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Classical gene-by-environment interaction (GxE) analysis can be used to characterize genetic effect heterogeneity but has a high multiple testing burden in the context of genome-wide association studies (GWAS). We adapt a colocalization method, SharePro, to account for effect heterogeneity in fine-mapping and identify candidates for GxE analysis with reduced multiple testing burden. SharePro demonstrates improved power for both fine-mapping and GxE analysis compared to existing methods as well as well-controlled false type I error in simulations. Using smoking status stratified GWAS summary statistics, we identify genetic effects on lung function modulated by smoking status that are not identified by existing methods. Additionally, using sex stratified GWAS summary statistics, we characterize sex differentiated genetic effects on fat distribution. In summary, we have developed an analytical framework to account for effect heterogeneity in fine-mapping and subsequently improve power for GxE analysis. The SharePro software for GxE analysis is openly available at https://github.com/zhwm/SharePro_gxe . Zhang et al. present an efficient method to simultaneously account for heterogeneity in fine-mapping and improve statistical power of gene-environment interaction analysis, with summary statistics from genome-wide association studies.

In observational studies, type-2 diabetes (T2D) is associated with an increased risk of coronary heart disease (CHD), yet interventional trials have shown no clear effect of glucose-lowering on CHD. Confounding may have therefore influenced these observational estimates. Here we use Mendelian randomization to obtain unconfounded estimates of the influence of T2D and fasting glucose (FG) on CHD risk. Using multiple genetic variants associated with T2D and FG, we find that risk of T2D increases CHD risk (odds ratio (OR)=1.11 (1.05–1.17), per unit increase in odds of T2D, P =8.8 × 10 −5 ; using data from 34,840/114,981 T2D cases/controls and 63,746/130,681 CHD cases/controls). FG in non-diabetic individuals tends to increase CHD risk (OR=1.15 (1.00–1.32), per mmol·per l, P =0.05; 133,010 non-diabetic individuals and 63,746/130,681 CHD cases/controls). These findings provide evidence supporting a causal relationship between T2D and CHD and suggest that long-term trials may be required to discern the effects of T2D therapies on CHD risk. In order to effectively design interventions, it is useful to understand the complex interplay between multiple syndromes. Here, Ahmad et al . use genome-wide association study data and Mendelian randomisation to examine the influence of Type 2 diabetes and fasting glucose levels on coronary heart disease.

Genome-wide association studies have revealed that common noncoding variants in MTNR1B (encoding melatonin receptor 1B, also known as MT2) increase type 2 diabetes (T2D) risk(1,2). Although the strongest association signal was highly significant (P < 1 x 10(-20)), its contribution to T2D risk was modest (odds ratio (OR) of similar to 1.10-1.15)(1-3). We performed large-scale exon resequencing in 7,632 Europeans, including 2,186 individuals with T2D, and identified 40 nonsynonymous variants, including 36 very rare variants (minor allele frequency (MAF) < 0.1%), associated with T2D (OR = 3.31, 95% confidence interval (CI) = 1.78-6.18; P = 1.64 x 10(-4)). A four-tiered functional investigation of all 40 mutants revealed that 14 were nonfunctional and rare (MAF < 1%), and 4 were very rare with complete loss of melatonin binding and signaling capabilities. Among the very rare variants, the partial-or total-loss-of-function variants but not the neutral ones contributed to T2D (OR = 5.67, CI = 2.17-14.82; P = 4.09 x 10(-4)). Genotyping the four complete loss-of-function variants in 11,854 additional individuals revealed their association with T2D risk (8,153 individuals with T2D and 10,100 controls; OR = 3.88, CI = 1.49-10.07; P = 5.37 x 10(-3)). This study establishes a firm functional link between MTNR1B and T2D risk.

Type 2 diabetes mellitus results from the interaction of environmental factors with a combination of genetic variants, most of which were hitherto unknown. A systematic search for these variants was recently made possible by the development of high-density arrays that permit the genotyping of hundreds of thousands of polymorphisms. We tested 392,935 single-nucleotide polymorphisms in a French case–control cohort. Markers with the most significant difference in genotype frequencies between cases of type 2 diabetes and controls were fast-tracked for testing in a second cohort. This identified four loci containing variants that confer type 2 diabetes risk, in addition to confirming the known association with the TCF7L2 gene. These loci include a non-synonymous polymorphism in the zinc transporter SLC30A8, which is expressed exclusively in insulin-producing β-cells, and two linkage disequilibrium blocks that contain genes potentially involved in β-cell development or function ( IDE–KIF11–HHEX and EXT2–ALX4 ). These associations explain a substantial portion of disease risk and constitute proof of principle for the genome-wide approach to the elucidation of complex genetic traits. Diabetes in the genes Overeating and physical inactivity are major causes of type 2 diabetes mellitus, but they affect only genetically susceptible individuals and the genetic basis of the disease is notoriously complex. Recent research has suggested that specific genes may be associated with the risk of developing the disease, however. Now a genome-wide search using high-density genotyping arrays has identified four previously unknown genes as diabetes risk factors, and confirmed a known association with the TCF7L2 gene. Together these five genes may contribute a sizeable fraction of the disease risk in type 2 diabetes, and analysis of their function should clarify the pathogenesis of diabetes and point to new drug targets. In addition, individuals shown to have these mutations could minimize their risk by adjusting diet. A survey of the entire human genome has found that five genetic loci contribute a large fraction of disease risk in type 2 diabetes.

Glioblastoma (GBM) is the most common and deadly malignant brain cancer of glial cell origin, with a median patient survival of less than 20 months. Transcription factors FOXG1 and TLE1 promote GBM propagation by supporting maintenance of brain tumour‐initiating cells (BTICs) with stem‐like properties. Here, we characterize FOXG1 and TLE1 target genes in GBM patient‐derived BTICs using ChIP‐Seq and RNA‐Seq approaches. These studies identify 150 direct FOXG1 targets, several of which are also TLE1 targets, involved in cell proliferation, differentiation, survival, chemotaxis and angiogenesis. Negative regulators of NOTCH signalling, including CHAC1, are among the transcriptional repression targets of FOXG1:TLE1 complexes, suggesting a crosstalk between FOXG1:TLE1 and NOTCH‐mediated pathways in GBM. These results provide previously unavailable insight into the transcriptional programs underlying the tumour‐promoting functions of FOXG1:TLE1 in GBM. Transcription factors FOXG1 and TLE1 promote glioblastoma (GBM) propagation. Here, we characterize FOXG1 and TLE1 target genes in GBM patient‐derived brain tumour‐initiating cells using ChIP‐Seq and RNA‐Seq approaches. These studies identify 150 direct FOXG1 targets, several of which are also TLE1 targets, involved in cell proliferation, differentiation, survival, chemotaxis and angiogenesis.

We report Brownian dynamics simulation results with the specific goal to identify key parameters controlling the experimentally measurable characteristics of protein tags on a dsDNA construct translocating through a double nanopore setup. First, we validate the simulation scheme in silico by reproducing and explaining the physical origin of the asymmetric experimental dwell time distributions of the oligonucleotide flap markers on a 48 kbp long dsDNA at the left and the right pore. We study the effect of the electric field inside and beyond the pores, critical to discriminate the protein tags based on their effective charges and masses revealed through a generic power-law dependence of the average dwell time at each pore. The simulation protocols monitor piecewise dynamics at a sub-nanometer length scale and explain the disparate velocity using the concepts of nonequilibrium tension propagation theory. We further justify the model and the chosen simulation parameters by calculating the Péclet number which is in close agreement with the experiment. We demonstrate that our carefully chosen simulation strategies can serve as a powerful tool to discriminate different types of neutral and charged tags of different origins on a dsDNA construct in terms of their physical characteristics and can provide insights to increase both the efficiency and accuracy of an experimental dual-nanopore setup.

Insulin Storage and Glucose Homeostasis in Mice Null for the Granule Zinc Transporter ZnT8 and Studies of the Type 2 Diabetes–Associated Variants Tamara J. Nicolson 1 , Elisa A. Bellomo 1 , Nadeeja Wijesekara 2 , Merewyn K. Loder 1 , Jocelyn M. Baldwin 3 , Armen V. Gyulkhandanyan 2 , Vasilij Koshkin 2 , Andrei I. Tarasov 1 , Raffaella Carzaniga 4 , Katrin Kronenberger 4 , Tarvinder K. Taneja 1 , Gabriela da Silva Xavier 1 , Sarah Libert 5 , Philippe Froguel 6 , 7 , Raphael Scharfmann 8 , Volodymir Stetsyuk 8 , Philippe Ravassard 9 , Helen Parker 10 , Fiona M. Gribble 10 , Frank Reimann 10 , Robert Sladek 11 , Stephen J. Hughes 12 , Paul R.V. Johnson 12 , Myriam Masseboeuf 13 , Remy Burcelin 13 , Stephen A. Baldwin 3 , Ming Liu 14 , Roberto Lara-Lemus 14 , Peter Arvan 14 , Frans C. Schuit 15 , Michael B. Wheeler 3 , Fabrice Chimienti 6 and Guy A. Rutter 1 1 Section of Cell Biology, Division of Medicine, Imperial College London, London, U.K.; 2 Department of Physiology, University of Toronto, Toronto, Canada; 3 Institute of Membrane and Systems Biology, University of Leeds, Leeds, U.K.; 4 Electron Microscopy Centre, Imperial College London, London, U.K.; 5 Mellitech, Grenoble, France; 6 Section of Genomic Medicine, Division of Medicine, Imperial College London, London, U.K.; 7 Centre National de la Recherche Scientifique Unite Mixte de Recherche 8090, Institute of Biology, Lille, France; 8 INSERM U845, University Paris Descartes, Paris, France; 9 Centre National de la Recherche Scientifique and Université Pierre et Marie Curie, Paris, France; 10 Cambridge Institute for Medical Research, University of Cambridge, Cambridge, U.K.; 11 Department of Human Genetics, McGill University, Montreal, Canada; 12 Nuffield Department of Surgery, University of Oxford, Oxfordshire, U.K.; 13 Institut de Medecine Moleculaire de Rangueil, INSERM U858, IFR31, Toulouse III University, CHU Rangueil, Toulouse Cedex, Toulouse, France; 14 Division of Metabolism, Endocrinology & Diabetes, University of Michigan Medical School, Ann Arbor, Michigan; 15 Gene Expression Unit, Department of Molecular Cell Biology, Katholieke Universiteit Leuven, Leuven, Belgium. Corresponding author: Guy A. Rutter, g.rutter{at}imperial.ac.uk . T.J.N., E.A.B., N.W., M.K.L., and J.M.B. contributed equally to this study. Abstract OBJECTIVE Zinc ions are essential for the formation of hexameric insulin and hormone crystallization. A nonsynonymous single nucleotide polymorphism rs13266634 in the SLC30A8 gene, encoding the secretory granule zinc transporter ZnT8, is associated with type 2 diabetes. We describe the effects of deleting the ZnT8 gene in mice and explore the action of the at-risk allele. RESEARCH DESIGN AND METHODS Slc30a8 null mice were generated and backcrossed at least twice onto a C57BL/6J background. Glucose and insulin tolerance were measured by intraperitoneal injection or euglycemic clamp, respectively. Insulin secretion, electrophysiology, imaging, and the generation of adenoviruses encoding the low- (W325) or elevated- (R325) risk ZnT8 alleles were undertaken using standard protocols. RESULTS ZnT8 −/− mice displayed age-, sex-, and diet-dependent abnormalities in glucose tolerance, insulin secretion, and body weight. Islets isolated from null mice had reduced granule zinc content and showed age-dependent changes in granule morphology, with markedly fewer dense cores but more rod-like crystals. Glucose-stimulated insulin secretion, granule fusion, and insulin crystal dissolution, assessed by total internal reflection fluorescence microscopy, were unchanged or enhanced in ZnT8 −/− islets. Insulin processing was normal. Molecular modeling revealed that residue-325 was located at the interface between ZnT8 monomers. Correspondingly, the R325 variant displayed lower apparent Zn 2+ transport activity than W325 ZnT8 by fluorescence-based assay. CONCLUSIONS ZnT8 is required for normal insulin crystallization and insulin release in vivo but not, remarkably, in vitro. Defects in the former processes in carriers of the R allele may increase type 2 diabetes risks. Footnotes The costs of publication of this article were defrayed in part by the payment of page charges. This article must therefore be hereby marked “advertisement” in accordance with 18 U.S.C. Section 1734 solely to indicate this fact. Received April 15, 2009. Accepted June 2, 2009. © 2009 by the American Diabetes Association.

Confinement of single molecules within nanoscale environments is crucial in a range of fields, including biomedicine, genomics, and biophysics. Here, we present a method that can concentrate, confine, and linearly stretch DNA molecules within a single optical field of view using dielectrophoretic (DEP) force. The method can convert an open surface into one confining DNA molecules without a requirement for bonding, hydrodynamic or mechanical components. We use a transverse DEP field between a top coverslip and a bottom substrate, both of which are coated with a transparent conductive material. Both layers are attached using double-sided tape, defining the chamber. The nanofeatures lie at the “floor” and do not require any bonding. With the application of an alternating (AC) electric field (2 V p-p ) between the top and bottom electrodes, a DEP field gradient is established and used to concentrate, confine and linearly extend DNA in nanogrooves as small as 100-nm in width. We also demonstrate reversible loading/unloading of DNA molecules into nanogrooves and nanopits by switching frequency (between 10 kHz to 100 kHz). The technology presented in this paper provides a new method for single-molecule trapping and analysis.

While the Arabian population has a high prevalence of metabolic disorders, it has not been included in global studies that identify genetic risk loci for metabolic traits. Determining the transferability of such largely Euro-centric established risk loci is essential to transfer the research tools/resources, and drug targets generated by global studies to a broad range of ethnic populations. Further, consideration of populations such as Arabs, that are characterized by consanguinity and a high level of inbreeding, can lead to identification of novel risk loci. We imputed published GWAS data from two Kuwaiti Arab cohorts (n = 1434 and 1298) to the 1000 Genomes Project haplotypes and performed meta-analysis for associations with 13 metabolic traits. We compared the observed association signals with those established for metabolic traits. Our study highlighted 70 variants from 9 different genes, some of which have established links to metabolic disorders. By relaxing the genome-wide significance threshold, we identified ‘novel’ risk variants from 11 genes for metabolic traits. Many novel risk variant association signals were observed at or borderline to genome-wide significance. Furthermore, 349 previously established variants from 187 genes were validated in our study. Pleiotropic effect of risk variants on multiple metabolic traits were observed. Fine-mapping illuminated rs7838666/CSMD1 rs1864163/CETP and rs112861901/[INTS10,LPL] as candidate causal variants influencing fasting plasma glucose and high-density lipoprotein levels. Computational functional analysis identified a variety of gene regulatory signals around several variants. This study enlarges the population ancestry diversity of available GWAS and elucidates new variants in an ethnic group burdened with metabolic disorders.

The recent development of whole genome association studies has lead to the robust identification of several loci involved in different common human diseases. Interestingly, some of the strongest signals of association observed in these studies arise from non-coding regions located in very large introns or far away from any annotated genes, raising the possibility that these regions are involved in the etiology of the disease through some unidentified regulatory mechanisms. These findings highlight the importance of better understanding the mechanisms leading to inter-individual differences in gene expression in humans. Most of the existing approaches developed to identify common regulatory polymorphisms are based on linkage/association mapping of gene expression to genotypes. However, these methods have some limitations, notably their cost and the requirement of extensive genotyping information from all the individuals studied which limits their applications to a specific cohort or tissue. Here we describe a robust and high-throughput method to directly measure differences in allelic expression for a large number of genes using the Illumina Allele-Specific Expression BeadArray platform and quantitative sequencing of RT-PCR products. We show that this approach allows reliable identification of differences in the relative expression of the two alleles larger than 1.5-fold (i.e., deviations of the allelic ratio larger than 60:40) and offers several advantages over the mapping of total gene expression, particularly for studying humans or outbred populations. Our analysis of more than 80 individuals for 2,968 SNPs located in 1,380 genes confirms that differential allelic expression is a widespread phenomenon affecting the expression of 20% of human genes and shows that our method successfully captures expression differences resulting from both genetic and epigenetic cis-acting mechanisms.