Gene variants influencing measures of inflammation or predisposing to autoimmune and inflammatory diseases are not associated with the risk of type 2 diabetes.

AIMS/HYPOTHESIS: There are strong associations between measures of inflammation and type 2 diabetes, but the causal directions of these associations are not known. We tested the hypothesis that common gene variants known to alter circulating levels of inflammatory proteins, or known to alter autoimmune-related disease risk, influence type 2 diabetes risk. METHODS: We selected 46 variants: (1) eight variants known to alter circulating levels of inflammatory proteins, including those in the IL18, IL1RN, IL6R, MIF, PAI1 (also known as SERPINE1) and CRP genes; and (2) 38 variants known to predispose to autoimmune diseases, including type 1 diabetes. We tested the associations of these variants with type 2 diabetes using a meta-analysis of 4,107 cases and 5,187 controls from the Wellcome Trust Case Control Consortium, the Diabetes Genetics Initiative, and the Finland-United States Investigation of NIDDM studies. We followed up associated variants (p < 0.01) in a further set of 3,125 cases and 3,596 controls from the UK. RESULTS: We found no evidence that inflammatory or autoimmune disease variants are associated with type 2 diabetes (at p <or= 0.01). The OR observed between the variant altering IL-18 levels, rs2250417, and type 2 diabetes (OR 1.00 [95% CI 0.99-1.03]), is much lower than that expected given (1) the effect of the variant on IL-18 levels (0.28 SDs per allele); and (2) estimates, based on other studies, of the correlation between IL-18 levels and type 2 diabetes risk (approximate OR 1.15 [95% CI 1.09-1.21] per 0.28 SD increase in IL-18 levels). CONCLUSIONS/INTERPRETATION: Our study provided no evidence that variants known to alter measures of inflammation, autoimmune or inflammatory disease risk, including type 1 diabetes, alter type 2 diabetes risk

An in-frame deletion at the polymerase active site of POLD1 causes a multisystem disorder with lipodystrophy

This is the author accepted manuscript. The final version is available from the publisher via the DOI in this record.DNA polymerase δ, whose catalytic subunit is encoded by POLD1, is responsible for lagging-strand DNA synthesis during DNA replication. It carries out this synthesis with high fidelity owing to its intrinsic 3'- to 5'-exonuclease activity, which confers proofreading ability. Missense mutations affecting the exonuclease domain of POLD1 have recently been shown to predispose to colorectal and endometrial cancers. Here we report a recurring heterozygous single-codon deletion in POLD1 affecting the polymerase active site that abolishes DNA polymerase activity but only mildly impairs 3'- to 5'-exonuclease activity. This mutation causes a distinct multisystem disorder that includes subcutaneous lipodystrophy, deafness, mandibular hypoplasia and hypogonadism in males. This discovery suggests that perturbing the function of the ubiquitously expressed POLD1 polymerase has unexpectedly tissue-specific effects in humans and argues for an important role for POLD1 function in adipose tissue homeostasis.This work was supported by NIHR Exeter Clinical Research Facility through funding for SE and ATH and general infrastructure. The authors thank Michael Day, Annet Damhuis and Richard Gilbert for technical assistance. We thank Karen Knapp for providing the data for the DEXA calculations. SE, ATH, SO are supported by Wellcome Weedon et al. Page 6 Nat Genet. Author manuscript; available in PMC 2014 February 01. Europe PMC Funders Author Manuscripts Europe PMC Funders Author Manuscripts Trust Senior Investigator awards. DS and RKS (098498/Z/12/Z) are supported by Wellcome Trust Senior Research Fellowships in Clinical Science. MNW is supported by the Wellcome Trust as part of the WT Biomedical Informatics Hub funding. RO is supported by Diabetes UK. DS, RKS and SO are supported by the UK National Institute for Health Research (NIHR) Cambridge Biomedical Research Centre. KJG is supported by the Agency for Science, Technology and Research, Singapore (A*STAR). LAL and MJP are supported by grants NCI-61-6845 and 62-4860

TEAD and YAP regulate the enhancer network of human embryonic pancreatic progenitors.

The genomic regulatory programmes that underlie human organogenesis are poorly understood. Pancreas development, in particular, has pivotal implications for pancreatic regeneration, cancer and diabetes. We have now characterized the regulatory landscape of embryonic multipotent progenitor cells that give rise to all pancreatic epithelial lineages. Using human embryonic pancreas and embryonic-stem-cell-derived progenitors we identify stage-specific transcripts and associated enhancers, many of which are co-occupied by transcription factors that are essential for pancreas development. We further show that TEAD1, a Hippo signalling effector, is an integral component of the transcription factor combinatorial code of pancreatic progenitor enhancers. TEAD and its coactivator YAP activate key pancreatic signalling mediators and transcription factors, and regulate the expansion of pancreatic progenitors. This work therefore uncovers a central role for TEAD and YAP as signal-responsive regulators of multipotent pancreatic progenitors, and provides a resource for the study of embryonic development of the human pancreas

Genetic Variants of Diabetes Risk and Incident Cardiovascular Events in Chronic Coronary Artery Disease

Objective: To determine whether information from genetic risk variants for diabetes is associated with cardiovascular events incidence. Methods: From the about 30 known genes associated with diabetes, we genotyped single-nucleotide polymorphisms at the 10 loci most associated with type-2 diabetes in 425 subjects from the MASS-II Study, a randomized study in patients with multi-vessel coronary artery disease. The combined genetic information was evaluated by number of risk alleles for diabetes. Performance of genetic models relative to major cardiovascular events incidence was analyzed through Kaplan-Meier curve comparison and Cox Hazard Models and the discriminatory ability of models was assessed for cardiovascular events by calculating the area under the ROC curve. Results: Genetic information was able to predict 5-year incidence of major cardiovascular events and overall-mortality in non-diabetic individuals, even after adjustment for potential confounders including fasting glycemia. Non-diabetic individuals with high genetic risk had a similar incidence of events then diabetic individuals (cumulative hazard of 33.0 versus 35.1% of diabetic subjects). The addition of combined genetic information to clinical predictors significantly improved the AUC for cardiovascular events incidence (AUC = 0.641 versus 0.610). Conclusions: Combined information of genetic variants for diabetes risk is associated to major cardiovascular events incidence, including overall mortality, in non-diabetic individuals with coronary artery disease.FAPESP[2007/54138-2

A genome-wide association study identifies protein quantitative trait loci (pQTLs)

There is considerable evidence that human genetic variation influences gene expression. Genome-wide studies have revealed that mRNA levels are associated with genetic variation in or close to the gene coding for those mRNA transcripts - cis effects, and elsewhere in the genome - trans effects. The role of genetic variation in determining protein levels has not been systematically assessed. Using a genome-wide association approach we show that common genetic variation influences levels of clinically relevant proteins in human serum and plasma. We evaluated the role of 496,032 polymorphisms on levels of 42 proteins measured in 1200 fasting individuals from the population based InCHIANTI study. Proteins included insulin, several interleukins, adipokines, chemokines, and liver function markers that are implicated in many common diseases including metabolic, inflammatory, and infectious conditions. We identified eight Cis effects, including variants in or near the IL6R (p = 1.8×10 -57), CCL4L1 (p = 3.9×10-21), IL18 (p = 6.8×10-13), LPA (p = 4.4×10-10), GGT1 (p = 1.5×10-7), SHBG (p = 3.1×10-7), CRP (p = 6.4×10-6) and IL1RN (p = 7.3×10-6) genes, all associated with their respective protein products with effect sizes ranging from 0.19 to 0.69 standard deviations per allele. Mechanisms implicated include altered rates of cleavage of bound to unbound soluble receptor (IL6R), altered secretion rates of different sized proteins (LPA), variation in gene copy number (CCL4L1) and altered transcription (GGT1). We identified one novel trans effect that was an association between ABO blood group and tumour necrosis factor alpha (TNF-alpha) levels (p = 6.8×10-40), but this finding was not present when TNF-alpha was measured using a different assay , or in a second study, suggesting an assay-specific association. Our results show that protein levels share some of the features of the genetics of gene expression. These include the presence of strong genetic effects in cis locations. The identification of protein quantitative trait loci (pQTLs) may be a powerful complementary method of improving our understanding of disease pathways. © 2008 Melzer et al

A rare SNP in pre-miR-34a is associated with increased levels of miR-34a in pancreatic beta cells.

Open Access Article.Changes in the levels of specific microRNAs (miRNAs) can reduce glucose-stimulated insulin secretion and increase beta-cell apoptosis, two causes of islet dysfunction and progression to type 2 diabetes. Studies have shown that single nucleotide polymorphisms (SNPs) within miRNA genes can affect their expression. We sought to determine whether miRNAs, with a known role in beta-cell function, possess SNPs within the pre-miRNA structure which can affect their expression. Using published literature and dbSNP, we aimed to identify miRNAs with a role in beta-cell function that also possess SNPs within the region encoding its pre-miRNA. Following transfection of plasmids, encoding the pre-miRNA and each allele of the SNP, miRNA expression was measured. Two rare SNPs located within the pre-miRNA structure of two miRNA genes important to beta-cell function (miR-34a and miR-96) were identified. Transfection of INS-1 and MIN6 cells with plasmids encoding pre-miR-34a and the minor allele of rs72631823 resulted in significantly (p < 0.05) higher miR-34a expression, compared to cells transfected with plasmids encoding the corresponding major allele. Similarly, higher levels were also observed upon transfection of HeLa cells. Transfection of MIN6 cells with plasmids encoding pre-miR-96 and each allele of rs41274239 resulted in no significant differences in miR-96 expression. A rare SNP in pre-miR-34a is associated with increased levels of mature miR-34a. Given that small changes in miR-34a levels have been shown to cause increased levels of beta-cell apoptosis this finding may be of interest to studies looking at determining the effect of rare variants on type 2 diabetes susceptibility

Extent of Height Variability Explained by Known Height-Associated Genetic Variants in an Isolated Population of the Adriatic Coast of Croatia

BACKGROUND: Human height is a classical example of a polygenic quantitative trait. Recent large-scale genome-wide association studies (GWAS) have identified more than 200 height-associated loci, though these variants explain only 2∼10% of overall variability of normal height. The objective of this study was to investigate the variance explained by these loci in a relatively isolated population of European descent with limited admixture and homogeneous genetic background from the Adriatic coast of Croatia. METHODOLOGY/PRINCIPAL FINDINGS: In a sample of 1304 individuals from the island population of Hvar, Croatia, we performed genome-wide SNP typing and assessed the variance explained by genetic scores constructed from different panels of height-associated SNPs extracted from five published studies. The combined information of the 180 SNPs reported by Lango Allen el al. explained 7.94% of phenotypic variation in our sample. Genetic scores based on 20~50 SNPs reported by the remaining individual GWA studies explained 3~5% of height variance. These percentages of variance explained were within ranges comparable to the original studies and heterogeneity tests did not detect significant differences in effect size estimates between our study and the original reports, if the estimates were obtained from populations of European descent. CONCLUSIONS/SIGNIFICANCE: We have evaluated the portability of height-associated loci and the overall fitting of estimated effect sizes reported in large cohorts to an isolated population. We found proportions of explained height variability were comparable to multiple reference GWAS in cohorts of European descent. These results indicate similar genetic architecture and comparable effect sizes of height loci among populations of European descent

Hundreds of variants clustered in genomic loci and biological pathways affect human height

Most common human traits and diseases have a polygenic pattern of inheritance: DNA sequence variants at many genetic loci influence the phenotype. Genome-wide association (GWA) studies have identified more than 600 variants associated with human traits, but these typically explain small fractions of phenotypic variation, raising questions about the use of further studies. Here, using 183,727 individuals, we show that hundreds of genetic variants, in at least 180 loci, influence adult height, a highly heritable and classic polygenic trait. The large number of loci reveals patterns with important implications for genetic studies of common human diseases and traits. First, the 180 loci are not random, but instead are enriched for genes that are connected in biological pathways (P = 0.016) and that underlie skeletal growth defects (P < 0.001). Second, the likely causal gene is often located near the most strongly associated variant: in 13 of 21 loci containing a known skeletal growth gene, that gene was closest to the associated variant. Third, at least 19 loci have multiple independently associated variants, suggesting that allelic heterogeneity is a frequent feature of polygenic traits, that comprehensive explorations of already-discovered loci should discover additional variants and that an appreciable fraction of associated loci may have been identified. Fourth, associated variants are enriched for likely functional effects on genes, being over-represented among variants that alter amino-acid structure of proteins and expression levels of nearby genes. Our data explain approximately 10% of the phenotypic variation in height, and we estimate that unidentified common variants of similar effect sizes would increase this figure to approximately 16% of phenotypic variation (approximately 20% of heritable variation). Although additional approaches are needed to dissect the genetic architecture of polygenic human traits fully, our findings indicate that GWA studies can identify large numbers of loci that implicate biologically relevant genes and pathways.

A Methodological Perspective on Genetic Risk Prediction Studies in Type 2 Diabetes: Recommendations for Future Research

Fueled by the successes of genome-wide association studies, numerous studies have investigated the predictive ability of genetic risk models in type 2 diabetes. In this paper, we review these studies from a methodological perspective, focusing on the variables included in the risk models as well as the study designs and populations investigated. We argue and show that differences in study design and characteristics of the study population have an impact on the observed predictive ability of risk models. This observation emphasizes that genetic risk prediction studies should be conducted in those populations in which the prediction models will ultimately be applied, if proven useful. Of all genetic risk prediction studies to date, only a few were conducted in populations that might be relevant for targeting preventive interventions

From Disease Association to Risk Assessment: An Optimistic View from Genome-Wide Association Studies on Type 1 Diabetes

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