoimmunity, autoinflammation, and cancer. Because of its multisystem involvement, clinicians from each and every medical discipline need to be made aware of this autosomal-dominant disease. Hematopoietic stem cell transplantation and NF-κB1 pathway-targeted therapeutic strategies should be considered in the future.

Numerous genomic loci have been implicated in autoimmune disorders, but attempts to identify causal variants, and thereby disease mechanisms, have been hampered by strong linkage disequilibrium; leaving most loci unresolved and the potential of GWAS largely unfulfilled. To overcome this, we developed a massively-parallel reporter assay for use in primary CD4 T-cells, a key effector of many autoimmune diseases, and sought to resolve potential causal variants via their functional effects. This provided testable hypotheses into disease mechanisms, which we illustrate using a multi-disease-associated locus on chromosome 6. By investigating the lead expression-modulating SNP, we uncover an NF-κB-driven regulatory circuit which constrains T-cell activation through the dynamic formation of a super-enhancer that upregulates expression of TNFAIP3 (A20), a key NF-κB inhibitor. In activated T-cells, this circuit is disrupted – and super-enhancer formation prevented – by the risk variant at the lead SNP, thus revealing a molecular mechanism that predisposes to multiple autoimmune diseases.

A. Palotie on työryhmän Int IBD Genetics Consortium jäsen. One goal of human genetics is to understand the genetic basis of disease, a challenge for diseases of complex inheritance because risk alleles are few relative to the vast set of benign variants. Risk variants are often sought by association studies in which allele frequencies in case subjects are contrasted with those from population-based samples used as control subjects. In an ideal world we would know population-level allele frequencies, releasing researchers to focus on case subjects. We argue this ideal is possible, at least theoretically, and we outline a path to achieving it in reality. If such a resource were to exist, it would yield ample savings and would facilitate the effective use of data repositories by removing administrative and technical barriers. We call this concept the Universal Control Repository Network (UNICORN), a means to perform association analyses without necessitating direct access to individual-level control data. Our approach to UNICORN uses existing genetic resources and various statistical tools to analyze these data, including hierarchical clustering with spectral analysis of ancestry; and empirical Bayesian analysis along with Gaussian spatial processes to estimate ancestry-specific allele frequencies. We demonstrate our approach using tens of thousands of control subjects from studies of Crohn disease, showing how it controls false positives, provides power similar to that achieved when all control data are directly accessible, and enhances power when control data are limiting or even imperfectly matched ancestrally. These results highlight how UNICORN can enable reliable, powerful, and convenient genetic association analyses without access to the individual-level data.

In the version of this article initially published, the bibliographic information for reference 2 was incorrect in the reference list, and reference 2 was cited incorrectly at the end of the second sentence in the second paragraph (“...were identified2.”). The correct reference 2 is as follows: “Kong, A. et al. The nature of nurture: Effects of parental genotypes. Science 359, 424–428 (2018).” The reference that should be cited at the end of the aforementioned sentence, which should be numbered ‘5’ (“...were identified5.”), is as follows: “Okada, Y. et al. Genetics of rheumatoid arthritis contributes to biology and drug discovery. Nature 506, 376–381 (2014).” All subsequent references (5–161) should be renumbered accordingly (6–162) in the list and text. Also, several of the gene symbols in Table 2 were formatted incorrectly (without commas); the correct gene symbols are as follows: column 3 row 13, RBM17, IL2RA; column 3 row 30, DEXI, CLEC16A; column 3 row 39, UBASH3A, ICOSLG; column 4 row 15, PTEN, KLLN; column 4 row 21, CLEC7A, CLEC9A; and column 5 rows 7–9, AL391559.1, ENSG00000238747, RP11-63K6.7, RP3-512E2.2. The errors have been corrected in the HTML and PDF version of the article.

Primary immunodeficiency (PID) is characterised by recurrent and often life-threatening infections, autoimmunity and cancer, and it presents major diagnostic and therapeutic challenges. Although the most severe forms present in early childhood, the majority of patients present in adulthood, typically with no apparent family history and a variable clinical phenotype of widespread immune dysregulation: about 25% of patients have autoimmune disease, allergy is prevalent, and up to 10% develop lymphoid malignancies1–3. Consequently, in sporadic PID genetic diagnosis is difficult and the role of genetics is not well defined. We addressed these challenges by performing whole genome sequencing (WGS) of a large PID cohort of 1,318 participants. Analysis of coding regions of 886 index cases found disease-causing mutations in known monogenic PID genes in 10.3%, while a Bayesian approach (BeviMed4) identified multiple potential new candidate genes, including IVNS1ABP. Exploration of the non-coding genome revealed deletions in regulatory regions which contribute to disease causation. Finally, a genome-wide association study (GWAS) identified PID-associated loci and uncovered evidence for co-localisation of, and interplay between, novel high penetrance monogenic variants and common variants (at the PTPN2 and SOCS1 loci). This begins to explain the contribution of common variants to variable penetrance and phenotypic complexity in PID. Thus, a cohort-based WGS approach to PID diagnosis can increase diagnostic yield while deepening our understanding of the key pathways influencing human immune responsiveness.

Background Hi-C and capture Hi-C (CHi-C) are used to map physical contacts between chromatin regions in cell nuclei using high-throughput sequencing. Analysis typically proceeds considering the evidence for contacts between each possible pair of fragments independent from other pairs. This can produce long runs of fragments which appear to all make contact with the same baited fragment of interest. Results We hypothesised that these long runs could result from a smaller subset of direct contacts and propose a new method, based on a Bayesian sparse variable selection approach, which attempts to fine map these direct contacts. Our model is conceptually novel, exploiting the spatial pattern of counts in CHi-C data. Although we use only the CHi-C count data in fitting the model, we show that the fragments prioritised display biological properties that would be expected of true contacts: for bait fragments corresponding to gene promoters, we identify contact fragments with active chromatin and contacts that correspond to edges found in previously defined enhancer-target networks; conversely, for intergenic bait fragments, we identify contact fragments corresponding to promoters for genes expressed in that cell type. We show that long runs of apparently co-contacting fragments can typically be explained using a subset of direct contacts consisting of

Summary: Promoter capture Hi-C (PCHi-C) allows the genome-wide interrogation of physical interactions between distal DNA regulatory elements and gene promoters in multiple tissue contexts. Visual integration of the resultant chromosome interaction maps with other sources of genomic annotations can provide insight into underlying regulatory mechanisms. We have developed Capture HiC Plotter (CHiCP), a web-based tool that allows interactive exploration of PCHi-C interaction maps and integration with both public and user-defined genomic datasets. Availability and Implementation: CHiCP is freely accessible from www.chicp.org and supports most major HTML5 compliant web browsers. Full source code and installation instructions are available from http://github.com/D-I-L/django-chicp. Contact: ob219@cam.ac.uk

Genome-wide association studies are transformative in revealing the polygenetic basis of common diseases, with autoimmune diseases leading the charge. Although the field is just over 10 years old, advances in understanding the underlying mechanistic pathways of these conditions, which result from a dense multifactorial blend of genetic, developmental and environmental factors, have already been informative, including insights into therapeutic possibilities. Nevertheless, the challenge of identifying the actual causal genes and pathways and their biological effects on altering disease risk remains for many identified susceptibility regions. It is this fundamental knowledge that will underpin the revolution in patient stratification, the discovery of therapeutic targets and clinical trial design in the next 20 years. Here we outline recent advances in analytical and phenotyping approaches and the emergence of large cohorts with standardized gene-expression data and other phenotypic data that are fueling a bounty of discovery and improved understanding of human physiology.

Type 1 diabetes genotype datasets have undergone several well powered genome wide analysis studies (GWAS), identifying 57 associated regions at the time of analysis. There are still many regions of smaller effect size or low frequency left to discover, and better exploitation of existing type 1 diabetes cohorts with meta analysis and imputation can precede the acquisition of new or larger cohorts. An existing dataset of 5,913 case and 8,828 control samples was analysed using genome-wide microarrays (Affymetrix GeneChip 500K and Illumina Infinium 550K) with imputation via IMPUTE2 with the 1000 Genomes Project (phase 3) reference panel. Genotyping coverage was doubled in known association regions, and increased by four fold in other regions compared to previous studies. Our analysis resulted in new index variants for 17/57 regions, an expanded set of plausible candidate SNPs for 17 regions, and five novel type 1 diabetes association regions at 1p31.3, 1q24.3, 1q31.2, 2q11.2 and 11q12.2. Candidate genes for the new loci included ITGB3BP, FASLG, RGS1, AFF3 and CD5/CD6. Further prioritisation of causal genes and causal variants will require detailed RNA and protein expression studies, in conjunction with genome annotation studies including analysis of physical promoter-enhancer interactions.

BackgroundAutoimmune disease-associated variants are preferentially found in regulatory regions in immune cells, particularly CD4+ T cells. Linking such regulatory regions to gene promoters in disease-relevant cell contexts facilitates identification of candidate disease genes.ResultsWithin four hours, activation of CD4+ T cells invokes changes in histone modifications and enhancer RNA transcription that correspond to altered expression of the interacting genes identified by promoter capture Hi-C. By integrating promoter capture Hi-C data with genetic associations for five autoimmune diseases we prioritised 245 candidate genes with a median distance from peak signal to prioritised gene of 153 kb. Just under half (108/245) prioritised genes related to activation-sensitive interactions. This included IL2RA, where allele-specific expression analyses were consistent with its interaction-mediated regulation, illustrating the utility of the approach.ConclusionsOur systematic experimental framework offers an alternative approach to candidate causal gene identification for variants with cell state-specific functional effects, with achievable sample sizes.

IL2RA is associated with multiple autoimmune diseases including type 1 diabetes (T1D). Higher expression of IL2RA mRNA and its protein product CD25 in T lymphocytes is associated with a T1D-protective haplotype. Here we show that a rare variation of this haplotype that loses the protective allele at a single SNP, rs61839660, reduces IL2RA expression and T1D protection, identifying it as the causal factor in disease.

Copy number variants (CNVs) account for a major proportion of human genetic polymorphism and have been predicted to have an important role in genetic susceptibility to common disease. To address this we undertook a large, direct genome-wide study of association between CNVs and eight common human diseases. Using a purpose-designed array we typed 19,000 individuals into distinct copy-number classes at 3,432 polymorphic CNVs, including an estimated 50% of all common CNVs larger than 500 base pairs. We identified several biological artefacts that lead to false-positive associations, including systematic CNV differences between DNAs derived from blood and cell lines. Association testing and follow-up replication analyses confirmed three loci where CNVs were associated with diseaseIRGM for Crohns disease, HLA for Crohns disease, rheumatoid arthritis and type 1 diabetes, and TSPAN8 for type 2 diabetesalthough in each case the locus had previously been identified in single nucleotide polymorphism (SNP)-based studies, reflecting our observation that most common CNVs that are well-typed on our array are well tagged by SNPs and so have been indirectly explored through SNP studies. We conclude that common CNVs that can be typed on existing platforms are unlikely to contribute greatly to the genetic basis of common human diseases. © 2010 Macmillan Publishers Limited. All rights reserved.

Summary Long-range interactions between regulatory elements and gene promoters play key roles in transcriptional regulation. The vast majority of interactions are uncharted, constituting a major missing link in understanding genome control. Here, we use promoter capture Hi-C to identify interacting regions of 31,253 promoters in 17 human primary hematopoietic cell types. We show that promoter interactions are highly cell type specific and enriched for links between active promoters and epigenetically marked enhancers. Promoter interactomes reflect lineage relationships of the hematopoietic tree, consistent with dynamic remodeling of nuclear architecture during differentiation. Interacting regions are enriched in genetic variants linked with altered expression of genes they contact, highlighting their functional role. We exploit this rich resource to connect non-coding disease variants to putative target promoters, prioritizing thousands of disease-candidate genes and implicating disease pathways. Our results demonstrate the power of primary cell promoter interactomes to reveal insights into genomic regulatory mechanisms underlying common diseases., Graphical Abstract, Highlights • High-resolution maps of promoter interactions in 17 human primary blood cell types • Interaction patterns are cell type specific and segregate with the hematopoietic tree • Promoter-interacting regions enriched for regulatory chromatin features and eQTLs • Promoter interactions link non-coding GWAS variants with putative target genes, This study deploys a promoter capture Hi-C approach in 17 primary blood cell types to match collaborating regulatory regions and identify genes regulated by noncoding disease-associated variants. Explore this and other papers at the Cell Press IHEC webportal at http://www.cell.com/consortium/IHEC.

OBJECTIVE Vitamin D deficiency (25-hydroxyvitamin D [25(OH)D] RESEARCH DESIGN AND METHODS We measured 25(OH)D concentrations in 720 case and 2,610 control plasma samples and genotyped single nucleotide polymorphisms from seven vitamin D metabolism genes in 8,517 case, 10,438 control, and 1,933 family samples. We tested genetic variants influencing 25(OH)D metabolism for an association with both circulating 25(OH)D concentrations and disease status. RESULTS Type 1 diabetic patients have lower circulating levels of 25(OH)D than similarly aged subjects from the British population. Only 4.3 and 18.6% of type 1 diabetic patients reached optimal levels (≥75 nmol/L) of 25(OH)D for bone health in the winter and summer, respectively. We replicated the associations of four vitamin D metabolism genes (GC, DHCR7, CYP2R1, and CYP24A1) with 25(OH)D in control subjects. In addition to the previously reported association between type 1 diabetes and CYP27B1 (P = 1.4 × 10−4), we obtained consistent evidence of type 1 diabetes being associated with DHCR7 (P = 1.2 × 10−3) and CYP2R1 (P = 3.0 × 10−3). CONCLUSIONS Circulating levels of 25(OH)D in children and adolescents with type 1 diabetes vary seasonally and are under the same genetic control as in the general population but are much lower. Three key 25(OH)D metabolism genes show consistent evidence of association with type 1 diabetes risk, indicating a genetic etiological role for vitamin D deficiency in type 1 diabetes.

Identifying whether potential causal variants for related diseases are shared can increase understanding of the shared etiology between diseases. Colocalization methods are designed to disentangle shared and distinct causal variants in regions where two diseases show association, but existing methods are limited by assuming independent datasets. We extended existing methods to allow for the shared control design common in GWAS and applied them to four autoimmune diseases: type 1 diabetes (T1D); rheumatoid arthritis; celiac disease (CEL) and multiple sclerosis (MS). Ninety regions associated with at least one disease. In 22 regions (24%), we identify association to precisely one of our four diseases and can find no published association of any other disease to the same region; some of these may reflect effects mediated by the target of immune attack. Thirty-three regions (37%) were associated with two or more, but in 14 of these there was evidence that causal variants differed between diseases. By leveraging information across datasets, we identified novel disease associations to 12 regions previously associated with one or more of the other three autoimmune disorders. For instance, we link the CEL-associatedFASLGregion to T1D and identify a single SNP, rs78037977, as a likely causal variant. We also highlight several particularly complex association patterns, including theCD28-CTLA4-ICOSregion, in which it appears that three distinct causal variants associate with three diseases in three different patterns. Our results underscore the complexity in genetic variation underlying related but distinct autoimmune diseases and help to approach its dissection.

Identification of candidate causal variants in regions associated with risk of common diseases is complicated by linkage disequilibrium (LD) and multiple association signals. Nonetheless, accurate maps of these variants are needed, both to fully exploit detailed cell specific chromatin annotation data to highlight disease causal mechanisms and cells, and for design of the functional studies that will ultimately be required to confirm causal mechanisms. We adapted a Bayesian evolutionary stochastic search algorithm to the fine mapping problem, and demonstrated its improved performance over conventional stepwise and regularised regression through simulation studies. We then applied it to fine map the established multiple sclerosis (MS) and type 1 diabetes (T1D) associations in the IL-2RA (CD25) gene region. For T1D, both stepwise and stochastic search approaches identified four T1D association signals, with the major effect tagged by the single nucleotide polymorphism, rs12722496. In contrast, for MS, the stochastic search found two distinct competing models: a single candidate causal variant, tagged by rs2104286 and reported previously using stepwise analysis; and a more complex model with two association signals, one of which was tagged by the major T1D associated rs12722496 and the other by rs56382813. There is low to moderate LD between rs2104286 and both rs12722496 and rs56382813 (r2 ≃ 0:3) and our two SNP model could not be recovered through a forward stepwise search after conditioning on rs2104286. Both signals in the two variant model for MS affect CD25 expression on distinct subpopulations of CD4+ T cells, which are key cells in the autoimmune process. The results support a shared causal variant for T1D and MS. Our study illustrates the benefit of using a purposely designed model search strategy for fine mapping and the advantage of combining disease and protein expression data., Author Summary Genetic association studies have identified many DNA sequence variants that associate with disease risk. By exploiting the known correlation that exists between neighbouring variants in the genome, inference can be extended beyond those individual variants tested to identify sets within which a causal variant is likely to reside. However, this correlation, particularly in the presence of multiple disease causing variants in relative proximity, makes disentangling the specific causal variants difficult. Statistical approaches to this fine mapping problem have traditionally taken a stepwise search approach, beginning with the most associated variant in a region, then iteratively attempting to find additional associated variants. We adapted a stochastic search approach that avoids this stepwise process and is explicitly designed for dealing with highly correlated predictors to the fine mapping problem. We showed in simulated data that it outperforms its stepwise counterpart and other variable selection strategies such as the lasso. We applied our approach to understand the association of two immune-mediated diseases to a region on chromosome 10p15. We identified a model for multiple sclerosis containing two variants, neither of which was found through a stepwise search, and functionally linked both of these to the neighbouring candidate gene, IL2RA, in independent data. Our approach can be used to aid fine mapping of other disease-associated regions, which is critical for design of functional follow-up studies required to understand the mechanisms through which genetic variants influence disease.

Susceptibility to the autoimmune disease type 1 diabetes has been linked to human chromosome 6q27 and, moreover, recently associated with one of the genes in the region, TATA box-binding protein (TBP). Using a much larger sample of T1D families than those studied by others, and by extensive re-sequencing of nine other genes in the proximity, in which we identified 279 polymorphisms, 83 of which were genotyped in up to 725 T1D multiplex and simplex families, we obtained no evidence for association of the TBP CAG/CAA (glutamine) microsatellite repeat sequence with disease, or for nine other genes, PDCD2, PSMB1, KIAA1838, DLL1, dJ894D12.4, FLJ25454, FLJ13162, FLJ11152, PHF10 and CCR6. This study also provides an exon-based tag single nucleotide polymorphism map for these 10 genes that can be used for analysis of other diseases.

T1DBase (http://T1DBase.org) is a public website and database that supports the type 1 diabetes (T1D) research community. The site is currently focused on the molecular genetics and biology of T1D susceptibility and pathogenesis. It includes the following datasets: annotated genome sequence for human, rat and mouse; information on genetically identified T1D susceptibility regions in human, rat and mouse, and genetic linkage and association studies pertaining to T1D; descriptions of NOD mouse congenic strains; the Beta Cell Gene Expression Bank, which reports expression levels of genes in beta cells under various conditions, and annotations of gene function in beta cells; data on gene expression in a variety of tissues and organs; and biological pathways from KEGG and BioCarta. Tools on the site include the GBrowse genome browser, site-wide context dependent search, Connect-the-Dots for connecting gene and other identifiers from multiple data sources, Cytoscape for visualizing and analyzing biological networks, and the GESTALT workbench for genome annotation. All data are open access and all software is open source.

Variations in the interleukin 4 receptor A (IL4RA) gene have been reported to be associated with atopy, asthma, and allergy, which may occur less frequently in subjects with type 1 diabetes (T1D). Since atopy shows a humoral immune reactivity pattern, and T1D results from a cellular (T lymphocyte) response, we hypothesised that alleles predisposing to atopy could be protective for T1D and transmitted less often than the expected 50% from heterozygous parents to offspring with T1D. We genotyped seven exonic single nucleotide polymorphisms (SNPs) and the -3223 C>T SNP in the putative promoter region of IL4RA in up to 3475 T1D families, including 1244 Finnish T1D families. Only the -3223 C>T SNP showed evidence of negative association (P=0.014). There was some evidence for an interaction between -3233 C>T and the T1D locus IDDM2 in the insulin gene region (P=0.001 in the combined and P=0.02 in the Finnish data set). We, therefore, cannot rule out a genetic effect of IL4RA in T1D, but it is not a major one.

Diagnosis of the autoimmune disease type 1 diabetes (T1D) is preceded by the appearance of circulating autoantibodies to pancreatic islets. However, almost nothing is known about events leading to this islet autoimmunity. Previous epidemiological and genetic data have associated viral infections and antiviral type I interferon (IFN) immune response genes with T1D. Here, we first used DNA microarray analysis to identify IFN-β–inducible genes in vitro and then used this set of genes to define an IFN-inducible transcriptional signature in peripheral blood mononuclear cells from a group of active systemic lupus erythematosus patients (n = 25). Using this predefined set of 225 IFN signature genes, we investigated the expression of the signature in cohorts of healthy controls (n = 87), patients with T1D (n = 64), and a large longitudinal birth cohort of children genetically predisposed to T1D (n = 109; 454 microarrayed samples). Expression of the IFN signature was increased in genetically predisposed children before the development of autoantibodies (P = 0.0012) but not in patients with established T1D. Upregulation of IFN-inducible genes was transient, temporally associated with a recent history of upper respiratory tract infections (P = 0.0064), and marked by increased expression of SIGLEC-1 (CD169), a lectin-like receptor expressed on CD14+ monocytes. DNA variation in IFN-inducible genes altered T1D risk (P = 0.007), as exemplified by IFIH1, one of the genes in our IFN signature for which increased expression is a known risk factor for disease. These findings identify transient increased expression of type I IFN genes in preclinical diabetes as a risk factor for autoimmunity in children with a genetic predisposition to T1D.

Motivation: Genome-wide association studies (GWAS) have identified many loci implicated in disease susceptibility. Integration of GWAS summary statistics (P-values) and functional genomic datasets should help to elucidate mechanisms. Results: We extended a non-parametric SNP set enrichment method to test for enrichment of GWAS signals in functionally defined loci to a situation where only GWAS P-values are available. The approach is implemented in VSEAMS, a freely available software pipeline. We use VSEAMS to identify enrichment of type 1 diabetes (T1D) GWAS associations near genes that are targets for the transcription factors IKZF3, BATF and ESRRA. IKZF3 lies in a known T1D susceptibility region, while BATF and ESRRA overlap other immune disease susceptibility regions, validating our approach and suggesting novel avenues of research for T1D. Availability and implementation: VSEAMS is available for download (http://github.com/ollyburren/vseams). Contact: chris.wallace@cimr.cam.ac.uk Supplementary information: Supplementary data are available at Bioinformatics online.

Background Killer Immunoglobulin-like Receptors (KIRs) are surface receptors of natural killer cells that bind to their corresponding Human Leukocyte Antigen (HLA) class I ligands, making them interesting candidate genes for HLA-associated autoimmune diseases, including type 1 diabetes (T1D). However, allelic and copy number variation in the KIR region effectively mask it from standard genome-wide association studies: single nucleotide polymorphism (SNP) probes targeting the region are often discarded by standard genotype callers since they exhibit variable cluster numbers. Quantitative Polymerase Chain Reaction (qPCR) assays address this issue. However, their cost is prohibitive at the sample sizes required for detecting effects typically observed in complex genetic diseases. Results We propose a more powerful and cost-effective alternative, which combines signals from SNPs with more than three clusters found in existing datasets, with qPCR on a subset of samples. First, we showed that noise and batch effects in multiplexed qPCR assays are addressed through normalisation and simultaneous copy number calling of multiple genes. Then, we used supervised classification to impute copy numbers of specific KIR genes from SNP signals. We applied this method to assess copy number variation in two KIR genes, KIR3DL1 and KIR3DS1, which are suitable candidates for T1D susceptibility since they encode the only KIR molecules known to bind with HLA-Bw4 epitopes. We find no association between KIR3DL1/3DS1 copy number and T1D in 6744 cases and 5362 controls; a sample size twenty-fold larger than in any previous KIR association study. Due to our sample size, we can exclude odds ratios larger than 1.1 for the common KIR3DL1/3DS1 copy number groups at the 5% significance level. Conclusion We found no evidence of association of KIR3DL1/3DS1 copy number with T1D, either overall or dependent on HLA-Bw4 epitope. Five other KIR genes, KIR2DS4, KIR2DL3, KIR2DL5, KIR2DS5 and KIR2DS1, in high linkage disequilibrium with KIR3DL1 and KIR3DS1, are also unlikely to be significantly associated. Our approach could potentially be applied to other KIR genes to allow cost effective assaying of gene copy number in large samples.

A search for variants in coding exons of 25 genome-wide association study risk genes in a large cohort of autoimmune patients finds that rare coding-region variants at known loci have a negligible role in common autoimmune disease susceptibility, arguing against the previously proposed rare-variant synthetic genome-wide association hypothesis. Although many common variants of modest-effect size have been identified in genome-wide association studies (GWAS), much of the heritability of complex traits remains unexplained. These authors looked for variants in coding exons of 25 GWAS risk genes in a large cohort of subjects with six autoimmune disease phenotypes and controls, and show that rare coding-region variants at known loci have at most a minor role in common autoimmune disease susceptibility. These results do not support the theory that the missing heritability for common autoimmune diseases is attributable to rare coding mutations at known loci, but are consistent with disease caused by many common-variant loci of weak effect. Genome-wide association studies (GWAS) have identified common variants of modest-effect size at hundreds of loci for common autoimmune diseases; however, a substantial fraction of heritability remains unexplained, to which rare variants may contribute1,2. To discover rare variants and test them for association with a phenotype, most studies re-sequence a small initial sample size and then genotype the discovered variants in a larger sample set3,4,5. This approach fails to analyse a large fraction of the rare variants present in the entire sample set. Here we perform simultaneous amplicon-sequencing-based variant discovery and genotyping for coding exons of 25 GWAS risk genes in 41,911 UK residents of white European origin, comprising 24,892 subjects with six autoimmune disease phenotypes and 17,019 controls, and show that rare coding-region variants at known loci have a negligible role in common autoimmune disease susceptibility. These results do not support the rare-variant synthetic genome-wide-association hypothesis6 (in which unobserved rare causal variants lead to association detected at common tag variants). Many known autoimmune disease risk loci contain multiple, independently associated, common and low-frequency variants, and so genes at these loci are a priori stronger candidates for harbouring rare coding-region variants than other genes. Our data indicate that the missing heritability for common autoimmune diseases may not be attributable to the rare coding-region variant portion of the allelic spectrum, but perhaps, as others have proposed, may be a result of many common-variant loci of weak effect7,8,9,10.

We have previously proposed that sequence variation of the CD101 gene between NOD and C57BL/6 mice accounts for the protection from type 1 diabetes (T1D) provided by the insulin-dependent diabetes susceptibility region 10 (Idd10), a

T1DBase (http://www.t1dbase.org) is web platform, which supports the type 1 diabetes (T1D) community. It integrates genetic, genomic and expression data relevant to T1D research across mouse, rat and human and presents this to the user as a set of web pages and tools. This update describes the incorporation of new data sets, tools and curation efforts as well as a new website design to simplify site use. New data sets include curated summary data from four genome-wide association studies relevant to T1D, HaemAtlas-a data set and tool to query gene expression levels in haematopoietic cells and a manually curated table of human T1D susceptibility loci, incorporating genetic overlap with other related diseases. These developments will continue to support T1D research and allow easy access to large and complex T1D relevant data sets.

Human genome wide association studies (GWAS) have recently identified at least four new, non-MHC-linked candidate genes or gene regions causing type one diabetes (T1D), highlighting the need for functional models to investigate how susceptibility alleles at multiple common genes interact to mediate disease. Progress in localizing genes in congenic strains of the nonobese diabetic (NOD) mouse has allowed the reproducible testing of gene functions and gene-gene interactions that can be reflected biologically as intrapathway interactions, for example, IL-2 and its receptor CD25, pathway-pathway interactions such as two signaling pathways within a cell, or cell-cell interactions. Recent studies have identified likely causal genes in two congenic intervals associated with T1D, Idd3, and Idd5, and have documented the occurrence of gene-gene interactions, including "genetic masking", involving the genes encoding the critical immune molecules IL-2 and CTLA-4. The demonstration of gene-gene interactions in congenic mouse models of T1D has major implications for the understanding of human T1D since such biological interactions are highly likely to exist for human T1D genes. Although it is difficult to detect most gene-gene interactions in a population in which susceptibility and protective alleles at many loci are randomly segregating, their existence as revealed in congenic mice reinforces the hypothesis that T1D alleles can have strong biological effects and that such genes highlight pathways to consider as targets for immune intervention.

Human genome wide association studies (GWAS) have recently identified at least four new, non-MHC-linked candidate genes or gene regions causing type one diabetes (T1D), highlighting the need for functional models to investigate how susceptibility alleles at multiple common genes interact to mediate disease. Progress in localizing genes in congenic strains of the nonobese diabetic (NOD) mouse has allowed the reproducible testing of gene functions and gene–gene interactions that can be reflected biologically as intrapathway interactions, for example, IL-2 and its receptor CD25, pathway-pathway interactions such as two signaling pathways within a cell, or cell-cell interactions. Recent studies have identified likely causal genes in two congenic intervals associated with T1D, Idd3 , and Idd5 , and have documented the occurrence of gene–gene interactions, including “genetic masking”, involving the genes encoding the critical immune molecules IL-2 and CTLA-4. The demonstration of gene–gene interactions in congenic mouse models of T1D has major implications for the understanding of human T1D since such biological interactions are highly likely to exist for human T1D genes. Although it is difficult to detect most gene–gene interactions in a population in which susceptibility and protective alleles at many loci are randomly segregating, their existence as revealed in congenic mice reinforces the hypothesis that T1D alleles can have strong biological effects and that such genes highlight pathways to consider as targets for immune intervention.

The Wellcome Trust Case Control Consortium (WTCCC) primary genome-wide association (GWA) scan1 on seven diseases, including the multifactorial, autoimmune disease, type 1 diabetes (T1D), shows significant association (P < 5 × 10−7 between T1D and six chromosome regions: 12q24, 12q13, 16p13, 18p11, 12p13 and 4q27. Here, we attempted to validate these and six other top findings in 4,000 individuals with T1D, 5,000 controls and 2,997 family trios that were independent of the WTCCC study. We confirmed unequivocally the associations of 12q24, 12q13, 16p13 and 18p11 (Pfollow-up ≤ 1.35 × 10−9; Poverall ≤ 1.15 × 10−14), leaving eight regions with small effects or false-positive associations with T1D. We also obtained evidence for chromosome 18q22 (Poverall = 1.38 × 10−8) from a genome-wide association study of nonsynonymous SNPs. Several regions, including 18q22 and 18p11, showed association with autoimmune thyroid disease. This study increases the number of T1D loci with compelling evidence from six to at least ten.

Nat. Genet. 47, 839–846 (2015); published online 8 June 2015; corrected after print 8 July 2015 In the version of this article initially published, the two panels in Figure 2 were presented in the incorrect order. The error has been corrected in the HTML and PDF versions of the article.

BackgroundType 1 diabetes (T1D) is a common autoimmune disease resulting from T-cell mediated destruction of pancreatic beta cells. Decay accelerating factor (DAF, CD55), a glycosylphosphatidylinositol-anchored membrane protein, is a candidate for autoimmune disease susceptibility based on its role in restricting complement activation and evidence that DAF expression modulates the phenotype of mice models for autoimmune disease. In this study, we adopt a linkage disequilibrium (LD) mapping approach to test for an association between the DAF gene and T1D.ResultsInitially, we used HapMap II genotype data to examine LD across theDAFregion. Additional resequencing was required, identifying 16 novel polymorphisms. Combining both datasets, a LD mapping approach was adopted to test for association with T1D. Seven tag SNPs were selected and genotyped in case-control (3,523 cases and 3,817 controls) and family (725 families) collections.ConclusionWe obtained no evidence of association between T1D and theDAFregion in two independent collections. In addition, we assessed the impact of using only HapMap II genotypes for the selection of tag SNPs and, based on this study, found that HapMap II genotypes may require additional SNP discovery for comprehensive LD mapping of some genes in common disease.

In this study we report convincing statistical support for a sixth type 1 diabetes (T1D) locus in the innate immunity viral RNA receptor gene region IFIH1 (also known as mda-5 or Helicard) on chromosome 2q24.3. We found the association in an interim analysis of a genome-wide nonsynonymous SNP (nsSNP) scan, and we validated it in a case-control collection and replicated it in an independent family collection. In 4,253 cases, 5,842 controls and 2,134 parent-child trio genotypes, the risk ratio for the minor allele of the nsSNP rs1990760 A --G (A946T) was 0.86 (95% confidence interval = 0.82-0.90) at P = 1.42 x 10(-10).