Genetics Of Type 2 Diabetes Research Articles

Validating and automating learning of cardiometabolic polygenic risk scores from direct-to-consumer genetic and phenotypic data: implications for scaling precision health research

IntroductionA major challenge to enabling precision health at a global scale is the bias between those who enroll in state sponsored genomic research and those suffering from chronic disease. More than 30 million people have been genotyped by direct-to-consumer (DTC) companies such as 23andMe, Ancestry DNA, and MyHeritage, providing a potential mechanism for democratizing access to medical interventions and thus catalyzing improvements in patient outcomes as the cost of data acquisition drops. However, much of these data are sequestered in the initial provider network, without the ability for the scientific community to either access or validate. Here, we present a novel geno-pheno platform that integrates heterogeneous data sources and applies learnings to common chronic disease conditions including Type 2 diabetes (T2D) and hypertension.MethodsWe collected genotyped data from a novel DTC platform where participants upload their genotype data files and were invited to answer general health questionnaires regarding cardiometabolic traits over a period of 6 months. Quality control, imputation, and genome-wide association studies were performed on this dataset, and polygenic risk scores were built in a case–control setting using the BASIL algorithm.ResultsWe collected data on N = 4,550 (389 cases / 4,161 controls) who reported being affected or previously affected for T2D and N = 4,528 (1,027 cases / 3,501 controls) for hypertension. We identified 164 out of 272 variants showing identical effect direction to previously reported genome-significant findings in Europeans. Performance metric of the PRS models was AUC = 0.68, which is comparable to previously published PRS models obtained with larger datasets including clinical biomarkers.DiscussionDTC platforms have the potential of inverting research models of genome sequencing and phenotypic data acquisition. Quality control (QC) mechanisms proved to successfully enable traditional GWAS and PRS analyses. The direct participation of individuals has shown the potential to generate rich datasets enabling the creation of PRS cardiometabolic models. More importantly, federated learning of PRS from reuse of DTC data provides a mechanism for scaling precision health care delivery beyond the small number of countries who can afford to finance these efforts directly.ConclusionsThe genetics of T2D and hypertension have been studied extensively in controlled datasets, and various polygenic risk scores (PRS) have been developed. We developed predictive tools for both phenotypes trained with heterogeneous genotypic and phenotypic data generated outside of the clinical environment and show that our methods can recapitulate prior findings with fidelity. From these observations, we conclude that it is possible to leverage DTC genetic repositories to identify individuals at risk of debilitating diseases based on their unique genetic landscape so that informed, timely clinical interventions can be incorporated.

The missing heritability in type 1 diabetes.

Type 1 diabetes (T1D) is a complex autoimmune disease characterized by an absolute deficiency of insulin. It affects more than 20 million people worldwide and imposes an enormous financial burden on patients. The underlying pathogenic mechanisms of T1D are still obscure, but it is widely accepted that both genetics and the environment play an important role in its onset and development. Previous studies have identified more than 60 susceptible loci associated with T1D, explaining approximately 80%‐85% of the heritability. However, most identified variants confer only small increases in risk, which restricts their potential clinical application. In addition, there is still a so‐called ‘missing heritability’ phenomenon. While the gap between known heritability and true heritability in T1D is small compared with that in other complex traits and disorders, further elucidation of T1D genetics has the potential to bring novel insights into its aetiology and provide new therapeutic targets. Many hypotheses have been proposed to explain the missing heritability, including variants remaining to be found (variants with small effect sizes, rare variants and structural variants) and interactions (gene–gene and gene–environment interactions; e.g. epigenetic effects). In the following review, we introduce the possible sources of missing heritability and discuss the existing related knowledge in the context of T1D.

The Genetics of Type 2 Diabetes in Youth: Where We Are and the Road Ahead

The Genetics of Type 2 Diabetes in Youth: Where We Are and the Road Ahead

294-OR: The Type 1 Diabetes Protective Variant of Tyrosine Kinase 2 (TYK2) Protects ß-Cells from Autoimmunity

Type 1 diabetes (T1D) is a complex autoimmune disease where genes and environmental factors impact pathogenesis. Analysis of the >150 loci identified by GWAS reveals the compelling theme that candidate genes cluster into pathways. A pathway connecting environment to T1D genetics is the anti-viral/Type 1 interferon (IFN1) pathway that is regulated by at least 7 T1D linked loci, including Tyrosine Kinase 2 (TYK2) which regulates IFN1 receptor signaling. We test the hypothesis that the T1D protective variant of TYK2 restrains signaling though the IFN1 receptor functions in β-cells reduce interactions with and killing by autoreactive, cytolytic CD8+ T lymphocytes (CTL) . These studies employed β-cells edited in the endogenous TYK2 locus with CRISPR/Cas9 to express the T1D protective TYK2110A or common allotype, TYK2110P. These cells were treated with Poly I:C (50ng/mL) , to see IFN1 response to an imitated viral infection. Outcomes were IFN1 production, gene expression, antigen presentation, and responses to CTL. When allotypic β-cells were treated with Poly I:C, both produced equivalent levels of IFN1. While β-cells with TYK2110P showed increased mechanisms of antigen presentation, this pathway was attenuated in TYK2110A β-cells (p<0.05) . These results were replicated by treating TYK2110P and TYK2110A IFN1 (100U/mL) , where we observed increased antigen presentation in TYK2110P β-cells but not TYK2110A β-cells (p<0.05) . Interestingly, prior to treatment cell surface human leukocyte antigen class I levels were reduced in TYK2110A β-cells (p<0.01) . Studies to test the interactions of autoreactive CTL with β-cells supported our hypothesis. TYK2110P β-cells were efficiently lysed by CTL and lysis was increased by pre-treatment of β-cells with IFN1 or PolyI:C. In stark contrast, TYK2110A β-cells exhibited low levels of CTL-lysis and pre-treatment with IFN1 or PolyI:C did not modify lysis. In conclusion, TYK2110A protects β-cells from autoimmune-mediated death. Disclosure A.Rodriguez tamayo: None. M.Huber: None. E.J.Butfiloski: None. C.E.Mathews: None. Funding National Institutes of Health PAI42288National Institutes of Health RR01DK127497

Segregation Analysis of Genotyped and Family-Phased, Long Range MHC Classical Class I and Class II Haplotypes in 5 Families With Type 1 Diabetes Proband in the United Arab Emirates.

The classical Human Leucocyte Antigen (HLA) class II haplotypes of the Major Histocompatibility Complex (MHC) that are associated with type 1 diabetes (T1D) were identified in five families from the United Arab Emirates (UAE). Segregation analyses were performed on these 5 families with the disease, 3 with one child and 2 with 2 children diagnosed with T1D. Three HLA-DR4 haplotypes were identified: HLA- DRB1∗04:01:01-DQB1∗03:02:01:01; HLA- DRB1∗04:02:01- DQB1∗03:02:01; and HLA -DRB1∗04:05:01-DQB1∗02:02:01:02. All have previously been identified to be associated with T1D in studies of the Arabian population. In the 10 parents from the 5 families, 9 had at least one HLA-DR4 and HLA-DR3 haplotype which potentially increases the risk of T1D. Of these 9 parents, 3 were heterozygous for HLA-DR4/HLA-DR3 and one was homozygous for HLA-DR3. Two haplotypes that were identified here extend to the HLA class I region were previously designated AH8.2 (HLA -A∗26-B∗08-DRB1∗03) and AH50.2 (HLA -C∗06-B∗50-DRB1∗03:01-DQ∗02) and associated with diabetes in neighboring North Indian populations. This study provides examples of MHC haplotype analysis in pedigrees to improve our understanding of the genetics of T1D in the understudied population of the UAE.

Genome-wide association analysis of type 2 diabetes in the EPIC-InterAct study

Type 2 diabetes (T2D) is a global public health challenge. Whilst the advent of genome-wide association studies has identified >400 genetic variants associated with T2D, our understanding of its biological mechanisms and translational insights is still limited. The EPIC-InterAct project, centred in 8 countries in the European Prospective Investigations into Cancer and Nutrition study, is one of the largest prospective studies of T2D. Established as a nested case-cohort study to investigate the interplay between genetic and lifestyle behavioural factors on the risk of T2D, a total of 12,403 individuals were identified as incident T2D cases, and a representative sub-cohort of 16,154 individuals was selected from a larger cohort of 340,234 participants with a follow-up time of 3.99 million person-years. We describe the results from a genome-wide association analysis between more than 8.9 million SNPs and T2D risk among 22,326 individuals (9,978 cases and 12,348 non-cases) from the EPIC-InterAct study. The summary statistics to be shared provide a valuable resource to facilitate further investigations into the genetics of T2D.

ZRANB3 is an African-specific type 2 diabetes locus associated with beta-cell mass and insulin response

Genome analysis of diverse human populations has contributed to the identification of novel genomic loci for diseases of major clinical and public health impact. Here, we report a genome-wide analysis of type 2 diabetes (T2D) in sub-Saharan Africans, an understudied ancestral group. We analyze ~18 million autosomal SNPs in 5,231 individuals from Nigeria, Ghana and Kenya. We identify a previously-unreported genome-wide significant locus: ZRANB3 (Zinc Finger RANBP2-Type Containing 3, lead SNP p = 2.831 × 10−9). Knockdown or genomic knockout of the zebrafish ortholog results in reduction in pancreatic β-cell number which we demonstrate to be due to increased apoptosis in islets. siRNA transfection of murine Zranb3 in MIN6 β-cells results in impaired insulin secretion in response to high glucose, implicating Zranb3 in β-cell functional response to high glucose conditions. We also show transferability in our study of 32 established T2D loci. Our findings advance understanding of the genetics of T2D in non-European ancestry populations.

Genes Expression in Type 1 Diabetes: An Update

Type 1 Diabetes (T1D) is autoimmune disease with a sturdy genetic component, which, through interactions with particular environmental factors, causes disease onset. T1D usually reveals in early to mid-childhood through the autoimmune destruction of pancreatic cells resulting in a lack of insulin production. Traditionally, prior to genome-wide association studies (GWAS), six loci in the genome were fully established to be associated with T1D. The originations of genetic factors involved in T1D through GWAS present the first step in a long process leading to cure. Genes uncovered using this approach is indeed necessary to disease biology and will define the key molecular pathways leading to cure of T1D. However, such genome wide scans can lack coverage in certain regions where it is difficult to , thus, it is possible that other loci with practical effect sizes remain to be uncovered through whole genome sequencing approaches. In this review, we address recent expansions in the genetics of T1D and provide an update on the latest predisposition loci added to the list of genes involved in the of T1D

Genetic Approaches to the Study of Gene Variants and Their Impact on the Pathophysiology of Type 2 Diabetes.

Diabetes mellitus is an incurable progressive disease, characterized by elevated blood glucose levels, which lead to the development of micro- and macrovascular complications. Although the etiopathology of the disease remains unclear, it seems to be multifactorial, with an important interaction between genetics and environmental causes. Currently, the genetics of type 2 diabetes (T2D) is poorly understood. The recent advance of the genetic technologies and with a better understanding of genetics, more than 120 distinct genetic loci, with more than 150 variants, have been identified that may be involved in the pathogenesis of T2D. However, as these variants can account for only approximately 20% of the heritability of T2D, there is an obvious need for additional approaches to identify susceptibility genes or genetic mechanisms involved in the development of this disease. There is a growing number of genes found to be related to T2D; however, their individual impact on the pathogenesis of the disease appears to be low, while silencing of protective genes may also contribute to the development of this disease. The present review attempts to summarize our current knowledge in the field of genetics of T2D, highlighting the possible practical applications for each approach.

Genetics of type 1 diabetes.

Type 1 diabetes (T1D) results from immune-mediated loss of pancreatic beta cells leading to insulin deficiency. It is the most common form of diabetes in children, and its incidence is on the rise. This article reviews the current knowledge on the genetics of T1D. In particular, we discuss the influence of HLA and non-HLA genes on T1D risk and disease progression through the preclinical stages of the disease, and the development of genetic scores that can be applied to disease prediction. Racial/ethnic differences, challenges and future directions in the genetics of T1D are also discussed.

Genetic Risk Scores for Type 1 Diabetes Prediction and Diagnosis.

About 50% of the heritability of type 1 diabetes (T1D) is attributed to human leukocyte antigen (HLA) alleles and the remainder to several (close to 50) non-HLA loci. A current challenge in the field of the genetics of T1D is to apply the knowledge accumulated in the last 40years towards differential diagnosis and risk assessment. T1D genetic risk scores seek to combine the information from HLA and non-HLA alleles to improve the accuracy of diagnosis, prediction, and prognosis. Here, we describe genetic risk scores that have been developed and validated in various settings and populations. Several genetic scores have been proposed that merge disease risk information from multiple genetic factors to optimize the use of genetic information and ultimately improve prediction and diagnosis of T1D.

Analysis of population-specific pharmacogenomic variants using next-generation sequencing data

Functional rare variants in drug-related genes are believed to be highly differentiated between ethnic- or racial populations. However, knowledge of population differentiation (PD) of rare single-nucleotide variants (SNVs), remains widely lacking, with the highest fixation indices, (Fst values), from both rare and common variants annotated to specific genes, having only been marginally used to understand PD at the gene level. In this study, we suggest a new, gene-based PD method, PD of Rare and Common variants (PDRC), for analyzing rare variants, as inspired by Generalized Cochran-Mantel-Haenszel (GCMH) statistics, to identify highly population-differentiated drug response-related genes (“pharmacogenes”). Through simulation studies, we reveal that PDRC adequately summarizes rare and common variants, due to PD, over a specific gene. We also applied the proposed method to a real whole-exome sequencing dataset, consisting of 10,000 datasets, from the Type 2 Diabetes Genetic Exploration by Next-generation sequencing in multi-Ethnic Samples (T2D-GENES) initiative, and 3,000 datasets from the Genetics of Type 2 diabetes (Go-T2D) repository. Among the 48 genes annotated with Very Important Pharmacogenetic summaries (VIPgenes), in the PharmGKB database, our PD method successfully identified candidate genes with high PD, including ACE, CYP2B6, DPYD, F5, MTHFR, and SCN5A.

Sibling method increases risk assessment estimates for type 1 diabetes.

We presented a risk assessment model to distinguish between type 1 diabetes (T1D) affected and unaffected siblings using only three single nucleotide polymorphism (SNP) genotypes. In addition we calculated the heritability from genome-wide identity-by-descent (IBD) sharing between full siblings. We analyzed 1,253 pairs of affected individuals and their unaffected siblings (750 pairs from a discovery set and 503 pairs from a validation set) from the T1D Genetics Consortium (T1DGC), applying a logistic regression to analyze the area under the receiver operator characteristic (ROC) curve (AUC). To calculate the heritability of T1D we used the Haseman-Elston regression analysis of the squared difference between the phenotypes of the pairs of siblings on the estimate of their genome-wide IBD proportion. The model with only 3 SNPs achieving an AUC of 0.75 in both datasets outperformed the model using the presence of the high-risk DR3/4 HLA genotype, namely AUC of 0.60. The heritability on the liability scale of T1D was approximately from 0.53 to 0.92, close to the results obtained from twin studies, ranging from 0.4 to 0.88.

Genetics of Type 2 Diabetes: the Power of Isolated Populations.

Type 2 diabetes (T2D) affects millions of people worldwide. Improving the understanding of the underlying mechanisms and ultimately improving the treatment strategies are, thus, of great interest. To achieve this, identification of genetic variation predisposing to T2D is important. A large number of variants have been identified in large outbred populations, mainly from Europe and Asia. However, to elucidate additional variation, isolated populations have a number of advantageous properties, including increased amounts of linkage disequilibrium, and increased probability for presence of high frequency disease-associated variants due to genetic drift. Collectively, this increases the statistical power to detect association signals in isolated populations compared to large outbred populations. In this review, we elaborate on why isolated populations are a powerful resource for the identification of complex disease variants and describe their contributions to the understanding of the genetics of T2D.

Role of Human Leucocyte Antigen (HLA) Class II Gene Polymorphism in Type 1 Diabetes Mellitus

Background : Type 1 diabetes (T1D) is a multifactorial disease caused by a complex interaction of genetic and environmental factors, which makes identification of disease causing variants very difficult. Knowledge of the genetics of T1D would allow better disease definition and improved ability to identify individual's risks. Objectives : Identification of the Human Leukocyte Antigen gene polymorphism in T1D Mellitus and its relationship to maternal and paternal gene polymorphism. Methodology : This study included 20 children (with their parents) with Type 1 Diabetes Mellitus who had regular follow up in the Paediatric Endocrinology Outpatient’s Clinic Children Hospital, Minia University; during the period from June 2014 to March 2015. The studied groups were subjected to the followings; History taking, general and systemic examination and Real time PCR for detection of (DQB1* 0102, DQB1* 0302, DRB1*04) for the parents and the studied diabetic children. Results : Thorough history taking, general and systemic examination and RT- PCR for detection of (DQB1* 0102, DQB1* 0302, DRB1*04) for the parents and the studied diabetic children. Ten (50%) of the studied group had DQB1*0102 Gene versus 12 (60%) of their fathers and 11 (55%) of their mothers. Concerning DQB1*0302 Gene, 15 (75%) of the diabetic patients had it versus16 (80%) of their fathers and 15 (75%) of their mothers. Lastly, as regard DRB1*04 Gene 14 (70%) of the diabetic patients were positive versus 13 (65%) of their fathers and 11 (55%) of their mothers. There is a great importance of avoiding exposure to the different predisposing factors which may precipitate early onset of diabetes. Conclusion: The genetic makeup could determines the age of onset of type 1 diabetes. There is an importance of avoiding exposure to the different predisposing factors which may precipitate early onset of diabetes, this may help to delay it especially if it is genetically determined.

The (in)famous GWAS P-value threshold revisited and updated for low-frequency variants.

Genome-wide association studies (GWAS) have long relied on proposed statistical significance thresholds to be able to differentiate true positives from false positives. Although the genome-wide significance P-value threshold of 5 × 10(-8) has become a standard for common-variant GWAS, it has not been updated to cope with the lower allele frequency spectrum used in many recent array-based GWAS studies and sequencing studies. Using a whole-genome- and -exome-sequencing data set of 2875 individuals of European ancestry from the Genetics of Type 2 Diabetes (GoT2D) project and a whole-exome-sequencing data set of 13 000 individuals from five ancestries from the GoT2D and T2D-GENES (Type 2 Diabetes Genetic Exploration by Next-generation sequencing in multi-Ethnic Samples) projects, we describe guidelines for genome- and exome-wide association P-value thresholds needed to correct for multiple testing, explaining the impact of linkage disequilibrium thresholds for distinguishing independent variants, minor allele frequency and ancestry characteristics. We emphasize the advantage of studying recent genetic isolate populations when performing rare and low-frequency genetic association analyses, as the multiple testing burden is diminished due to higher genetic homogeneity.

Evaluating the Calibration and Power of Three Gene-Based Association Tests of Rare Variants for the X Chromosome.

Although genome-wide association studies (GWAS) have identified thousands of trait-associated genetic variants, there are relatively few findings on the X chromosome. For analysis of low-frequency variants (minor allele frequency <5%), investigators can use region- or gene-based tests where multiple variants are analyzed jointly to increase power. To date, there are no gene-based tests designed for association testing of low-frequency variants on the X chromosome. Here we propose three gene-based tests for the X chromosome: burden, sequence kernel association test (SKAT), and optimal unified SKAT (SKAT-O). Using simulated case-control and quantitative trait (QT) data, we evaluate the calibration and power of these tests as a function of (1) male:female sample size ratio; and (2) coding of haploid male genotypes for variants under X-inactivation. For case-control studies, all three tests are reasonably well-calibrated for all scenarios we evaluated. As expected, power for gene-based tests depends on the underlying genetic architecture of the genomic region analyzed. Studies with more (haploid) males are generally less powerful due to decreased number of chromosomes. Power generally is slightly greater when the coding scheme for male genotypes matches the true underlying model, but the power loss for misspecifying the (generally unknown) model is small. For QT studies, type I error and power results largely mirror those for binary traits. We demonstrate the use of these three gene-based tests for X-chromosome association analysis in simulated data and sequencing data from the Genetics of Type 2 Diabetes (GoT2D) study.

The Genetic Architecture of Type 2 Diabetes Pharmacotherapy: The Emerging Genomic Evidence

Response to anti-diabetic medications is not always predictable or favourable even in phenotypically similar type-2 diabetes (T2D) cases. This is not only due to patient's compliance and access to care but is also considered to be an effect of idiosyncratic differences among individuals, stemming from the combination of their unique genetic background and environmental exposures. In this systematic review, we aimed to summarise the available evidence on pharmacogenetic and pharmacogenomic studies of oral agents for T2D that are currently in the market and describe the agents studied, the targeted loci in regards to the efficacy and the toxicity profile of the agents included. We included 53 studies published between 2003-2012, which examined the following anti-diabetic classes: sulphonylureas, metformin, metiglinides and thiazolidenediones. There were no published studies on newer agents (e.g. incretin based treatments). Forty-nine studies (92.5%) examined the therapeutic response to oral antiglycaemic agents. Outcomes assessed included changes in metabolic markers (fasting or postprandial blood glucose, fasting or postprandial insulin, HbA1c), Homeostasis Assessment Model (HOMA)-Insulin Resistance (IR) or HOMA-B-cell function (HOMA-B), and time to monotherapy failure. Regarding side effects, hypoglycaemia and TZD-related oedema were the most commonly assessed. In the vast majority of the studies included (n=38, 71.7%), more than one outcomes (n=27, 50.9%) and/or more than one SNPs (n=21, 39.6%) were evaluated in the same publication, but most studies examined one drug (n=50, 94.3%). A considerable number of the proposed genes seem to be related to beta-cell development and function, but there are several genes whose underlying pathway linked to diabetes pharmacotherapy remains poorly understood. Pharmacogenomics are still not in pace with the wealth of information provided by GWAS in the genetics of T2D and related traits and the proposed associations need further validation in well-characterized large studies of varying ancestral origins.

Type 1 diabetes in mice and men: gene expression profiling to investigate disease pathogenesis

Type 1 diabetes (T1D) is a complex polygenic disease that is triggered by various environmental factors in genetically susceptible individuals. The emphasis placed on genome-wide association studies to explain the genetics of T1D has failed to advance our understanding of T1D pathogenesis or identify biomarkers of disease progression or therapeutic targets. Using the nonobese diabetic (NOD) mouse model of T1D and the non-disease prone congenic NOD.B10 mice, our laboratory demonstrated striking tissue-specific and age-dependent changes in gene expression during disease progression. We established a "roadmap" of differential gene expression and used this to identify candidate genes in mice (and human orthologs) that play a role in disease pathology. Here, we describe two genes, Deformed epidermal autoregulatory factor 1 (Deaf1) and Adenosine A1 receptor (Adora1), that are differentially expressed and alternatively spliced in the pancreatic lymph nodes or islets of NOD mice and T1D patients to form dominant-negative non-functional isoforms. Loss of Deaf1 function leads to reduced peripheral tissue antigen expression in lymph node stromal cells and may contribute to a breakdown in peripheral tolerance, while reduced Adora1 function results in an early intrinsic alpha cell defect that may explain the hyperglucagonemia and resulting beta cell stress observed prior to the onset of diabetes. Remarkably, both genes were also alternatively spliced in the same tissues of auto-antibody positive prediabetic patients, and these splicing events resulted in similar downstream effects as those seen in NOD mice. These findings demonstrate the value of gene expression profiling in studying disease pathogenesis in T1D.

Genes Involved in Type 1 Diabetes: An Update

Type 1 Diabetes (T1D) is a chronic multifactorial disease with a strong genetic component, which, through interactions with specific environmental factors, triggers disease onset. T1D typically manifests in early to mid childhood through the autoimmune destruction of pancreatic β cells resulting in a lack of insulin production. Historically, prior to genome-wide association studies (GWAS), six loci in the genome were fully established to be associated with T1D. With the advent of high-throughput single nucleotide polymorphism (SNP) genotyping array technologies, enabling investigators to perform high-density GWAS, many additional T1D susceptibility genes have been discovered. Indeed, recent meta-analyses of multiple datasets from independent investigators have brought the tally of well-validated T1D disease genes to almost 60. In this mini-review, we address recent advances in the genetics of T1D and provide an update on the latest susceptibility loci added to the list of genes involved in the pathogenesis of T1D.