Type 1 diabetes (T1D) in children results from autoimmune destruction of pancreatic beta cells, leading to insufficient production of insulin. A number of genetic determinants of T1D have already been established through candidate gene studies, primarily within the major histocompatibility complex but also within other loci. To identify new genetic factors that increase the risk of T1D, we performed a genome-wide association study in a large paediatric cohort of European descent. In addition to confirming previously identified loci, we found that T1D was significantly associated with variation within a 233-kb linkage disequilibrium block on chromosome 16p13. This region contains KIAA0350, the gene product of which is predicted to be a sugar-binding, C-type lectin. Three common non-coding variants of the gene (rs2903692, rs725613 and rs17673553) in strong linkage disequilibrium reached genome-wide significance for association with T1D. A subsequent transmission disequilibrium test replication study in an independent cohort confirmed the association. These results indicate that KIAA0350 might be involved in the pathogenesis of T1D and demonstrate the utility of the genome-wide association approach in the identification of previously unsuspected genetic determinants of complex traits.

In comparison to severe acute respiratory syndrome coronavirus (SARS-CoV), SARS-CoV-2 appears to be more contagious [1], and coronavirus disease 2019 (COVID-19) patients demonstrate varied clinical manifestations distinct from those seen in patients with SARS-CoV and Middle East respiratory syndrome coronavirus infections [2]. Collective results from the clinical and epidemiological observations suggest a distinct viral-host interaction in COVID-19 patients. Profiling of the antibody response during SARS-CoV-2 infection may help improve our understanding of the viral-host interaction and the immunopathological mechanisms of the disease. Studies on humoral responses to infections have been mainly geared toward the production of high-affinity IgG antibodies that efficiently resolve an infection. It has been well recognised, however, that humoral immune response to infection can be a double-edged sword that either serves as a protective mechanism to resolve the infection or aggravates the tissue injury, e.g. the IgG response causes fatal acute lung injury by skewing the inflammation-resolving response in SARS-CoV [3]. In the case of respiratory infection, while IgM and IgG isotypes have been the primary emphasis in characterising immunity, mucosal and systemic IgA responses that may play a critical role in the disease pathogenesis have received much less attention. This study was designed to better understand the timing and patterns of humoral immune responses to SARS-CoV-2 in a cohort of COVID-19 patients and evaluate their relationship with the disease course and severity. 37 patients with COVID-19, with a mean±SD age of 52.3±16.3 years, were enrolled in this study. The enrolled COVID-19 patients consisted of 25 (67.6%) males and 12 (32.4%) females. All patients tested positive for viral nucleic acid of SARS-CoV-2 (Real-Time Fluorescent RT-PCR Kit; BGI, Shenzhen, China). According to the "Guidelines for the Diagnosis and Treatment of Novel Coronavirus (2019-nCoV) Infection" published by the National Health Commission of China, the enrolled COVID-19 patients were categorised into two groups: 20 (54.1%) severe cases and 17 (46.0%) nonsevere cases [4]. The nonsevere group included patients with mild and moderate symptoms who were also required to be admitted to hospital by the COVID-19 control policy in China. The severe group included severe and critically ill patients. Mild patients did not demonstrate abnormal computed tomography (CT) imaging. Moderate patients had fever and/or classical respiratory symptoms, and typical CT images of viral pneumonia. Severe patients met at least one of following additional conditions: 1) shortness of breath with a respiratory rate ⩾30 times•min −1 ; 2) oxygen saturation measured by pulse oximetry (resting state) of ⩽93%; or 3) arterial oxygen tension/inspiratory oxygen tension of ⩽300 mmHg. Critically ill patients met at least one of the extra following conditions in addition to the COVID-19 diagnosis: 1) respiratory failure that required mechanical ventilation; 2) shock; or 3) mu...

Diabetes impacts approximately 200 million people worldwide, of whom approximately 10% are affected by type 1 diabetes (T1D). The application of genome-wide association studies (GWAS) has robustly revealed dozens of genetic contributors to the pathogenesis of T1D, with the most recent meta-analysis identifying in excess of 40 loci. To identify additional genetic loci for T1D susceptibility, we examined associations in the largest meta-analysis to date between the disease and ∼2.54 million SNPs in a combined cohort of 9,934 cases and 16,956 controls. Targeted follow-up of 53 SNPs in 1,120 affected trios uncovered three new loci associated with T1D that reached genome-wide significance. The most significantly associated SNP (rs539514, P = 5.66×10−11) resides in an intronic region of the LMO7 (LIM domain only 7) gene on 13q22. The second most significantly associated SNP (rs478222, P = 3.50×10−9) resides in an intronic region of the EFR3B (protein EFR3 homolog B) gene on 2p23; however, the region of linkage disequilibrium is approximately 800 kb and harbors additional multiple genes, including NCOA1, C2orf79, CENPO, ADCY3, DNAJC27, POMC, and DNMT3A. The third most significantly associated SNP (rs924043, P = 8.06×10−9) lies in an intergenic region on 6q27, where the region of association is approximately 900 kb and harbors multiple genes including WDR27, C6orf120, PHF10, TCTE3, C6orf208, LOC154449, DLL1, FAM120B, PSMB1, TBP, and PCD2. These latest associated regions add to the growing repertoire of gene networks predisposing to T1D.

Genome-wide association studies (GWAS) have been fruitful in identifying disease susceptibility loci for common and complex diseases. A remaining question is whether we can quantify individual disease risk based on genotype data, in order to facilitate personalized prevention and treatment for complex diseases. Previous studies have typically failed to achieve satisfactory performance, primarily due to the use of only a limited number of confirmed susceptibility loci. Here we propose that sophisticated machine-learning approaches with a large ensemble of markers may improve the performance of disease risk assessment. We applied a Support Vector Machine (SVM) algorithm on a GWAS dataset generated on the Affymetrix genotyping platform for type 1 diabetes (T1D) and optimized a risk assessment model with hundreds of markers. We subsequently tested this model on an independent Illumina-genotyped dataset with imputed genotypes (1,008 cases and 1,000 controls), as well as a separate Affymetrix-genotyped dataset (1,529 cases and 1,458 controls), resulting in area under ROC curve (AUC) of ∼0.84 in both datasets. In contrast, poor performance was achieved when limited to dozens of known susceptibility loci in the SVM model or logistic regression model. Our study suggests that improved disease risk assessment can be achieved by using algorithms that take into account interactions between a large ensemble of markers. We are optimistic that genotype-based disease risk assessment may be feasible for diseases where a notable proportion of the risk has already been captured by SNP arrays.

Inflammatory bowel disease, including Crohn's disease (CD) and ulcerative colitis (UC), and type 1 diabetes (T1D) are autoimmune diseases that may share common susceptibility pathways. We examined known susceptibility loci for these diseases in a cohort of 1689 CD cases, 777 UC cases, 989 T1D cases and 6197 shared control subjects of European ancestry, who were genotyped by the Illumina HumanHap550 SNP arrays. We identified multiple previously unreported or unconfirmed disease associations, including known CD loci (ICOSLG and TNFSF15) and T1D loci (TNFAIP3) that confer UC risk, known UC loci (HERC2 and IL26) that confer T1D risk and known UC loci (IL10 and CCNY) that confer CD risk. Additionally, we show that T1D risk alleles residing at the PTPN22, IL27, IL18RAP and IL10 loci protect against CD. Furthermore, the strongest risk alleles for T1D within the major histocompatibility complex (MHC) confer strong protection against CD and UC; however, given the multi-allelic nature of the MHC haplotypes, sequencing of the MHC locus will be required to interpret this observation. These results extend our current knowledge on genetic variants that predispose to autoimmunity, and suggest that many loci involved in autoimmunity may be under a balancing selection due to antagonistic pleiotropic effect. Our analysis implies that variants with opposite effects on different diseases may facilitate the maintenance of common susceptibility alleles in human populations, making autoimmune diseases especially amenable to genetic dissection by genome-wide association studies.

OBJECTIVE— Two recent genome-wide association (GWA) studies have revealed novel loci for type 1 diabetes, a common multifactorial disease with a strong genetic component. To fully utilize the GWA data that we had obtained by genotyping 563 type 1 diabetes probands and 1,146 control subjects, as well as 483 case subject–parent trios, using the Illumina HumanHap550 BeadChip, we designed a full stage 2 study to capture other possible association signals. RESEARCH DESIGN AND METHODS— From our existing datasets, we selected 982 markers with P < 0.05 in both GWA cohorts. Genotyping these in an independent set of 636 nuclear families with 974 affected offspring revealed 75 markers that also had P < 0.05 in this third cohort. Among these, six single nucleotide polymorphisms in five novel loci also had P < 0.05 in the Wellcome Trust Case-Control Consortium dataset and were further tested in 1,303 type 1 diabetes probands from the Diabetes Control and Complications Trial/Epidemiology of Diabetes Interventions and Complications (DCCT/EDIC) plus 1,673 control subjects. RESULTS— Two markers (rs9976767 and rs3757247) remained significant after adjusting for the number of tests in this last cohort; they reside in UBASH3A (OR 1.16; combined P = 2.33 × 10 −8 ) and BACH2 (1.13; combined P = 1.25 × 10 −6 ). CONCLUSIONS— Evaluation of a large number of statistical GWA candidates in several independent cohorts has revealed additional loci that are associated with type 1 diabetes. The two genes at these respective loci, UBASH3A and BACH2 , are both biologically relevant to autoimmunity.

OBJECTIVE—In stage 1 of our genome-wide association (GWA) study for type 1 diabetes, one locus at 16p13 was detected (P = 1.03 × 10−10) and confirmed in two additional cohorts. Here we describe the results of testing, in these additional cohorts, 23 loci that were next in rank of statistical significance. RESEARCH DESIGN AND METHODS—Two independent cohorts were studied. The Type 1 Diabetes Genetics Consortium replication cohort consisted of 549 families with at least one child diagnosed with diabetes (946 total affected) and DNA from both parents. The Canadian replication cohort consisted of 364 nuclear family trios with one type 1 diabetes–affected offspring and two parents (1,092 individuals). RESULTS—One locus at 12q13, with the highest statistical significance among the 23, was confirmed. It involves type 1 diabetes association with the minor allele of rs1701704 (P = 9.13 × 10−10, OR 1.25 [95% CI 1.12–1.40]). CONCLUSIONS—We have discovered a type 1 diabetes locus at 12q13 that is replicated in an independent cohort of type 1 diabetic patients and confers a type 1 diabetes risk comparable with that of the 16p13 locus we recently reported. These two loci are identical to two loci identified by the whole-genome association study of the Wellcome Trust Case-Control Consortium, a parallel independent discovery that adds further support to the validity of the GWA approach.

The success of genome-wide association studies (GWAS) to identify risk loci of complex diseases is now well-established. One persistent major hurdle is the cost of those studies, which make them beyond the reach of most research groups. Performing GWAS on pools of DNA samples may be an effective strategy to reduce the costs of these studies. In this study, we performed pooling-based GWAS with more than 550,000 SNPs in two case-control cohorts consisting of patients with Type II diabetes (T2DM) and with chronic rhinosinusitis (CRS). In the T2DM study, the results of the pooling experiment were compared to individual genotypes obtained from a previously published GWAS. TCF7L2 and HHEX SNPs associated with T2DM by the traditional GWAS were among the top ranked SNPs in the pooling experiment. This dataset was also used to refine the best strategy to correctly identify SNPs that will remain significant based on individual genotyping. In the CRS study, the top hits from the pooling-based GWAS located within ten kilobases of known genes were validated by individual genotyping of 1,536 SNPs. Forty-one percent (598 out of the 1,457 SNPs that passed quality control) were associated with CRS at a nominal P value of 0.05, confirming the potential of pooling-based GWAS to identify SNPs that differ in allele frequencies between two groups of subjects. Overall, our results demonstrate that a pooling experiment on high-density genotyping arrays can accurately determine the minor allelic frequency as compared to individual genotyping and produce a list of top ranked SNPs that captures genuine allelic differences between a group of cases and controls. The low cost associated with a pooling-based GWAS clearly justifies its use in screening for genetic determinants of complex diseases.