To characterize type 2 diabetes (T2D)-associated variation across the allele frequency spectrum, we conducted a meta-analysis of genome-wide association data from 26,676 T2D case and 132,532 control subjects of European ancestry after imputation using the 1000 Genomes multiethnic reference panel. Promising association signals were followed up in additional data sets (of 14,545 or 7,397 T2D case and 38,994 or 71,604 control subjects). We identified 13 novel T2D-associated loci (P < 5 × 10⁻⁸), including variants near the GLP2R, GIP, and HLA-DQA1 genes. Our analysis brought the total number of independent T2D associations to 128 distinct signals at 113 loci. Despite substantially increased sample size and more complete coverage of low-frequency variation, all novel associations were driven by common single nucleotide variants. Credible sets of potentially causal variants were generally larger than those based on imputation with earlier reference panels, consistent with resolution of causal signals to common risk haplotypes. Stratification of T2D-associated loci based on T2D-related quantitative trait associations revealed tissue-specific enrichment of regulatory annotations in pancreatic islet enhancers for loci influencing insulin secretion and in adipocytes, monocytes, and hepatocytes for insulin action–associated loci. These findings highlight the predominant role played by common variants of modest effect and the diversity of biological mechanisms influencing T2D pathophysiology.

Motivation: As the study of information processing in living cells moves from individual pathways to complex regulatory networks, mathematical models and simulation become indispensable tools for analyzing the complex behavior of such networks and can provide deep insights into the functioning of cells. The dynamics of gene expression, for example, can be modeled with Boolean networks (BNs). These are mathematical models of low complexity, but have the advantage of being able to capture essential properties of gene-regulatory networks. However, current implementations of BNs only focus on different sub-aspects of this model and do not allow for a seamless integration into existing preprocessing pipelines. Results:BoolNet efficiently integrates methods for synchronous, asynchronous and probabilistic BNs. This includes reconstructing networks from time series, generating random networks, robustness analysis via perturbation, Markov chain simulations, and identification and visualization of attractors. Availability: The package BoolNet is freely available from the R project at http://cran.r-project.org/ or http://www.informatik.uni-ulm.de/ni/mitarbeiter/HKestler/boolnet/ under Artistic License 2.0. Contact:hans.kestler@uni-ulm.de Supplementary information: Supplementary data are available at Bioinformatics online.

Aims Because of its high spatial resolution and tissue contrast, magnetic resonance imaging (MRI) was used to assess cardiac structure and function in a large population of patients with acute myocardial infarction (AMI). Methods and results One hundred and ten patients were studied by MRI 6.1±2.2 days after AMI. Infarct size (IS), persistent microvascular obstruction (PMO), left and right ventricular (LV/RV) volumes, and functions were measured. The same MRI measurements were repeated in 89 patients after a mean follow-up period of 225±92 days. IS was 11.9±7.3% of total LV muscle mass. PMO was detected in 51/110 (46.4%) patients and comprised 15.6±8.5% of IS and 2.8±2.3% of LV muscle mass. Papillary muscle infarct was seen in 26%, RV infarction in 16%, pericarditis in 40%, and pericardial effusion in 66% of the patients. During follow-up, there were 16 major adverse cardiac events (MACE) including seven deaths. IS, PMO, and amount of transmural infarction were predictive for LV adverse remodelling defined as >20% increase in LV end-diastolic volume. Multivariable analysis revealed LV end-diastolic volume, LV ejection fraction, and PMO as significant predictors for the occurrence of MACE. Conclusion MRI is a highly sensitive and reliable tool to detect morphologic and functional sequelae of AMI providing baseline MRI parameters with relevant predictive power for LV adverse remodelling and occurrence of MACE.

Kyle Gaulton, Mark McCarthy, Andrew Morris and colleagues report fine mapping and genomic annotation of 39 established type 2 diabetes susceptibility loci. They find that the set of potential causal variants is enriched for overlap with FOXA2 binding sites in human islet and liver cells, and they show that a likely causal variant near MTNR1B increases FOXA2-bound enhancer activity, providing a molecular mechanism to explain the effect of this locus on disease risk. We performed fine mapping of 39 established type 2 diabetes (T2D) loci in 27,206 cases and 57,574 controls of European ancestry. We identified 49 distinct association signals at these loci, including five mapping in or near KCNQ1. 'Credible sets' of the variants most likely to drive each distinct signal mapped predominantly to noncoding sequence, implying that association with T2D is mediated through gene regulation. Credible set variants were enriched for overlap with FOXA2 chromatin immunoprecipitation binding sites in human islet and liver cells, including at MTNR1B, where fine mapping implicated rs10830963 as driving T2D association. We confirmed that the T2D risk allele for this SNP increases FOXA2-bound enhancer activity in islet- and liver-derived cells. We observed allele-specific differences in NEUROD1 binding in islet-derived cells, consistent with evidence that the T2D risk allele increases islet MTNR1B expression. Our study demonstrates how integration of genetic and genomic information can define molecular mechanisms through which variants underlying association signals exert their effects on disease.