A genome-wide association study identifies KIAA0350 as a type 1 diabetes gene

Association of CD247 (CD3ζ) gene polymorphisms with T1D and AITD in the population of northern Sweden

Background

T1D and AITD are autoimmune disorders commonly occurring in the same family and even in the same individual. The genetic contribution to these disorders is complex making uncovering of susceptibility genes very challenging. The general aim of this study was to identify loci and genes contributing to T1D/AITD susceptibility. Our strategy was to perform linkage and association studies in the relatively genetically homogenous population of northern Sweden. We performed a GWLS to find genomic regions linked to T1D/AITD in families from northern Sweden and we performed an association study in the families to test for association between T1D/AITD and variants in previously published candidate genes as well as a novel candidate gene, CD247.

Methods

DNA prepared from 459 individuals was used to perform a linkage and an association study. The ABI PRISM Linkage Mapping Set v2.5MD10 was employed for an initial 10-cM GWLS, and additional markers were added for fine mapping. Merlin was used for linkage calculations. For the association analysis, a GoldenGate Custom Panel from Illumina containing 79 SNPs of interest was used and FBAT was used for association calculations.

Results

Our study revealed linkage to two previously identified chromosomal regions, 4q25 and 6p22, as well as to a novel chromosomal region, 1q23. The association study replicated association to PTPN22, HLA-DRB1, INS, IFIH1, CTLA4 and C12orf30. Evidence in favor of association was also found for SNPs in the novel susceptibility gene CD247.

Conclusions

Several risk loci for T1D/AITD identified in published association studies were replicated in a family material, of modest size, from northern Sweden. This provides evidence that these loci confer disease susceptibility in this population and emphasizes that small to intermediate sized family studies in this population can be used in a cost-effective manner for the search of genes involved in complex diseases. The linkage study revealed a chromosomal region in which a novel T1D/AITD susceptibility gene, CD247, is located. The association study showed association between T1D/AITD and several variants in this gene. These results suggests that common susceptibility genes act in concert with variants of CD247 to generate genetic risk for T1D/AITD in this population.

Electronic supplementary material

The online version of this article (doi:10.1186/s12881-016-0333-z) contains supplementary material, which is available to authorized users.

Biomarkers of β-Cell Stress and Death in Type 1 Diabetes

The hallmark of type 1 diabetes (T1D) is a decline in functional β-cell mass arising as a result of autoimmunity. Immunomodulatory interventions at disease onset have resulted in partial stabilization of β-cell function, but full recovery of insulin secretion has remained elusive. Revised efforts have focused on disease prevention through interventions administered at earlier disease stages. To support this paradigm, there is a parallel effort ongoing to identify circulating biomarkers that have the potential to identify stress and death of the islet β-cells. Whereas no definitive biomarker(s) have been fully validated, several approaches hold promise that T1D can be reliably identified in the pre-symptomatic phase, such that either β-cell preservation or immunomodulatory agents might be employed in at-risk populations. This review summarizes the most promising protein- and nucleic acid-based biomarkers discovered to date and reviews the context in which they have been studied.

Phenome-Wide Association Study to Explore Relationships between Immune System Related Genetic Loci and Complex Traits and Diseases

We performed a Phenome-Wide Association Study (PheWAS) to identify interrelationships between the immune system genetic architecture and a wide array of phenotypes from two de-identified electronic health record (EHR) biorepositories. We selected variants within genes encoding critical factors in the immune system and variants with known associations with autoimmunity. To define case/control status for EHR diagnoses, we used International Classification of Diseases, Ninth Revision (ICD-9) diagnosis codes from 3,024 Geisinger Clinic MyCode^® subjects (470 diagnoses) and 2,899 Vanderbilt University Medical Center BioVU biorepository subjects (380 diagnoses). A pooled-analysis was also carried out for the replicating results of the two data sets. We identified new associations with potential biological relevance including SNPs in tumor necrosis factor (TNF) and ankyrin-related genes associated with acute and chronic sinusitis and acute respiratory tract infection. The two most significant associations identified were for the C6orf10 SNP rs6910071 and “rheumatoid arthritis” (ICD-9 code category 714) (p_METAL = 2.58 x 10⁻⁹) and the ATN1 SNP rs2239167 and “diabetes mellitus, type 2” (ICD-9 code category 250) (p_METAL = 6.39 x 10⁻⁹). This study highlights the utility of using PheWAS in conjunction with EHRs to discover new genotypic-phenotypic associations for immune-system related genetic loci.

Physical Activity Benefits the Skeleton of Children Genetically Predisposed to Lower Bone Density in Adulthood

Both genetics and physical activity (PA) contribute to bone mineral density (BMD), but it is unknown if the benefits of physical activity on childhood bone accretion depend on genetic risk. We, therefore, aimed to determine if PA influenced the effect of bone fragility genetic variants on BMD in childhood. Our sample comprised US children of European ancestry enrolled in the Bone Mineral Density in Childhood Study (N = 918, aged 5 to 19 years, and 52.4% female). We used a questionnaire to estimate hours per day spent in total, high-, and low-impact PA. We calculated a BMD genetic score (% BMD lowering alleles) using adult genome-wide association study (GWAS)-implicated BMD variants. We used dual-energy X-ray absorptiometry to estimate femoral neck, total hip, and spine areal-BMD and total body less head (TBLH) bone mineral content (BMC) Z-scores. The BMD genetic score was negatively associated with each bone Z-score (eg, TBLH-BMC: estimate = -0.03, p = 1.3 × 10(-6) ). Total PA was positively associated with bone Z-scores; these associations were driven by time spent in high-impact PA (eg, TBLH-BMC: estimate = 0.05, p = 4.0 × 10(-10) ) and were observed even for children with lower than average bone Z-scores. We found no evidence of PA-adult genetic score interactions (p interaction > 0.05) at any skeletal site, and there was no evidence of PA-genetic score-Tanner stage interactions at any skeletal site (p interaction > 0.05). However, exploratory analyses at the individual variant level revealed that PA statistically interacted with rs2887571 (ERC1/WNT5B) to influence TBLH-BMC in males (p interaction = 7.1 × 10(-5) ), where PA was associated with higher TBLH-BMC Z-score among the BMD-lowering allele carriers (rs2887571 AA homozygotes: estimate = 0.08 [95% CI 0.06, 0.11], p = 2.7 × 10(-9) ). In conclusion, the beneficial effect of PA on bone, especially high-impact PA, applies to the average child and those genetically predisposed to lower adult BMD (based on GWAS-implicated BMD variants). Independent replication of our exploratory individual variant findings is warranted. © 2016 American Society for Bone and Mineral Research.

Understanding the genetic and epigenetic basis of common variable immunodeficiency disorder through omics approaches

BACKGROUND

Common variable immunodeficiency disorder (CVID) is the most frequently encountered symptomatic primary immunodeficiency, characterized by highly heterogeneous immunological features and clinical presentations. As better targeted therapies are importantly needed for CVID, improved understanding of the genetic and epigenetic basis for the development of CVID presents the most promising venue for improvement.

SCOPE OF REVIEW

Several genomic and epigenomic studies of CVID have recently been carried out on cohorts of sporadic cases of CVID. Using high-throughput array and sequencing technologies, these studies identified several loci associated with the disease. Here, we review the omics approaches used in these studies and resulting discoveries. We also discuss how these findings lead to improved understanding of the molecular basis of CVID and possible future directions to pursue.

MAJOR CONCLUSIONS

High-throughput omics approaches have been productive in genetic and epigenetic studies of CVID, leading to the identifications of several significantly associated loci of different variant types, as well as genes and pathways elucidating the shared genetic basis of CVID and autoimmunity. Complex polygenic model of inheritance together with interplay between genetic components and environmental factors may account for the etiology of CVID and various associated comorbidities.

GENERAL SIGNIFICANCE

The genetic and epigenetic basis of CVID when further translated through functional studies will allow for improved understanding of the CVID etiology and will provide new insights into the development of potential new therapeutic approaches for this devastating condition. This article is part of a Special Issue entitled "System Genetics" Guest Editor: Dr. Yudong Cai and Dr. Tao Huang.

Gene Co-Expression Network Analysis for Identifying Modules and Functionally Enriched Pathways in Type 1 Diabetes

Type 1 diabetes (T1D) is a complex disease, caused by the autoimmune destruction of the insulin producing pancreatic beta cells, resulting in the body’s inability to produce insulin. While great efforts have been put into understanding the genetic and environmental factors that contribute to the etiology of the disease, the exact molecular mechanisms are still largely unknown. T1D is a heterogeneous disease, and previous research in this field is mainly focused on the analysis of single genes, or using traditional gene expression profiling, which generally does not reveal the functional context of a gene associated with a complex disorder. However, network-based analysis does take into account the interactions between the diabetes specific genes or proteins and contributes to new knowledge about disease modules, which in turn can be used for identification of potential new biomarkers for T1D. In this study, we analyzed public microarray data of T1D patients and healthy controls by applying a systems biology approach that combines network-based Weighted Gene Co-Expression Network Analysis (WGCNA) with functional enrichment analysis. Novel co-expression gene network modules associated with T1D were elucidated, which in turn provided a basis for the identification of potential pathways and biomarker genes that may be involved in development of T1D.

Long non-coding RNA expression profile in minor salivary gland of primary Sjögren's syndrome

BACKGROUND

To examine the roles of long noncoding RNAs (lncRNAs) in the regulation of primary Sjögren's syndrome (pSS) and reveal the expression profile of lncRNAs in labial salivary glands (LSGs) in pSS patients.

METHOD

The expression of 63,431 lncRNAs and 39,887 mRNAs were determined in the LSG of four pSS patients and four healthy controls using microarray experiments. Validation was performed in 30 pSS patients and 16 controls using real-time PCR. LncRNA-mRNA co-expression and gene-pathway networks were constructed using bioinformatics software.

RESULT

A total of 1243 lncRNAs (upregulated: 890, downregulated: 353) and 1457 mRNAs (upregulated: 1141, downregulated: 316) were differentially expressed in the LSGs of pSS patients (fold change >2, P <0.05). Eight of these lncRNAs were validated using real-time PCR. ENST00000420219.1 (3.13-fold), ENST00000455309.1 (2.51-fold), n336161 (2.45-fold), NR_002712 (2.41-fold), ENST00000546086.1 (1.94-fold), Lnc-UTS2D-1:1 (1.79-fold), n340599 (1.69-fold), and TCONS_l2_00014794 (1.28-fold) were significantly upregulated in pSS. There were strong correlations between these lncRNAs and β2 microglobulin, disease course, erythrocyte sedimentation rate (ESR), rheumatoid factor (RF), IgA, IgM, visual analogue scale (VAS) of parotid swelling and VAS of dry eyes. Computational analyses revealed that 28 of the differentially expressed (DE) mRNAs were associated with eight DE lncRNAs involved in chemokine signaling pathways, the nuclear factor-kappa B (NF-κB) signaling pathway, and tumor necrosis factor (TNF) signaling pathway.

CONCLUSIONS

Our study revealed the expression profile of lncRNAs in LSGs of pSS patients. Many novel lncRNA transcripts that play important roles in the pathogenesis of pSS were dysregulated in pSS. Therefore, this study will aid in the development of new diagnostic biomarkers and drug therapies.

Influence of SNP*SNP interaction on BMI in European American adolescents: findings from the National Longitudinal Study of Adolescent Health

BACKGROUND

Adolescent obesity is predictive of future weight gain, obesity and adult onset severe obesity (body mass index [BMI] ≥40 kg m(-2) ). Despite successful efforts to identify Single Nucleotide Polymorphisms (SNPs) influencing BMI, <5% of the 40-80% heritability of the phenotype has been explained. Identification of gene-gene (G-G) interactions between known variants can help explain this hidden heritability as well as identify potential biological mechanisms affecting weight gain during this critical developmental period.

OBJECTIVE

We have recently shown distinct genetic effects on BMI across the life course, and thus it is important to examine the evidence for epistasis in adolescence.

METHODS

In adolescent participants of European descent from wave II of the National Longitudinal Study of Adolescent Health (Add Health, n = 5072, ages 12-21, 52.5% female), we tested 34 established BMI-related SNPs for G-G interaction effects on BMI z-score. We used mixed-effects regression, assuming multiplicative interaction models adjusting for age, sex and geographic region, with random effects for family and school.

RESULTS

For 28 G-G interactions that were nominally significant (P < 0.05), we attempted to replicate our results in an adolescent sample from the Childhood European American Cohort from Philadelphia. In the replication study, one interaction (PRKD1-FTO) was significant after correction for multiple testing.

CONCLUSIONS

Our results are suggestive of epistatic effects on BMI during adolescence and point to potentially interactive effects between genes in biological pathways important in obesity.

Multiple testing correction in linear mixed models

BACKGROUND

Multiple hypothesis testing is a major issue in genome-wide association studies (GWAS), which often analyze millions of markers. The permutation test is considered to be the gold standard in multiple testing correction as it accurately takes into account the correlation structure of the genome. Recently, the linear mixed model (LMM) has become the standard practice in GWAS, addressing issues of population structure and insufficient power. However, none of the current multiple testing approaches are applicable to LMM.

RESULTS

We were able to estimate per-marker thresholds as accurately as the gold standard approach in real and simulated datasets, while reducing the time required from months to hours. We applied our approach to mouse, yeast, and human datasets to demonstrate the accuracy and efficiency of our approach.

CONCLUSIONS

We provide an efficient and accurate multiple testing correction approach for linear mixed models. We further provide an intuition about the relationships between per-marker threshold, genetic relatedness, and heritability, based on our observations in real data.

Genetic Risk Scores Implicated in Adult Bone Fragility Associate With Pediatric Bone Density

Using adult identified bone mineral density (BMD) loci, we calculated genetic risk scores (GRS) to determine if they were associated with changes in BMD during childhood. Longitudinal data from the Bone Mineral Density in Childhood Study were analyzed (N = 798, 54% female, all European ancestry). Participants had up to 6 annual dual energy X-ray scans, from which areal BMD (aBMD) Z-scores for the spine, total hip, and femoral neck were estimated, as well as total body less head bone mineral content (TBLH-BMC) Z-scores. Sixty-three single-nucleotide polymorphisms (SNPs) were genotyped, and the percentage of BMD-lowering alleles carried was calculated (overall adult GRS). Subtype GRS that include SNPs associated with fracture risk, pediatric BMD, WNT signaling, RANK-RANKL-OPG, and mesenchymal stem cell differentiation were also calculated. Linear mixed effects models were used to test associations between each GRS and bone Z-scores, and if any association differed by sex and/or chronological age. The overall adult, fracture, and WNT signaling GRS were associated with lower Z-scores (eg, spine aBMD Z-score: βadult  = -0.04, p = 3.4 × 10(-7) ; βfracture = -0.02, p = 8.9 × 10(-6) ; βWNT  = -0.01, p = 3.9 × 10(-4) ). The overall adult GRS was more strongly associated with lower Z-scores in females (p-interaction ≤ 0.05 for all sites). The fracture GRS was more strongly associated with lower Z-scores with increasing age (p-interaction ≤ 0.05 for all sites). The WNT GRS associations remained consistent for both sexes and all ages (p-interaction > 0.05 for all sites). The RANK-RANKL-OPG GRS was more strongly associated in females with increasing age (p-interaction < 0.05 for all sites). The mesenchymal stem cell GRS was associated with lower total hip and femoral neck Z-scores, in both boys and girls, across all ages. No associations were observed between the pediatric GRS and bone Z-scores. In conclusion, adult identified BMD loci associated with BMD and BMC in the pediatric setting, especially in females and in loci involved in fracture risk and WNT signaling.

Mitochondrial network regulation and its potential interference with inflammatory signals in pancreatic beta cells

Mitochondria fulfil multiple tasks in nutrient metabolism, energy production, redox homeostasis and stress response, and are essential for pancreatic beta cell function. The dynamism and health of the mitochondrial network is regulated by fission- and fusion-triggering factors and by a quality control system that removes dysfunctional organelles. Alongside the role of mitochondria in regulating apoptotic cell death mediated primarily via production of reactive oxygen species and release of cytochrome c, there is evidence of other links between mitochondria and inflammation that have implications for cell viability. This review briefly outlines two pathways that are potentially vital for pancreatic beta cell function. The first concerns the regulation of Parkin, a protein that acts, not only as a central player in regulating mitophagy, but also as an activator of the NF-ĸB pathway. The fact that expression of optic atrophy protein 1 (OPA1), a mitochondrial fusion inducer and master mitochondrial cristae biogenetic factor, is increased following NF-ĸB activation highlights a point of mitochondrial control that might be influenced by TNFα signalling. A second axis of interest is suggested by IL-6-mediated upregulation of the fission inducer FIS1 alongside downregulation of mitofusin 2 (MFN2), a guard of mitochondrial fusion and metabolism and an inhibitor of apoptosis. This review summarises a presentation given at the 'Islet inflammation in type 2 diabetes' symposium at the 2015 annual meeting of the EASD. It is accompanied two other reviews on topics from this symposium (by Marc Donath, DOI: 10.1007/s00125-016-3873-z , and Jerry Nadler and colleagues, DOI: 10.1007/s00125-016-3890-y ) and a commentary by the Session Chair, Piero Marchetti (DOI: 10.1007/s00125-016-3875-x ).

Genome-wide association analysis of milk yield traits in Nordic Red Cattle using imputed whole genome sequence variants

BACKGROUND

The Nordic Red Cattle consisting of three different populations from Finland, Sweden and Denmark are under a joint breeding value estimation system. The long history of recording of production and health traits offers a great opportunity to study production traits and identify causal variants behind them. In this study, we used whole genome sequence level data from 4280 progeny tested Nordic Red Cattle bulls to scan the genome for loci affecting milk, fat and protein yields.

RESULTS

Using a genome-wise significance threshold, regions on Bos taurus chromosomes 5, 14, 23, 25 and 26 were associated with fat yield. Regions on chromosomes 5, 14, 16, 19, 20 and 25 were associated with milk yield and chromosomes 5, 14 and 25 had regions associated with protein yield. Significantly associated variations were found in 227 genes for fat yield, 72 genes for milk yield and 30 genes for protein yield. Ingenuity Pathway Analysis was used to identify networks connecting these genes displaying significant hits. When compared to previously mapped genomic regions associated with fertility, significantly associated variations were found in 5 genes common for fat yield and fertility, thus linking these two traits via biological networks.

CONCLUSION

This is the first time when whole genome sequence data is utilized to study genomic regions affecting milk production in the Nordic Red Cattle population. Sequence level data offers the possibility to study quantitative traits in detail but still cannot unambiguously reveal which of the associated variations is causative. Linkage disequilibrium creates difficulties to pinpoint the causative genes and variations. One solution to overcome these difficulties is the identification of the functional gene networks and pathways to reveal important interacting genes as candidates for the observed effects. This information on target genomic regions may be exploited to improve genomic prediction.

Clec16a is Critical for Autolysosome Function and Purkinje Cell Survival

CLEC16A is in a locus genetically linked to autoimmune diseases including multiple sclerosis, but the function of this gene in the nervous system is unknown. Here we show that two mouse strains carrying independent Clec16a mutations developed neurodegenerative disease characterized by motor impairments and loss of Purkinje cells. Neurons from Clec16a-mutant mice exhibited increased expression of the autophagy substrate p62, accumulation of abnormal intra-axonal membranous structures bearing the autophagy protein LC3, and abnormal Golgi morphology. Multiple aspects of endocytosis, lysosome and Golgi function were normal in Clec16a-deficient murine embryonic fibroblasts and HeLa cells. However, these cells displayed abnormal bulk autophagy despite unimpaired autophagosome formation. Cultured Clec16a-deficient cells exhibited a striking accumulation of LC3 and LAMP-1 positive autolysosomes containing undigested cytoplasmic contents. Therefore Clec16a, an autophagy protein that is critical for autolysosome function and clearance, is required for Purkinje cell survival.

The DNA methylation profile of activated human natural killer cells

Natural killer (NK) cells are now recognized to exhibit characteristics akin to cells of the adaptive immune system. The generation of adaptive memory is linked to epigenetic reprogramming including alterations in DNA methylation. The study herein found reproducible genome wide DNA methylation changes associated with human NK cell activation. Activation led predominately to CpG hypomethylation (81% of significant loci). Bioinformatics analysis confirmed that non-coding and gene-associated differentially methylated sites (DMS) are enriched for immune related functions (i.e., immune cell activation). Known DNA methylation-regulated immune loci were also identified in activated NK cells (e.g., TNFA, LTA, IL13, CSF2). Twenty-one loci were designated high priority and further investigated as potential markers of NK activation. BHLHE40 was identified as a viable candidate for which a droplet digital PCR assay for demethylation was developed. The assay revealed high demethylation in activated NK cells and low demethylation in naïve NK, T- and B-cells. We conclude the NK cell methylome is plastic with potential for remodeling. The differentially methylated region signature of activated NKs revealed similarities with T cell activation, but also provided unique biomarker candidates of NK activation, which could be useful in epigenome-wide association studies to interrogate the role of NK subtypes in global methylation changes associated with exposures and/or disease states.

Targeted Deep Sequencing in Multiple-Affected Sibships of European Ancestry Identifies Rare Deleterious Variants in PTPN22 That Confer Risk for Type 1 Diabetes

Despite finding more than 40 risk loci for type 1 diabetes (T1D), the causative variants and genes remain largely unknown. Here, we sought to identify rare deleterious variants of moderate-to-large effects contributing to T1D. We deeply sequenced 301 protein-coding genes located in 49 previously reported T1D risk loci in 70 T1D cases of European ancestry. These cases were selected from putatively high-risk families that had three or more siblings diagnosed with T1D at early ages. A cluster of rare deleterious variants in PTPN22 was identified, including two novel frameshift mutations (ss538819444 and rs371865329) and two missense variants (rs74163663 and rs56048322). Genotyping in 3,609 T1D families showed that rs56048322 was significantly associated with T1D and that this association was independent of the T1D-associated common variant rs2476601. The risk allele at rs56048322 affects splicing of PTPN22, resulting in the production of two alternative PTPN22 transcripts and a novel isoform of LYP (the protein encoded by PTPN22). This isoform competes with the wild-type LYP for binding to CSK and results in hyporesponsiveness of CD4(+) T cells to antigen stimulation in T1D subjects. These findings demonstrate that in addition to common variants, rare deleterious variants in PTPN22 exist and can affect T1D risk.

Genome-wide association studies in the Japanese population identify seven novel loci for type 2 diabetes

Genome-wide association studies (GWAS) have identified more than 80 susceptibility loci for type 2 diabetes (T2D), but most of its heritability still remains to be elucidated. In this study, we conducted a meta-analysis of GWAS for T2D in the Japanese population. Combined data from discovery and subsequent validation analyses (23,399 T2D cases and 31,722 controls) identify 7 new loci with genome-wide significance (P<5 × 10(-8)), rs1116357 near CCDC85A, rs147538848 in FAM60A, rs1575972 near DMRTA1, rs9309245 near ASB3, rs67156297 near ATP8B2, rs7107784 near MIR4686 and rs67839313 near INAFM2. Of these, the association of 4 loci with T2D is replicated in multi-ethnic populations other than Japanese (up to 65,936 T2Ds and 158,030 controls, P<0.007). These results indicate that expansion of single ethnic GWAS is still useful to identify novel susceptibility loci to complex traits not only for ethnicity-specific loci but also for common loci across different ethnicities.

Identification of causal genes for complex traits

Motivation: Although genome-wide association studies (GWAS) have identified thousands of variants associated with common diseases and complex traits, only a handful of these variants are validated to be causal. We consider ‘causal variants’ as variants which are responsible for the association signal at a locus. As opposed to association studies that benefit from linkage disequilibrium (LD), the main challenge in identifying causal variants at associated loci lies in distinguishing among the many closely correlated variants due to LD. This is particularly important for model organisms such as inbred mice, where LD extends much further than in human populations, resulting in large stretches of the genome with significantly associated variants. Furthermore, these model organisms are highly structured and require correction for population structure to remove potential spurious associations.

Results: In this work, we propose CAVIAR-Gene (CAusal Variants Identification in Associated Regions), a novel method that is able to operate across large LD regions of the genome while also correcting for population structure. A key feature of our approach is that it provides as output a minimally sized set of genes that captures the genes which harbor causal variants with probability ρ. Through extensive simulations, we demonstrate that our method not only speeds up computation, but also have an average of 10% higher recall rate compared with the existing approaches. We validate our method using a real mouse high-density lipoprotein data (HDL) and show that CAVIAR-Gene is able to identify Apoa2 (a gene known to harbor causal variants for HDL), while reducing the number of genes that need to be tested for functionality by a factor of 2.

Availability and implementation: Software is freely available for download at genetics.cs.ucla.edu/caviar.

Contact: eeskin@cs.ucla.edu

Progress in understanding type 1 diabetes through its genetic overlap with other autoimmune diseases

Type 1 diabetes mellitus (T1DM) is the most common autoimmune disease in pediatrics with a prevalence of roughly 1 in 500 children in the USA. Genome-wide association studies have identified more than 50 variants associated with increased risk for type 1 diabetes. Comparison of these variants with those identified in other autoimmune diseases reveals three important findings: (1) there is a high degree of overlap in implicated variants in diseases with similar pathophysiology, (2) in diseases with differing pathophysiology the same variants are often implicated in opposite roles, (3) in diseases with differing pathophysiology that have many non-overlapping or oppositely implicated variants there are still several variants which are overlapping or shared. Thus, the genetic overlap between T1DM and other autoimmune diseases forms the basis for our understanding of druggable targets in type 1 diabetes.

Genetic sharing and heritability of paediatric age of onset autoimmune diseases

Autoimmune diseases (AIDs) are polygenic diseases affecting 7-10% of the population in the Western Hemisphere with few effective therapies. Here, we quantify the heritability of paediatric AIDs (pAIDs), including JIA, SLE, CEL, T1D, UC, CD, PS, SPA and CVID, attributable to common genomic variations (SNP-h(2)). SNP-h(2) estimates are most significant for T1D (0.863±s.e. 0.07) and JIA (0.727±s.e. 0.037), more modest for UC (0.386±s.e. 0.04) and CD (0.454±0.025), largely consistent with population estimates and are generally greater than that previously reported by adult GWAS. On pairwise analysis, we observed that the diseases UC-CD (0.69±s.e. 0.07) and JIA-CVID (0.343±s.e. 0.13) are the most strongly correlated. Variations across the MHC strongly contribute to SNP-h(2) in T1D and JIA, but does not significantly contribute to the pairwise rG. Together, our results partition contributions of shared versus disease-specific genomic variations to pAID heritability, identifying pAIDs with unexpected risk sharing, while recapitulating known associations between autoimmune diseases previously reported in adult cohorts.

Meta-analysis of shared genetic architecture across ten pediatric autoimmune diseases

Genome-wide association studies (GWASs) have identified hundreds of susceptibility genes, including shared associations across clinically distinct autoimmune diseases. We performed an inverse χ(2) meta-analysis across ten pediatric-age-of-onset autoimmune diseases (pAIDs) in a case-control study including more than 6,035 cases and 10,718 shared population-based controls. We identified 27 genome-wide significant loci associated with one or more pAIDs, mapping to in silico-replicated autoimmune-associated genes (including IL2RA) and new candidate loci with established immunoregulatory functions such as ADGRL2, TENM3, ANKRD30A, ADCY7 and CD40LG. The pAID-associated single-nucleotide polymorphisms (SNPs) were functionally enriched for deoxyribonuclease (DNase)-hypersensitivity sites, expression quantitative trait loci (eQTLs), microRNA (miRNA)-binding sites and coding variants. We also identified biologically correlated, pAID-associated candidate gene sets on the basis of immune cell expression profiling and found evidence of genetic sharing. Network and protein-interaction analyses demonstrated converging roles for the signaling pathways of type 1, 2 and 17 helper T cells (TH1, TH2 and TH17), JAK-STAT, interferon and interleukin in multiple autoimmune diseases.

Genetics of Bone Mass in Childhood and Adolescence: Effects of Sex and Maturation Interactions

We aimed to determine if adult bone mineral density (BMD) susceptibility loci were associated with pediatric bone mass and density, and if sex and pubertal stage influenced any association. We analyzed prospective areal BMD (aBMD) and bone mineral content (BMC) data from the Bone Mineral Density in Childhood Study (n = 603, European ancestry, 54% female). Linear mixed models were used to assess if 77 single-nucleotide polymorphisms (SNPs) near known adult BMD susceptibility loci interacted with sex and pubertal stage to influence the aBMD/BMC; adjusting for age, BMI, physical activity, and dietary calcium. The strongest main association was observed between an SNP near C7orf58 and distal radius aBMD. However, this association had a significant sex • SNP interaction, revealing a significant association only in females (b = -0.32, p = 1.8 × 10(-6)). Furthermore, the C12orf23 locus had significant interactions with both sex and pubertal stage, revealing associations in females during Tanner stage I for total hip aBMD (b = 0.24, p = 0.001) and femoral neck aBMD (b = 0.27, p = 3.0 × 10(-5)). In contrast, the sex • SNP interactions for loci near LRP5 and WNT16 uncovered associations that were only in males for total body less head BMC (b = 0.22, p = 4.4 × 10(-4)) and distal radius aBMD (b = 0.27, p = 0.001), respectively. Furthermore, the LRP5 locus interacted with both sex and pubertal stage, demonstrating associations that were exclusively in males during Tanner V for total hip aBMD (b = 0.29, p = 0.003). In total, significant sex • SNP interactions were found at 15 loci; pubertal stage • SNP interactions at 23 loci and 19 loci interacted with both sex and pubertal stage. In conclusion, variants originally associated with adult BMD influence bone mass in children of European ancestry, highlighting the fact that many of these loci operate early in life. However, the direction and magnitude of associations for a large number of SNPs only became evident when accounting for sex and maturation.

Quantitative Differences in the Urinary Proteome of Siblings Discordant for Type 1 Diabetes Include Lysosomal Enzymes

Individuals with type 1 diabetes (T1D) often have higher than normal blood glucose levels, causing advanced glycation end product formation and inflammation and increasing the risk of vascular complications years or decades later. To examine the urinary proteome in juveniles with T1D for signatures indicative of inflammatory consequences of hyperglycemia, we profiled the proteome of 40 T1D patients with an average of 6.3 years after disease onset and normal or elevated HbA1C levels, in comparison with a cohort of 41 healthy siblings. Using shotgun proteomics, 1036 proteins were identified, on average, per experiment, and 50 proteins showed significant abundance differences using a Wilcoxon signed-rank test (FDR q-value ≤ 0.05). Thirteen lysosomal proteins were increased in abundance in the T1D versus control cohort. Fifteen proteins with functional roles in vascular permeability and adhesion were quantitatively changed, including CD166 antigen and angiotensin-converting enzyme 2. α-N-Acetyl-galactosaminidase and α-fucosidase 2, two differentially abundant lysosomal enzymes, were detected in western blots with often elevated quantities in the T1D versus control cohort. Increased release of proteins derived from lysosomes and vascular epithelium into urine may result from hyperglycemia-associated inflammation in the kidney vasculature.

PRIMA1 mutation: a new cause of nocturnal frontal lobe epilepsy

Objective

Nocturnal frontal lobe epilepsy (NFLE) can be sporadic or autosomal dominant; some families have nicotinic acetylcholine receptor subunit mutations. We report a novel autosomal recessive phenotype in a single family and identify the causative gene.

Methods

Whole exome sequencing data was used to map the family, thereby narrowing exome search space, and then to identify the mutation.

Results

Linkage analysis using exome sequence data from two affected and two unaffected subjects showed homozygous linkage peaks on chromosomes 7, 8, 13, and 14 with maximum LOD scores between 1.5 and 1.93. Exome variant filtering under these peaks revealed that the affected siblings were homozygous for a novel splice site mutation (c.93+2T>C) in the PRIMA1 gene on chromosome 14. No additional PRIMA1 mutations were found in 300 other NFLE cases. The c.93+2T>C mutation was shown to lead to skipping of the first coding exon of the PRIMA1 mRNA using a minigene system.

Interpretation

PRIMA1 is a transmembrane protein that anchors acetylcholinesterase (AChE), an enzyme hydrolyzing acetycholine, to membrane rafts of neurons. PRiMA knockout mice have reduction of AChE and accumulation of acetylcholine at the synapse; our minigene analysis suggests that the c.93+2T>C mutation leads to knockout of PRIMA1. Mutations with gain of function effects in acetylcholine receptor subunits cause autosomal dominant NFLE. Thus, enhanced cholinergic responses are the likely cause of the severe NFLE and intellectual disability segregating in this family, representing the first recessive case to be reported and the first PRIMA1 mutation implicated in disease.

Systemic Lupus Erythematosus Patients Exhibit Reduced Expression of CLEC16A Isoforms in Peripheral Leukocytes

Systemic lupus erythematosus (SLE) is a prototypic autoimmune disease with multiple etiological factors. The SLE susceptibility locus on chromosome 16p13 encodes a novel gene CLEC16A and its functional relationship with SLE is unclear. This study aimed to investigate the expression correlation of the two major CLEC16A spliced transcripts with SLE development. Expressions of the long (V1) and short (V2) CLEC16A isoforms in the peripheral blood mononuclear cells (PBMCs) were assayed by quantitative real time PCR and compared between healthy individuals and SLE patients. Correlation of CLEC16A isoform expression levels with SLE susceptibility, disease severity and twelve clinical parameters were also evaluated. Full length transcripts of CLEC16A V1 and V2 isoforms were readily amplified from PBMCs of healthy controls and patients at varying abundance. Compared with healthy controls (n = 86), expression levels of V1 and V2 were significantly reduced by ~two- and four-fold respectively in SLE patients (n = 181). The relative V2/V1 ratio was also significantly reduced by approximately two-fold. With regard to SLE disease parameters, only a weak positive correlation was found between CLEC16A V1 expression levels and SLE disease activity index (SLEDAI) score. Taken together, CLEC16A was found to be a susceptibility factor for SLE, with possible contribution to the development of the disease.

Linkage Analysis in Autoimmune Addison's Disease: NFATC1 as a Potential Novel Susceptibility Locus

Background

Autoimmune Addison’s disease (AAD) is a rare, highly heritable autoimmune endocrinopathy. It is possible that there may be some highly penetrant variants which confer disease susceptibility that have yet to be discovered.

Methods

DNA samples from 23 multiplex AAD pedigrees from the UK and Norway (50 cases, 67 controls) were genotyped on the Affymetrix SNP 6.0 array. Linkage analysis was performed using Merlin. EMMAX was used to carry out a genome-wide association analysis comparing the familial AAD cases to 2706 UK WTCCC controls. To explore some of the linkage findings further, a replication study was performed by genotyping 64 SNPs in two of the four linked regions (chromosomes 7 and 18), on the Sequenom iPlex platform in three European AAD case-control cohorts (1097 cases, 1117 controls). The data were analysed using a meta-analysis approach.

Results

In a parametric analysis, applying a rare dominant model, loci on chromosomes 7, 9 and 18 had LOD scores >2.8. In a non-parametric analysis, a locus corresponding to the HLA region on chromosome 6, known to be associated with AAD, had a LOD score >3.0. In the genome-wide association analysis, a SNP cluster on chromosome 2 and a pair of SNPs on chromosome 6 were associated with AAD (P <5x10^-7). A meta-analysis of the replication study data demonstrated that three chromosome 18 SNPs were associated with AAD, including a non-synonymous variant in the NFATC1 gene.

Conclusion

This linkage study has implicated a number of novel chromosomal regions in the pathogenesis of AAD in multiplex AAD families and adds further support to the role of HLA in AAD. The genome-wide association analysis has also identified a region of interest on chromosome 2. A replication study has demonstrated that the NFATC1 gene is worthy of future investigation, however each of the regions identified require further, systematic analysis.

A trans-ethnic genome-wide association study identifies gender-specific loci influencing pediatric aBMD and BMC at the distal radius

Childhood fractures are common, with the forearm being the most common site. Genome-wide association studies (GWAS) have identified more than 60 loci associated with bone mineral density (BMD) in adults but less is known about genetic influences specific to bone in childhood. To identify novel genetic factors that influence pediatric bone strength at a common site for childhood fractures, we performed a sex-stratified trans-ethnic genome-wide association study of areal BMD (aBMD) and bone mineral content (BMC) Z-scores measured by dual energy X-ray absorptiometry at the one-third distal radius, in a cohort of 1399 children without clinical abnormalities in bone health. We tested signals with P < 5 × 10(-6) for replication in an independent, same-age cohort of 486 Caucasian children. Two loci yielded a genome-wide significant combined P-value: rs7797976 within CPED1 in females [P = 2.4 × 10(-11), β =- 0.30 standard deviations (SD) per T allele; aBMD-Z] and rs7035284 at 9p21.3 in males (P = 1.2 × 10(-8), β = 0.28 SD per G allele; BMC-Z). Signals at the CPED1-WNT16-FAM3C locus have been previously associated with BMD at other skeletal sites in adults and children. Our result at the distal radius underscores the importance of this locus at multiple skeletal sites. The 9p21.3 locus is within a gene desert, with the nearest gene flanking each side being MIR31HG and MTAP, neither of which has been implicated in BMD or BMC previously. These findings suggest that genetic determinants of childhood bone accretion at the radius, a skeletal site that is primarily cortical bone, exist and also differ by sex.

Emerging role of long noncoding RNAs in autoimmune diseases

Long noncoding RNA (lncRNA), with size larger than 200 nucleotides, is a new class of noncoding RNA. Emerging evidence has revealed that lncRNAs play a key role in the regulation of immunological functions and autoimmunity. Herein, we review the recent findings of lncRNA regulation in immune functions and in the development of autoimmunity and autoimmune disease. In addition, we focus on the involvement of lncRNA regulation in innate and adaptive immune responses, immune cell development, and differential expression of lncRNAs in autoimmune diseases, including systemic lupus erythematosus (SLE), rheumatoid arthritis (RA), type 1 diabetes mellitus (T1DM), multiple sclerosis (MS), autoimmune thyroid disease (AITD), psoriasis, polymyositis/dermatomyositis (PM/DM) and Crohn's disease (CD).

The Autoimmunity-Associated Gene CLEC16A Modulates Thymic Epithelial Cell Autophagy and Alters T Cell Selection

CLEC16A variation has been associated with multiple immune-mediated diseases, including type 1 diabetes, multiple sclerosis, systemic lupus erythematosus, celiac disease, Crohn's disease, Addison's disease, primary biliary cirrhosis, rheumatoid arthritis, juvenile idiopathic arthritis, and alopecia areata. Despite strong genetic evidence implicating CLEC16A in autoimmunity, this gene's broad association with disease remains unexplained. We generated Clec16a knock-down (KD) mice in the nonobese diabetic (NOD) model for type 1 diabetes and found that Clec16a silencing protected against autoimmunity. Disease protection was attributable to T cell hyporeactivity, which was secondary to changes in thymic epithelial cell (TEC) stimuli that drive thymocyte selection. Our data indicate that T cell selection and reactivity were impacted by Clec16a variation in thymic epithelium owing to Clec16a's role in TEC autophagy. These findings provide a functional link between human CLEC16A variation and the immune dysregulation that underlies the risk of autoimmunity.

Association of CLEC16A with human common variable immunodeficiency disorder and role in murine B cells

Common variable immunodeficiency disorder (CVID) is the most common symptomatic primary immunodeficiency in adults, characterized by B-cell abnormalities and inadequate antibody response. CVID patients have considerable autoimmune comorbidity and we therefore hypothesized that genetic susceptibility to CVID may overlap with autoimmune disorders. Here, in the largest genetic study performed in CVID to date, we compare 778 CVID cases with 10,999 controls across 123,127 single-nucleotide polymorphisms (SNPs) on the Immunochip. We identify the first non-HLA genome-wide significant risk locus at CLEC16A (rs17806056, P=2.0 × 10(-9)) and confirm the previously reported human leukocyte antigen (HLA) associations on chromosome 6p21 (rs1049225, P=4.8 × 10(-16)). Clec16a knockdown (KD) mice showed reduced number of B cells and elevated IgM levels compared with controls, suggesting that CLEC16A may be involved in immune regulatory pathways of relevance to CVID. In conclusion, the CLEC16A associations in CVID represent the first robust evidence of non-HLA associations in this immunodeficiency condition.

Multiple sclerosis-associated CLEC16A controls HLA class II expression via late endosome biogenesis

C-type lectins are key players in immune regulation by driving distinct functions of antigen-presenting cells. The C-type lectin CLEC16A gene is located at 16p13, a susceptibility locus for several autoimmune diseases, including multiple sclerosis. However, the function of this gene and its potential contribution to these diseases in humans are poorly understood. In this study, we found a strong upregulation of CLEC16A expression in the white matter of multiple sclerosis patients (n = 14) compared to non-demented controls (n = 11), mainly in perivascular leukocyte infiltrates. Moreover, CLEC16A levels were significantly enhanced in peripheral blood mononuclear cells of multiple sclerosis patients (n = 69) versus healthy controls (n = 46). In peripheral blood mononuclear cells, CLEC16A was most abundant in monocyte-derived dendritic cells, in which it strongly co-localized with human leukocyte antigen class II. Treatment of these professional antigen-presenting cells with vitamin D, a key protective environmental factor in multiple sclerosis, downmodulated CLEC16A in parallel with human leukocyte antigen class II. Knockdown of CLEC16A in distinct types of model and primary antigen-presenting cells resulted in severely impaired cytoplasmic distribution and formation of human leucocyte antigen class II-positive late endosomes, as determined by immunofluorescence and electron microscopy. Mechanistically, CLEC16A participated in the molecular machinery of human leukocyte antigen class II-positive late endosome formation and trafficking to perinuclear regions, involving the dynein motor complex. By performing co-immunoprecipitations, we found that CLEC16A directly binds to two critical members of this complex, RILP and the HOPS complex. CLEC16A silencing in antigen-presenting cells disturbed RILP-mediated recruitment of human leukocyte antigen class II-positive late endosomes to perinuclear regions. Together, we identify CLEC16A as a pivotal gene in multiple sclerosis that serves as a direct regulator of the human leukocyte antigen class II pathway in antigen-presenting cells. These findings are a first step in coupling multiple sclerosis-associated genes to the regulation of the strongest genetic factor in multiple sclerosis, human leukocyte antigen class II.

Multilevel-analysis identify a cis-expression quantitative trait locus associated with risk of renal cell carcinoma

We conducted multilevel analyses to identify potential susceptibility loci for renal cell carcinoma (RCC), which may be overlooked in traditional genome-wide association studies (GWAS). A gene set enrichment analysis was performed utilizing a GWAS dataset comprised of 894 RCC cases and 1,516 controls using GenGen, SNP ratio test, and ALIGATOR. The antigen processing and presentation pathway was consistently significant (P = 0.001, = 0.004, and < 0.001, respectively). Versatile gene-based association study approach was applied to the top-ranked pathway and identified the driven genes. By comparing the expression of the genes in RCC tumor and adjacent normal tissues, we observed significant overexpression of HLA genes in tumor tissues, which was also supported by public databases. We sought to validate genetic variants in antigen processing and presentation pathway in an independent GWAS dataset comprised of 1,311 RCC cases and 3,424 control subjects from the National Cancer Institute; one SNP, rs1063355, was significant in both populations (P_{meta-analysis} = 9.15 × 10⁻⁴, P_{heterogeneity} = 0.427). Strong correlation indicated that rs1063355 was a cis-expression quantitative trait loci which associated with HLA-DQB1 expression (Spearman's rank r = −0.59, p = 5.61 × 10⁻⁶). The correlation was further validated using a public dataset. Our results highlighted the role of immune-related pathway and genes in the etiology of RCC.

Type I diabetes mellitus: genetic factors and presumptive enteroviral etiology or protection

We review type 1 diabetes and host genetic components, as well as epigenetics and viruses associated with type 1 diabetes, with added emphasis on the enteroviruses, which are often associated with triggering the disease. Genus Enterovirus is classified into twelve species of which seven (Enterovirus A, Enterovirus B, Enterovirus C, and Enterovirus D and Rhinovirus A, Rhinovirus B, and Rhinovirus C) are human pathogens. These viruses are transmitted mainly by the fecal-oral route; they may also spread via the nasopharyngeal route. Enterovirus infections are highly prevalent, but these infections are usually subclinical or cause a mild flu-like illness. However, infections caused by enteroviruses can sometimes be serious, with manifestations of meningoencephalitis, paralysis, myocarditis, and in neonates a fulminant sepsis-like syndrome. These viruses are often implicated in chronic (inflammatory) diseases as chronic myocarditis, chronic pancreatitis, and type 1 diabetes. In this review we discuss the currently suggested mechanisms involved in the viral induction of type 1 diabetes. We recapitulate current basic knowledge and definitions.

Fine mapping and functional studies of risk variants for type 1 diabetes at chromosome 16p13.13

Single nucleotide polymorphisms (SNPs) located in the chromosomal region 16p13.13 have been previously associated with risk for several autoimmune diseases, including type 1 diabetes. To identify and localize specific risk variants for type 1 diabetes in this region and understand the mechanism of their action, we resequenced a 455-kb region in type 1 diabetic patients and unaffected control subjects, identifying 93 novel variants. A panel of 939 SNPs that included 46 of these novel variants was genotyped in 3,070 multiplex families with type 1 diabetes. Forty-eight SNPs, all located in CLEC16A, provided a statistically significant association (P < 5.32 × 10(-5)) with disease, with rs34306440 being most significantly associated (P = 5.74 × 10(-6)). The panel of SNPs used for fine mapping was also tested for association with transcript levels for each of the four genes in the region in B lymphoblastoid cell lines. Significant associations were observed only for transcript levels of DEXI, a gene with unknown function. We examined the relationship between the odds ratio for type 1 diabetes and the magnitude of the effect of DEXI transcript levels for each SNP in the region. Among SNPs significantly associated with type 1 diabetes, the common allele conferred an increased risk for disease and corresponded to lower DEXI expression. Our results suggest that the primary mechanism by which genetic variation at CLEC16A contributes to the risk for type 1 diabetes is through reduced expression of DEXI.

Detection and correction of artefacts in estimation of rare copy number variants and analysis of rare deletions in type 1 diabetes

Copy number variants (CNVs) have been proposed as a possible source of ‘missing heritability’ in complex human diseases. Two studies of type 1 diabetes (T1D) found null associations with common copy number polymorphisms, but CNVs of low frequency and high penetrance could still play a role. We used the Log-R-ratio intensity data from a dense single nucleotide polymorphism (SNP) array, ImmunoChip, to detect rare CNV deletions (rDELs) and duplications (rDUPs) in 6808 T1D cases, 9954 controls and 2206 families with T1D-affected offspring. Initial analyses detected CNV associations. However, these were shown to be false-positive findings, failing replication with polymerase chain reaction. We developed a pipeline of quality control (QC) tests that were calibrated using systematic testing of sensitivity and specificity. The case–control odds ratios (OR) of CNV burden on T1D risk resulting from this QC pipeline converged on unity, suggesting no global frequency difference in rDELs or rDUPs. There was evidence that deletions could impact T1D risk for a small minority of cases, with enrichment for rDELs longer than 400 kb (OR = 1.57, P = 0.005). There were also 18 de novo rDELs detected in affected offspring but none for unaffected siblings (P = 0.03). No specific CNV regions showed robust evidence for association with T1D, although frequencies were lower than expected (most less than 0.1%), substantially reducing statistical power, which was examined in detail. We present an R-package, plumbCNV, which provides an automated approach for QC and detection of rare CNVs that can facilitate equivalent analyses of large-scale SNP array datasets.

Toward a comprehensive map of the effectors of rab GTPases

The Rab GTPases recruit peripheral membrane proteins to intracellular organelles. These Rab effectors typically mediate the motility of organelles and vesicles and contribute to the specificity of membrane traffic. However, for many Rabs, few, if any, effectors have been identified; hence, their role remains unclear. To identify Rab effectors, we used a comprehensive set of Drosophila Rabs for affinity chromatography followed by mass spectrometry to identify the proteins bound to each Rab. For many Rabs, this revealed specific interactions with Drosophila orthologs of known effectors. In addition, we found numerous Rab-specific interactions with known components of membrane traffic as well as with diverse proteins not previously linked to organelles or having no known function. We confirm over 25 interactions for Rab2, Rab4, Rab5, Rab6, Rab7, Rab9, Rab18, Rab19, Rab30, and Rab39. These include tethering complexes, coiled-coiled proteins, motor linkers, Rab regulators, and several proteins linked to human disease.

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Proteomic screen identifies effectors of Drosophila Rabs with a human ortholog

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Specific hits include orthologs of numerous known effectors of mammalian Rabs

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Validated effectors include traffic proteins and those of unknown function

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Orthologs of disease genes CLEC16A, LRRK2, and SPG20 are validated as effectors

A Genome-Wide Screen for Large-Effect Alloimmunization Susceptibility Loci among Red Blood Cell Transfusion Recipients with Sickle Cell Disease

BACKGROUND

A selective susceptibility of certain individuals to form multiple alloantibodies in response to red cell transfusion is well-recognized in clinical practice, and is a particular problem in persons with sickle cell disease (SCD). The reason for this differential susceptibility is unclear, but inter-individual genetic differences are likely to contribute.

METHODS

We conducted a pilot case-control genome-wide association study using 1,000,000 SNPs in 94 alloimmune responders (cases) and non-responders (controls) with SCD in order to identify loci of large effect size associated with alloimmunization.

RESULTS

No loci showed evidence of association at a genome-wide significance cut-off (p < 0.5 × 10(-8)). SNPs in the ARAP1/STARD10 region showed suggestive association (p < 1 × 10(-6)), but no association was observed at previously implicated loci TRIM21 or HLA. In analyses of the number of accumulated antibodies, a modest association was found with SNPs in the Toll-like receptor gene TLR10 (p < 1 × 10(-4)).

CONCLUSIONS

Alloimmunization in persons with SCD is unlikely to be mediated by loci of very large effect size; however, larger and more comprehensive studies are required to fully evaluate loci with more moderate effects. This study provides a working approach to such future studies in SCD.

Genome-wide associations between genetic and epigenetic variation influence mRNA expression and insulin secretion in human pancreatic islets

Genetic and epigenetic mechanisms may interact and together affect biological processes and disease development. However, most previous studies have investigated genetic and epigenetic mechanisms independently, and studies examining their interactions throughout the human genome are lacking. To identify genetic loci that interact with the epigenome, we performed the first genome-wide DNA methylation quantitative trait locus (mQTL) analysis in human pancreatic islets. We related 574,553 single nucleotide polymorphisms (SNPs) with genome-wide DNA methylation data of 468,787 CpG sites targeting 99% of RefSeq genes in islets from 89 donors. We identified 67,438 SNP-CpG pairs in cis, corresponding to 36,783 SNPs (6.4% of tested SNPs) and 11,735 CpG sites (2.5% of tested CpGs), and 2,562 significant SNP-CpG pairs in trans, corresponding to 1,465 SNPs (0.3% of tested SNPs) and 383 CpG sites (0.08% of tested CpGs), showing significant associations after correction for multiple testing. These include reported diabetes loci, e.g. ADCY5, KCNJ11, HLA-DQA1, INS, PDX1 and GRB10. CpGs of significant cis-mQTLs were overrepresented in the gene body and outside of CpG islands. Follow-up analyses further identified mQTLs associated with gene expression and insulin secretion in human islets. Causal inference test (CIT) identified SNP-CpG pairs where DNA methylation in human islets is the potential mediator of the genetic association with gene expression or insulin secretion. Functional analyses further demonstrated that identified candidate genes (GPX7, GSTT1 and SNX19) directly affect key biological processes such as proliferation and apoptosis in pancreatic β-cells. Finally, we found direct correlations between DNA methylation of 22,773 (4.9%) CpGs with mRNA expression of 4,876 genes, where 90% of the correlations were negative when CpGs were located in the region surrounding transcription start site. Our study demonstrates for the first time how genome-wide genetic and epigenetic variation interacts to influence gene expression, islet function and potential diabetes risk in humans.

Inter-individual variation in genetics and epigenetics affects biological processes and disease susceptibility. However, most studies have investigated genetic and epigenetic mechanisms independently and to uncover novel mechanisms affecting disease susceptibility there is a highlighted need to study interactions between these factors on a genome-wide scale. To identify novel loci affecting islet function and potentially diabetes, we performed the first genome-wide methylation quantitative trait locus (mQTL) analysis in human pancreatic islets including DNA methylation of 468,787 CpG sites located throughout the genome. Our results showed that DNA methylation of 11,735 CpGs in 4,504 unique genes is regulated by genetic factors located in cis (67,438 SNP-CpG pairs). Furthermore, significant mQTLs cover previously reported diabetes loci including KCNJ11, INS, HLA, PDX1 and GRB10. We also found mQTLs associated with gene expression and insulin secretion in human islets. By performing causality inference tests (CIT), we identified CpGs where DNA methylation potentially mediates the genetic impact on gene expression and insulin secretion. Our functional follow-up experiments further demonstrated that identified mQTLs/genes (GPX7, GSTT1 and SNX19) directly affect pancreatic β-cell function. Together, our study provides a detailed map of genome-wide associations between genetic and epigenetic variation, which affect gene expression and insulin secretion in human pancreatic islets.

Association of the C-type lectin-like domain family-16A (CLEC16A) gene polymorphisms with acute coronary syndrome in Mexican patients

The CLEC16A gene has an important role in the immune activation and regulation inflammatory. This gene encodes to C-type lectin domain that is involved in the recognition of DAMPS. The aim of this study was assess the CLEC16A gene polymorphisms in the risk of developing ACS in a group of patients. Four rs12708716, rs12917716, rs6498142 and rs9925481 (positions 146529 A>G, 155804 G>C, 47905 C>G and 64135 C>T, respectively) single nucleotide polymorphisms of CLEC16A gene were analyzed by TaqMan assays in a group of 452 patients with ACS and 456 healthy controls. The analysis was performed on the total group of individuals and then in groups of men and women separately. Under co-dominant model adjusted by cardiovascular risk factors the rs12708716 (146529 A>G) and rs12917716 (155804 G>C) polymorphisms were significantly associated with decrease risk of ACS in men (OR=0.16, PCo-dom=0.027 and OR=0.37, PCo-dom=0.016, respectively). In summary, our data suggests that two polymorphisms of the CLEC16A gene play an important role in the developing of ACS in men.

Identifying causal variants at loci with multiple signals of association

Although genome-wide association studies have successfully identified thousands of risk loci for complex traits, only a handful of the biologically causal variants, responsible for association at these loci, have been successfully identified. Current statistical methods for identifying causal variants at risk loci either use the strength of the association signal in an iterative conditioning framework or estimate probabilities for variants to be causal. A main drawback of existing methods is that they rely on the simplifying assumption of a single causal variant at each risk locus, which is typically invalid at many risk loci. In this work, we propose a new statistical framework that allows for the possibility of an arbitrary number of causal variants when estimating the posterior probability of a variant being causal. A direct benefit of our approach is that we predict a set of variants for each locus that under reasonable assumptions will contain all of the true causal variants with a high confidence level (e.g., 95%) even when the locus contains multiple causal variants. We use simulations to show that our approach provides 20-50% improvement in our ability to identify the causal variants compared to the existing methods at loci harboring multiple causal variants. We validate our approach using empirical data from an expression QTL study of CHI3L2 to identify new causal variants that affect gene expression at this locus. CAVIAR is publicly available online at http://genetics.cs.ucla.edu/caviar/.

Morphological alterations in the dentition of type I diabetes mellitus patients

Introduction:

Type 1 diabetes mellitus (DM) is an endocrine disorder that occurs commonly in an age group, where the development of primary and permanent dentition takes place. As altered endocrine functions may affect the shape and size of teeth leading to dental anomalies, this study was conducted to look for the occurrence of any dental anomalies in type I DM patients.

Materials and Methods:

A diabetic camp was conducted at Alur Chandrashekharappa Memorial Hospital, Davangere, where 30 diabetic patients were examined and the impressions of their maxillary and mandibular arches were recorded. Age and sex matched controls were selected randomly, and similar recordings were done.

Results:

Type I diabetic patients showed statistically significant (P < 0.001) morphological alterations of total number of cusps, including presence of 6^th cusp in mandibular molars and extra cusps in mandibular premolars. Other alterations such as microdontia, flower shaped mandibular molars, prominent cusp of carabelli, and oblique ridge in maxillary molars were also noted. Severe attrition was found in 11 (36.6%) of the diabetic patients, whereas the control group showed attrition only in 2 (6.8%) patients.

Conclusion:

Remarkable morphological alterations do occur in the dentition of type I DM patients.

Principal component analysis characterizes shared pathogenetics from genome-wide association studies

Genome-wide association studies (GWASs) have recently revealed many genetic associations that are shared between different diseases. We propose a method, disPCA, for genome-wide characterization of shared and distinct risk factors between and within disease classes. It flips the conventional GWAS paradigm by analyzing the diseases themselves, across GWAS datasets, to explore their "shared pathogenetics". The method applies principal component analysis (PCA) to gene-level significance scores across all genes and across GWASs, thereby revealing shared pathogenetics between diseases in an unsupervised fashion. Importantly, it adjusts for potential sources of heterogeneity present between GWAS which can confound investigation of shared disease etiology. We applied disPCA to 31 GWASs, including autoimmune diseases, cancers, psychiatric disorders, and neurological disorders. The leading principal components separate these disease classes, as well as inflammatory bowel diseases from other autoimmune diseases. Generally, distinct diseases from the same class tend to be less separated, which is in line with their increased shared etiology. Enrichment analysis of genes contributing to leading principal components revealed pathways that are implicated in the immune system, while also pointing to pathways that have yet to be explored before in this context. Our results point to the potential of disPCA in going beyond epidemiological findings of the co-occurrence of distinct diseases, to highlighting novel genes and pathways that unsupervised learning suggest to be key players in the variability across diseases.

The diabetes susceptibility gene Clec16a regulates mitophagy

Clec16a has been identified as a disease susceptibility gene for type 1 diabetes, multiple sclerosis, and adrenal dysfunction, but its function is unknown. Here we report that Clec16a is a membrane-associated endosomal protein that interacts with E3 ubiquitin ligase Nrdp1. Loss of Clec16a leads to an increase in the Nrdp1 target Parkin, a master regulator of mitophagy. Islets from mice with pancreas-specific deletion of Clec16a have abnormal mitochondria with reduced oxygen consumption and ATP concentration, both of which are required for normal β cell function. Indeed, pancreatic Clec16a is required for normal glucose-stimulated insulin release. Moreover, patients harboring a diabetogenic SNP in the Clec16a gene have reduced islet Clec16a expression and reduced insulin secretion. Thus, Clec16a controls β cell function and prevents diabetes by controlling mitophagy. This pathway could be targeted for prevention and control of diabetes and may extend to the pathogenesis of other Clec16a- and Parkin-associated diseases.

Genome-wide association study of maternal and inherited effects on left-sided cardiac malformations

Congenital left-sided lesions (LSLs) are serious, heritable malformations of the heart. However, little is known about the genetic causes of LSLs. This study was undertaken to identify common variants acting through the genotype of the affected individual (i.e. case) or the mother (e.g. via an in utero effect) that influence the risk of LSLs. A genome-wide association study (GWAS) was performed using data from 377 LSL case-parent triads, with follow-up studies in an independent sample of 224 triads and analysis of the combined data. Associations with both the case and maternal genotypes were assessed using log-linear analyses under an additive model. An association between LSLs and the case genotype for one intergenic SNP on chromosome 16 achieved genome-wide significance in the combined data (rs8061121, combined P = 4.0 × 10(-9); relative risk to heterozygote: 2.6, 95% CI: 1.9-3.7). In the combined data, there was also suggestive evidence of association between LSLs and the case genotype for a variant in the synaptoporin gene (rs1975649, combined P = 3.4 × 10(-7); relative risk to heterozygote: 1.6, 95% CI: 1.4-2.0) and between LSLs and the maternal genotype for an intergenic SNP on chromosome 10 (rs11008222, combined P = 6.3 × 10(-7); relative risk to heterozygote: 1.6, 95% CI: 1.4-2.0). This is the first GWAS of LSLs to evaluate associations with both the case and maternal genotypes. The results of this study identify three candidate LSL susceptibility loci, including one that appears to be associated with the risk of LSLs via the maternal genotype.

Associations between TNF gene polymorphisms (-308 A/G, -238 A/G, -1031 C/T and -857 T/C) and genetic susceptibility to T1D: a meta-analysis

The aim of this study was to estimate the associations between tumor necrosis factor (TNF) gene polymorphisms and type 1 diabetes (T1D) using meta-analysis. Relevant studies were searched using PubMed and Embase up to August 2013. A total of 32 comparisons from 21 studies examining the associations between TNF polymorphisms and T1D were included in the present meta-analysis. Our meta-analysis identified a significant association between TNF -308 A/G polymorphism A allele and T1D in all subjects [odds ratio (OR) 2.001, 95 % confidence interval (CI) 1.732-2.312). Significant associations of AA and AA+AG genotype of TNF -308 A/G polymorphism with genetic susceptibility to T1D were also found (OR 3.203, 95 % CI 2.373-4.324; OR 2.232, 95 % CI 1.881-2.649). After stratification by ethnicity, significant associations of T1D with TNF -308 A/G polymorphism under all genetic models (A allele and AA, AA+AG genotype) were still detected in European (OR 1.952, 95 % CI 1.675-2.274; OR 3.108, 95 % CI 2.169-4.455; OR 2.249, 95 % CI 1.870-2.706, respectively) and non-European populations (OR 2.152, 95 % CI 1.488-3.112; OR 3.439, 95 % CI 2.000-5.914; OR 2.207, 95 % CI 1.496-3.257, respectively). Our meta-analysis also revealed an association of TNF -857 T/C polymorphism T allele with T1D risk (OR 1.647, 95 % CI 1.431-1.896). Furthermore, analysis of TT and TT+TC genotype indicated the same result patterns as shown by the TNF -857 T/C polymorphism T allele (OR 2.206, 95 % CI 1.467-3.317; OR 1.762, 95 % CI 1.490-2.083). In conclusion, our meta-analysis results indicate that TNF -308 A/G and -857 T/C polymorphisms are involved in the genetic background of T1D.

Meta-analysis of genome-wide association studies in African Americans provides insights into the genetic architecture of type 2 diabetes

Type 2 diabetes (T2D) is more prevalent in African Americans than in Europeans. However, little is known about the genetic risk in African Americans despite the recent identification of more than 70 T2D loci primarily by genome-wide association studies (GWAS) in individuals of European ancestry. In order to investigate the genetic architecture of T2D in African Americans, the MEta-analysis of type 2 DIabetes in African Americans (MEDIA) Consortium examined 17 GWAS on T2D comprising 8,284 cases and 15,543 controls in African Americans in stage 1 analysis. Single nucleotide polymorphisms (SNPs) association analysis was conducted in each study under the additive model after adjustment for age, sex, study site, and principal components. Meta-analysis of approximately 2.6 million genotyped and imputed SNPs in all studies was conducted using an inverse variance-weighted fixed effect model. Replications were performed to follow up 21 loci in up to 6,061 cases and 5,483 controls in African Americans, and 8,130 cases and 38,987 controls of European ancestry. We identified three known loci (TCF7L2, HMGA2 and KCNQ1) and two novel loci (HLA-B and INS-IGF2) at genome-wide significance (4.15 × 10(-94) Collapse

Key Words Collapse

MESH Headings

• Black or African American/genetics
• Diabetes Mellitus, Type 2/genetics
• Diabetes Mellitus, Type 2/pathology
• Genome-Wide Association Study
• HLA-B27 Antigen/genetics
• HMGA2 Protein/genetics
• Humans
• KCNQ1 Potassium Channel/genetics
• Mutant Chimeric Proteins/genetics
• Polymorphism, Single Nucleotide
• Transcription Factor 7-Like 2 Protein/genetics

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Grants

• R01 DK078616 NIDDK NIH HHS
• HHSN268201100012C NHLBI NIH HHS
• UL1RR025005 NCRR NIH HHS
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• Wellcome Trust
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Affiliation(s)

Maggie C. Y. Ng Center for Genomics and Personalized Medicine Research, Wake Forest School of Medicine, Winston-Salem, North Carolina, United States of America Center for Diabetes Research, Wake Forest School of Medicine, Winston-Salem, North Carolina, United States of America
Daniel Shriner Center for Research on Genomics and Global Health, National Human Genome Research Institute, Bethesda, Maryland, United States of America
Brian H. Chen Program on Genomics and Nutrition, School of Public Health, University of California Los Angeles, Los Angeles, California, United States of America Center for Metabolic Disease Prevention, School of Public Health, University of California Los Angeles, Los Angeles, California, United States of America
Jiang Li Center for Diabetes Research, Wake Forest School of Medicine, Winston-Salem, North Carolina, United States of America
Wei-Min Chen Center for Public Health Genomics, University of Virginia, Charlottesville, Virginia, United States of America Department of Public Health Sciences, University of Virginia, Charlottesville, Virginia, United States of America
Xiuqing Guo Institute for Translational Genomics and Population Sciences, Los Angeles BioMedical Research Institute at Harbor-UCLA Medical Center, Torrance, California, United States of America
Jiankang Liu Department of Medicine, University of Mississippi Medical Center, Jackson, Mississippi, United States of America
Suzette J. Bielinski Division of Epidemiology, Department of Health Sciences Research, Mayo Clinic, Rochester, Minnesota, United States of America
Lisa R. Yanek The GeneSTAR Research Program, Division of General Internal Medicine, Department of Medicine, The Johns Hopkins University School of Medicine, Baltimore, Maryland, United States of America
Michael A. Nalls Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, Maryland, United States of America
Mary E. Comeau Center for Public Health Genomics, Division of Public Health Sciences, Wake Forest School of Medicine, Winston-Salem, North Carolina, United States of America Department of Biostatistical Sciences, Division of Public Health Sciences, Wake Forest School of Medicine, Winston-Salem, North Carolina, United States of America
Laura J. Rasmussen-Torvik Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois, United States of America
Richard A. Jensen Cardiovascular Health Research Unit, University of Washington, Seattle, Washington, United States of America Department of Medicine, University of Washington, Seattle, Washington, United States of America
Daniel S. Evans San Francisco Coordinating Center, California Pacific Medical Center Research Institute, San Francisco, California, United States of America
Yan V. Sun Department of Epidemiology and Biomedical Informatics, Emory University, Atlanta, Georgia, United States of America
Ping An Division of Statistical Genomics, Washington University School of Medicine, St. Louis, Missouri, United States of America
Sanjay R. Patel Division of Sleep Medicine, Brigham and Women's Hospital, Boston, Massachusetts, United States of America
Yingchang Lu The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, United States of America The Genetics of Obesity and Related Metabolic Traits Program, Icahn School of Medicine at Mount Sinai, New York, New York, United States of America
Jirong Long Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University School of Medicine, Nashville, Tennessee, United States of America
Loren L. Armstrong Division of Endocrinology, Metabolism and Molecular Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois, United States of America
Lynne Wagenknecht Division of Public Health Sciences, Wake Forest School of Medicine, Winston-Salem, North Carolina, United States of America
Lingyao Yang Department of Biostatistical Sciences, Division of Public Health Sciences, Wake Forest School of Medicine, Winston-Salem, North Carolina, United States of America
Beverly M. Snively Department of Biostatistical Sciences, Division of Public Health Sciences, Wake Forest School of Medicine, Winston-Salem, North Carolina, United States of America
Nicholette D. Palmer Center for Genomics and Personalized Medicine Research, Wake Forest School of Medicine, Winston-Salem, North Carolina, United States of America Center for Diabetes Research, Wake Forest School of Medicine, Winston-Salem, North Carolina, United States of America Department of Biochemistry, Wake Forest School of Medicine, Winston-Salem, North Carolina, United States of America
Poorva Mudgal Center for Diabetes Research, Wake Forest School of Medicine, Winston-Salem, North Carolina, United States of America
Carl D. Langefeld Center for Public Health Genomics, Division of Public Health Sciences, Wake Forest School of Medicine, Winston-Salem, North Carolina, United States of America Department of Biostatistical Sciences, Division of Public Health Sciences, Wake Forest School of Medicine, Winston-Salem, North Carolina, United States of America
Keith L. Keene Department of Biology, Center for Health Disparities, East Carolina University, Greenville, North Carolina, United States of America
Barry I. Freedman Department of Internal Medicine, Wake Forest School of Medicine, Winston-Salem, North Carolina, United States of America
Josyf C. Mychaleckyj Center for Public Health Genomics, University of Virginia, Charlottesville, Virginia, United States of America Department of Public Health Sciences, University of Virginia, Charlottesville, Virginia, United States of America
Uma Nayak Center for Public Health Genomics, University of Virginia, Charlottesville, Virginia, United States of America Department of Public Health Sciences, University of Virginia, Charlottesville, Virginia, United States of America
Leslie J. Raffel Medical Genetics Research Institute, Cedars-Sinai Medical Center, Los Angeles, California, United States of America
Mark O. Goodarzi Medical Genetics Research Institute, Cedars-Sinai Medical Center, Los Angeles, California, United States of America
Y-D Ida Chen Institute for Translational Genomics and Population Sciences, Los Angeles BioMedical Research Institute at Harbor-UCLA Medical Center, Torrance, California, United States of America
Herman A. Taylor Department of Medicine, University of Mississippi Medical Center, Jackson, Mississippi, United States of America Jackson State University, Tougaloo College, Jackson, Mississippi, United States of America
Adolfo Correa Department of Medicine, University of Mississippi Medical Center, Jackson, Mississippi, United States of America
Mario Sims Department of Medicine, University of Mississippi Medical Center, Jackson, Mississippi, United States of America
David Couper Collaborative Studies Coordinating Center, Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America
James S. Pankow Division of Epidemiology and Community Health, University of Minnesota, Minneapolis, Minnesota, United States of America
Eric Boerwinkle Human Genetics Center, University of Texas Health Science Center at Houston, Houston, Texas, United States of America
Adebowale Adeyemo Center for Research on Genomics and Global Health, National Human Genome Research Institute, Bethesda, Maryland, United States of America
Ayo Doumatey Center for Research on Genomics and Global Health, National Human Genome Research Institute, Bethesda, Maryland, United States of America
Guanjie Chen Center for Research on Genomics and Global Health, National Human Genome Research Institute, Bethesda, Maryland, United States of America
Rasika A. Mathias The GeneSTAR Research Program, Division of General Internal Medicine, Department of Medicine, The Johns Hopkins University School of Medicine, Baltimore, Maryland, United States of America Division of Allergy and Clinical Immunology, Department of Medicine, The Johns Hopkins University School of Medicine, Baltimore, Maryland, United States of America
Dhananjay Vaidya The GeneSTAR Research Program, Division of General Internal Medicine, Department of Medicine, The Johns Hopkins University School of Medicine, Baltimore, Maryland, United States of America Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, United States of America
Andrew B. Singleton Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, Maryland, United States of America
Alan B. Zonderman Laboratory of Personality and Cognition, National Institute on Aging, National Institutes of Health, Baltimore, Maryland, United States of America
Robert P. Igo Department of Epidemiology and Biostatistics, Case Western Reserve University, Cleveland, Ohio, United States of America
John R. Sedor Department of Medicine, Case Western Reserve University, MetroHealth System campus, Cleveland, Ohio, United States of America Department of Physiology and Biophysics, Case Western Reserve University, Cleveland, Ohio, United States of America
the FIND Consortium
Edmond K. Kabagambe Division of Epidemiology, Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee, United States of America
David S. Siscovick Cardiovascular Health Research Unit, University of Washington, Seattle, Washington, United States of America Department of Medicine, University of Washington, Seattle, Washington, United States of America Department of Epidemiology, University of Washington, Seattle, Washington, United States of America
Barbara McKnight Cardiovascular Health Research Unit, University of Washington, Seattle, Washington, United States of America Department of Biostatistics, University of Washington, Seattle, Washington, United States of America
Kenneth Rice Cardiovascular Health Research Unit, University of Washington, Seattle, Washington, United States of America Department of Biostatistics, University of Washington, Seattle, Washington, United States of America
Yongmei Liu Department of Epidemiology and Prevention, Division of Public Health Sciences, Wake Forest School of Medicine, Winston-Salem, North Carolina, United States of America
Wen-Chi Hsueh Department of Medicine, University of California, San Francisco, California, United States of America
Wei Zhao Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, Michigan, United States of America
Lawrence F. Bielak Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, Michigan, United States of America
Aldi Kraja Division of Statistical Genomics, Washington University School of Medicine, St. Louis, Missouri, United States of America
Michael A. Province Division of Statistical Genomics, Washington University School of Medicine, St. Louis, Missouri, United States of America
Erwin P. Bottinger The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, United States of America
Omri Gottesman The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, United States of America
Qiuyin Cai Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University School of Medicine, Nashville, Tennessee, United States of America
Wei Zheng Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University School of Medicine, Nashville, Tennessee, United States of America
William J. Blot Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University School of Medicine, Nashville, Tennessee; International Epidemiology Institute, Rockville, Maryland, United States of America
William L. Lowe Division of Endocrinology, Metabolism and Molecular Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois, United States of America
Jennifer A. Pacheco Center for Genetic Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois, United States of America
Dana C. Crawford Center for Human Genetics Research and Department of Molecular Physiology and Biophysics, Vanderbilt University, Nashville, Tennessee, United States of America
the eMERGE Consortium
the DIAGRAM Consortium
Elin Grundberg Department of Twin Research and Genetic Epidemiology, King's College London, London, United Kingdom
the MuTHER Consortium
Stephen S. Rich Center for Public Health Genomics, University of Virginia, Charlottesville, Virginia, United States of America
M. Geoffrey Hayes Division of Endocrinology, Metabolism and Molecular Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois, United States of America
Xiao-Ou Shu Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University School of Medicine, Nashville, Tennessee, United States of America
Ruth J. F. Loos The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, United States of America The Genetics of Obesity and Related Metabolic Traits Program, Icahn School of Medicine at Mount Sinai, New York, New York, United States of America Child Health and Development Institute, Icahn School of Medicine at Mount Sinai, New York, New York, United States of America
Ingrid B. Borecki Division of Statistical Genomics, Washington University School of Medicine, St. Louis, Missouri, United States of America
Patricia A. Peyser Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, Michigan, United States of America
Steven R. Cummings San Francisco Coordinating Center, California Pacific Medical Center Research Institute, San Francisco, California, United States of America
Bruce M. Psaty Cardiovascular Health Research Unit, University of Washington, Seattle, Washington, United States of America Department of Medicine, University of Washington, Seattle, Washington, United States of America Department of Epidemiology, University of Washington, Seattle, Washington, United States of America Department of Health Services, University of Washington, Seattle, Washington, United States of America
Myriam Fornage Human Genetics Center, University of Texas Health Science Center at Houston, Houston, Texas, United States of America
Sudha K. Iyengar Department of Epidemiology and Biostatistics, Case Western Reserve University, Cleveland, Ohio, United States of America
Michele K. Evans Health Disparities Unit, National Institute on Aging, National Institutes of Health, Baltimore Maryland, United States of America
Diane M. Becker The GeneSTAR Research Program, Division of General Internal Medicine, Department of Medicine, The Johns Hopkins University School of Medicine, Baltimore, Maryland, United States of America Department of Health Policy and Management, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, United States of America
W. H. Linda Kao Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, United States of America
James G. Wilson Department of Physiology and Biophysics, University of Mississippi Medical Center, Jackson, Mississippi, United States of America
Jerome I. Rotter Institute for Translational Genomics and Population Sciences, Los Angeles BioMedical Research Institute at Harbor-UCLA Medical Center, Torrance, California, United States of America
Michèle M. Sale Center for Public Health Genomics, University of Virginia, Charlottesville, Virginia, United States of America Department of Medicine, University of Virginia, Charlottesville, Virginia, United States of America Department of Biochemistry and Molecular Genetics, University of Virginia, Charlottesville, Virginia, United States of America
Simin Liu Program on Genomics and Nutrition, School of Public Health, University of California Los Angeles, Los Angeles, California, United States of America Department of Epidemiology, University of California Los Angeles, Los Angeles, California, United States of America Departments of Epidemiology and Medicine, Brown University, Providence, Rhode Island, United States of America
Charles N. Rotimi Center for Research on Genomics and Global Health, National Human Genome Research Institute, Bethesda, Maryland, United States of America
Donald W. Bowden Center for Genomics and Personalized Medicine Research, Wake Forest School of Medicine, Winston-Salem, North Carolina, United States of America Center for Diabetes Research, Wake Forest School of Medicine, Winston-Salem, North Carolina, United States of America Department of Biochemistry, Wake Forest School of Medicine, Winston-Salem, North Carolina, United States of America
for the MEta-analysis of type 2 DIabetes in African Americans (MEDIA) Consortium

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Microbiome manipulation modifies sex-specific risk for autoimmunity

Despite growing evidence for a causal role of environmental factors in autoimmune diseases including the rise in disease frequencies over the past several decades we lack an understanding of how particular environmental exposures modify disease risk. In addition, many autoimmune diseases display sex-biased incidence, with females being disproportionately affected but the mechanisms underlying this sex bias remain elusive. Emerging evidence suggests that both host metabolism and immune function is crucially regulated by the intestinal microbiome. Recently, we showed that in the non-obese diabetic (NOD) mouse model of Type 1 Diabetes (T1D), the gut commensal microbial community strongly impacts the pronounced sex bias in T1D risk by controlling serum testosterone and metabolic phenotypes (1). Here we present new data in the NOD model that explores the correlations between microbial phylogeny, testosterone levels, and metabolic phenotypes, and discuss the future of microbiome-centered analysis and microbe-based therapeutic approaches in autoimmune diseases.

Role of immune system in type 1 diabetes mellitus pathogenesis

The immune system is the body's natural defense system against invading pathogens. It protects the body from infection and works to communicate an individual's well-being through a complex network of interconnected cells and cytokines. This system is an associated host defense. An uncontrolled immune system has the potential to trigger negative complications in the host. Type 1 diabetes results from the destruction of pancreatic β-cells by a β-cell-specific autoimmune process. Examples of β-cell autoantigens are insulin, glutamic acid decarboxylase, tyrosine phosphatase, and insulinoma antigen. There are many autoimmune diseases, but type 1 diabetes mellitus is one of the well-characterized autoimmune diseases. The mechanisms involved in the β-cell destruction are still not clear; it is generally believed that β-cell autoantigens, macrophages, dendritic cells, B lymphocytes, and T lymphocytes are involved in the β-cell-specific autoimmune process. It is necessary to determine what exact factors are causing the immune system to become unregulated in such a manner as to promote an autoimmune response.