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.

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...

Genetic mutations cause primary immunodeficiencies (PIDs), which predispose to infections. Here we describe Activated PI3K-δ Syndrome (APDS), a PID associated with a dominant gain-offunction mutation E1021K in the p110δ protein, the catalytic subunit of phosphoinositide 3-kinase δ (PI3Kδ), encoded by the PIK3CD gene. We found E1021K in 17 patients from seven unrelated

BackgroundActivated phosphoinositide 3-kinase δ syndrome (APDS) is a recently described combined immunodeficiency resulting from gain-of-function mutations in PIK3CD, the gene encoding the catalytic subunit of phosphoinositide 3-kinase δ (PI3Kδ).ObjectiveWe sought to review the clinical, immunologic, histopathologic, and radiologic features of APDS in a large genetically defined international cohort.MethodsWe applied a clinical questionnaire and performed review of medical notes, radiology, histopathology, and laboratory investigations of 53 patients with APDS.ResultsRecurrent sinopulmonary infections (98%) and nonneoplastic lymphoproliferation (75%) were common, often from childhood. Other significant complications included herpesvirus infections (49%), autoinflammatory disease (34%), and lymphoma (13%). Unexpectedly, neurodevelopmental delay occurred in 19% of the cohort, suggesting a role for PI3Kδ in the central nervous system; consistent with this, PI3Kδ is broadly expressed in the developing murine central nervous system. Thoracic imaging revealed high rates of mosaic attenuation (90%) and bronchiectasis (60%). Increased IgM levels (78%), IgG deficiency (43%), and CD4 lymphopenia (84%) were significant immunologic features. No immunologic marker reliably predicted clinical severity, which ranged from asymptomatic to death in early childhood. The majority of patients received immunoglobulin replacement and antibiotic prophylaxis, and 5 patients underwent hematopoietic stem cell transplantation. Five patients died from complications of APDS.ConclusionAPDS is a combined immunodeficiency with multiple clinical manifestations, many with incomplete penetrance and others with variable expressivity. The severity of complications in some patients supports consideration of hematopoietic stem cell transplantation for severe childhood disease. Clinical trials of selective PI3Kδ inhibitors offer new prospects for APDS treatment.

SNP heritability, the proportion of phenotypic variance explained by SNPs, has been reported for many hundreds of traits. Its estimation requires strong prior assumptions about the distribution of heritability across the genome, but the assumptions in current use have not been thoroughly tested. By analyzing imputed data for a large number of human traits, we empirically derive a model that more accurately describes how heritability varies with minor allele frequency, linkage disequilibrium and genotype certainty. Across 19 traits, our improved model leads to estimates of common SNP heritability on average 43% (standard deviation 3) higher than those obtained from the widely-used software GCTA, and 25% (standard deviation 2) higher than those from the recently-proposed extension GCTA-LDMS. Previously, DNaseI hypersensitivity sites were reported to explain 79% of SNP heritability; using our improved heritability model their estimated contribution is only 24%.

Motivation: Exome sequencing has proven to be an effective tool to discover the genetic basis of Mendelian disorders. It is well established that copy number variants (CNVs) contribute to the etiology of these disorders. However, calling CNVs from exome sequence data is challenging. A typical read depth strategy consists of using another sample (or a combination of samples) as a reference to control for the variability at the capture and sequencing steps. However, technical variability between samples complicates the analysis and can create spurious CNV calls.Results: Here, we introduce ExomeDepth, a new CNV calling algorithm designed to control for this technical variability. ExomeDepth uses a robust model for the read count data and uses this model to build an optimized reference set in order to maximize the power to detect CNVs. As a result, ExomeDepth is effective across a wider range of exome datasets than the previously existing tools, even for small (e.g. one to two exons) and heterozygous deletions. We used this new approach to analyse exome data from 24 patients with primary immunodeficiencies. Depending on data quality and the exact target region, we find between 170 and 250 exonic CNV calls per sample. Our analysis identified two novel causative deletions in the genes GATA2 and DOCK8.Availability: The code used in this analysis has been implemented into an R package called ExomeDepth and is available at the Comprehensive R Archive Network (CRAN).Contact: v.plagnol@ucl.ac.ukSupplementary Information: Supplementary data are available at Bioinformatics online.

The molecular mechanisms determining transmissibility and prevalence of drug-resistant tuberculosis in a population were investigated through whole genome sequencing of 1,000 prospectively-obtained patient isolates from Russia. Two-thirds belonged to the Beijing lineage, which was dominated by two homogeneous clades. MDR genotypes were found in 48% of isolates overall and 87% of the major clades. The most common rifampicin-resistance rpoB mutation was associated with fitness-compensatory mutations in rpoA or rpoC, and a novel intragenic compensatory substitution was identified. The proportion of MDR cases with XDR-tuberculosis was 16% overall with 65% of MDR isolates harboring eis mutations, selected by kanamycin therapy, which may drive the expansion of strains with enhanced virulence. The combination of drug resistance and compensatory mutations displayed by the major clades confer clinical resistance without compromising fitness and transmissibility, revealing a biological contribution to the tuberculosis program weaknesses driving the persistence and spread of M/XDR-tuberculosis in Russia and beyond.

The major histocompatibility complex (MHC) on chromosome 6 is associated with susceptibility to more common diseases than any other region of the human genome, including almost all disorders classified as autoimmune. In type 1 diabetes the major genetic susceptibility determinants have been mapped to the MHC class II genes HLA-DQB1 and HLA-DRB1 (refs 1-3), but these genes cannot completely explain the association between type 1 diabetes and the MHC region 4-11 . Owing to the region's extreme gene density, the multiplicity of diseaseassociated alleles, strong associations between alleles, limited genotyping capability, and inadequate statistical approaches and sample sizes, which, and how many, loci within the MHC determine susceptibility remains unclear. Here, in several large type 1 diabetes data sets, we analyse a combined total of 1,729 polymorphisms, and apply statistical methods-recursive partitioning and regression-to pinpoint disease susceptibility to the MHC class I genes HLA-B and HLA-A (risk ratios>1.5; P combined =2.01×10 -19 and 2.35×10 -13 , respectively) in addition to the established associations of the MHC class II genes. Other loci with smaller and/or rarer effects might also be involved, but to find these, future searches must take into account both the HLA class II and class I genes and use even larger samples. Taken together with previous