Vincent Plagnol

Robust associations of four new chromosome regions from genome-wide analyses of type 1 diabetes

The Wellcome Trust Case Control Consortium (WTCCC) primary genome-wide association (GWA) scan on seven diseases, including the multifactorial autoimmune disease type 1 diabetes (T1D), shows associations at 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 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. We also obtained evidence for chromosome 18q22 (Poverall = 1.38 × 10−8) from a GWA 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 study and meta-analysis find that over 40 loci affect risk of type 1 diabetes

Type 1 diabetes (T1D) is a common autoimmune disorder that arises from the action of multiple genetic and environmental risk factors. We report the findings of a genome-wide association study of T1D, combined in a meta-analysis with two previously published studies. The total sample set included 7,514 cases and 9,045 reference samples. Forty-one distinct genomic locations provided evidence for association with T1D in the meta-analysis (P < 10−6). After excluding previously reported associations, we further tested 27 regions in an independent set of 4,267 cases, 4,463 controls and 2,319 affected sib-pair (ASP) families. Of these, 18 regions were replicated (P < 0.01; overall P < 5 × 10−8) and 4 additional regions provided nominal evidence of replication (P < 0.05). The many new candidate genes suggested by these results include IL10, IL19, IL20, GLIS3, CD69 and IL27.

Markov chain Monte Carlo without likelihoods

Many stochastic simulation approaches for generating observations from a posterior distribution depend on knowing a likelihood function. However, for many complex probability models, such likelihoods are either impossible or computationally prohibitive to obtain. Here we present a Markov chain Monte Carlo method for generating observations from a posterior distribution without the use of likelihoods. It can also be used in frequentist applications, in particular for maximum-likelihood estimation. The approach is illustrated by an example of ancestral inference in population genetics. A number of open problems are highlighted in the discussion.

Shared and Distinct Genetic Variants in Type 1 Diabetes and Celiac Disease

BACKGROUND Two inflammatory disorders, type 1 diabetes and celiac disease, cosegregate in populations, suggesting a common genetic origin. Since both diseases are associated with the HLA class II genes on chromosome 6p21, we tested whether non-HLA loci are shared. METHODS We evaluated the association between type 1 diabetes and eight loci related to the risk of celiac disease by genotyping and statistical analyses of DNA samples from 8064 patients with type 1 diabetes, 9339 control subjects, and 2828 families providing 3064 parent-child trios (consisting of an affected child and both biologic parents). We also investigated 18 loci associated with type 1 diabetes in 2560 patients with celiac disease and 9339 control subjects. RESULTS Three celiac disease loci--RGS1 on chromosome 1q31, IL18RAP on chromosome 2q12, and TAGAP on chromosome 6q25--were associated with type 1 diabetes (P<1.00x10(-4)). The 32-bp insertion-deletion variant on chromosome 3p21 was newly identified as a type 1 diabetes locus (P=1.81x10(-8)) and was also associated with celiac disease, along with PTPN2 on chromosome 18p11 and CTLA4 on chromosome 2q33, bringing the total number of loci with evidence of a shared association to seven, including SH2B3 on chromosome 12q24. The effects of the IL18RAP and TAGAP alleles confer protection in type 1 diabetes and susceptibility in celiac disease. Loci with distinct effects in the two diseases included INS on chromosome 11p15, IL2RA on chromosome 10p15, and PTPN22 on chromosome 1p13 in type 1 diabetes and IL12A on 3q25 and LPP on 3q28 in celiac disease. CONCLUSIONS A genetic susceptibility to both type 1 diabetes and celiac disease shares common alleles. These data suggest that common biologic mechanisms, such as autoimmunity-related tissue damage and intolerance to dietary antigens, may be etiologic features of both diseases.

Dense genotyping identifies and localizes multiple common and rare variant association signals in celiac disease.

Using variants from the 1000 Genomes Project pilot European CEU dataset and data from additional resequencing studies, we densely genotyped 183 non-HLA risk loci previously associated with immune-mediated diseases in 12,041 individuals with celiac disease (cases) and 12,228 controls. We identified 13 new celiac disease risk loci reaching genome-wide significance, bringing the number of known loci (including the HLA locus) to 40. We found multiple independent association signals at over one-third of these loci, a finding that is attributable to a combination of common, low-frequency and rare genetic variants. Compared to previously available data such as those from HapMap3, our dense genotyping in a large sample collection provided a higher resolution of the pattern of linkage disequilibrium and suggested localization of many signals to finer scale regions. In particular, 29 of the 54 fine-mapped signals seemed to be localized to single genes and, in some instances, to gene regulatory elements. Altogether, we define the complex genetic architecture of the risk regions of and refine the risk signals for celiac disease, providing the next step toward uncovering the causal mechanisms of the disease.

Meta-analysis of genome-wide association study data identifies additional type 1 diabetes risk loci

We carried out a meta-analysis of data from three genome-wide association (GWA) studies of type 1 diabetes (T1D), testing 305,090 SNPs in 3,561 T1D cases and 4,646 controls of European ancestry. We obtained further support for 4q27 (IL2-IL21, P = 1.9 × 10−8) and, after genotyping an additional 6,225 cases, 6,946 controls and 2,828 families, convincing evidence for four previously unknown and distinct risk loci in chromosome regions 6q15 (BACH2, P = 4.7 × 10−12), 10p15 (PRKCQ, P = 3.7 × 10−9), 15q24 (CTSH, P = 3.2 × 10−15) and 22q13 (C1QTNF6, P = 2.0 × 10−8).

Large-scale genetic fine mapping and genotype-phenotype associations implicate polymorphism in the IL2RA region in type 1 diabetes

Genome-wide association studies are now identifying disease-associated chromosome regions. However, even after convincing replication, the localization of the causal variant(s) requires comprehensive resequencing, extensive genotyping and statistical analyses in large sample sets leading to targeted functional studies. Here, we have localized the type 1 diabetes (T1D) association in the interleukin 2 receptor alpha (IL2RA) gene region to two independent groups of SNPs, spanning overlapping regions of 14 and 40 kb, encompassing IL2RA intron 1 and the 5′ regions of IL2RA and RBM17 (odds ratio = 2.04, 95% confidence interval = 1.70–2.45; P = 1.92 × 10−28; control frequency = 0.635). Furthermore, we have associated IL2RA T1D susceptibility genotypes with lower circulating levels of the biomarker, soluble IL-2RA (P = 6.28 × 10−28), suggesting that an inherited lower immune responsiveness predisposes to T1D.

Phosphoinositide 3-Kinase δ Gene Mutation Predisposes to Respiratory Infection and Airway Damage

Answers from Exomes Exome sequencing, which targets only the protein-coding regions of the genome, has the potential to identify the underlying genetic causes of rare inherited diseases. Angulo et al. (p. 866, published online 17 October; see Perspective by Conley and Fruman) performed exome sequencing of individuals from seven unrelated families with severe, recurrent respiratory infections. The patients carried the same mutation in the gene coding for the catalytic subunit of phosphoinositide 3-kinase δ (PI3Kδ). The mutation caused aberrant activation of this kinase, which plays a key role in immune cell signaling. Drugs inhibiting PI3Kδ are already in clinical trials for other disorders. Gene sequencing of unrelated patients with recurrent airway infections identifies a common underlying mutation. [Also see Perspective by Conley and Fruman] Genetic mutations cause primary immunodeficiencies (PIDs) that predispose to infections. Here, we describe activated PI3K-δ syndrome (APDS), a PID associated with a dominant gain-of-function mutation in which lysine replaced glutamic acid at residue 1021 (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 families, but not among 3346 healthy subjects. APDS was characterized by recurrent respiratory infections, progressive airway damage, lymphopenia, increased circulating transitional B cells, increased immunoglobulin M, and reduced immunoglobulin G2 levels in serum and impaired vaccine responses. The E1021K mutation enhanced membrane association and kinase activity of p110δ. Patient-derived lymphocytes had increased levels of phosphatidylinositol 3,4,5-trisphosphate and phosphorylated AKT protein and were prone to activation-induced cell death. Selective p110δ inhibitors IC87114 and GS-1101 reduced the activity of the mutant enzyme in vitro, which suggested a therapeutic approach for patients with APDS.

Cell-specific protein phenotypes for the autoimmune locus IL2RA using a genotype-selectable human bioresource

Genome-wide association studies (GWAS) have identified over 300 regions associated with more than 70 common diseases. However, identifying causal genes within an associated region remains a major challenge. One approach to resolving causal genes is through the dissection of gene-phenotype correlations. Here we use polychromatic flow cytometry to show that differences in surface expression of the human interleukin-2 (IL-2) receptor alpha (IL2RA, or CD25) protein are restricted to particular immune cell types and correlate with several haplotypes in the IL2RA region that have previously been associated with two autoimmune diseases, type 1 diabetes (T1D) and multiple sclerosis. We confirm our strongest gene-phenotype correlation at the RNA level by allele-specific expression (ASE). We also define key parameters for the design and implementation of post-GWAS gene-phenotype investigations and demonstrate the usefulness of a large bioresource of genotype-selectable normal donors from whom fresh, primary cells can be analyzed.

A robust model for read count data in exome sequencing experiments and implications for copy number variant calling

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.uk Supplementary Information: Supplementary data are available at Bioinformatics online.