Background Measurement of myocardial iron is key to the clinical management of patients at risk of siderotic cardiomyopathy. The cardiovascular magnetic resonance (CMR) relaxation parameter R2* (assessed clinically via its reciprocal T2*) measured in the ventricular septum is used to assess cardiac iron, but iron calibration and distribution data in humans is limited. Methods and Results Twelve human hearts were studied from transfusion dependent patients following either death (heart failure n=7, stroke n=1) or transplantation for end-stage heart failure (n=4). After CMR R2* measurement, tissue iron concentration was measured in multiple samples of each heart using inductively coupled plasma atomic emission spectroscopy. Iron distribution throughout the heart showed no systematic variation between segments, but epicardial iron concentration was higher than in the endocardium. The mean (±SD) global myocardial iron causing severe heart failure in 10 patients was 5.98 ±2.42mg/g dw (range 3.19–9.50), but in 1 outlier case of heart failure was 25.9mg/g dw. Myocardial ln[R2*] was strongly linearly correlated with ln[Fe] (R2=0.910, p<0.001) leading to [Fe]=45.0•(T2*)−1.22 for the clinical calibration equation with [Fe] in mg/g dw and T2* in ms. Mid-ventricular septal iron concentration and R2* were both highly representative of mean global myocardial iron. Conclusions These data detail the iron distribution throughout the heart in iron overload and provide calibration in humans for CMR R2* against myocardial iron concentration. The iron values are of considerable interest with regard to the level of cardiac iron associated with iron-related death and indicate that the heart is more sensitive to iron loading than the liver. The results also validate the current clinical practice of monitoring cardiac iron in-vivo by CMR of the mid septum.

Background-The goal of this study was to determine the predictive value of cardiac T2* magnetic resonance for heart failure and arrhythmia in thalassemia major. Methods and Results-We analyzed cardiac and liver T2* magnetic resonance and serum ferritin in 652 thalassemia major patients from 21 UK centers with 1442 magnetic resonance scans. The relative risk for heart failure with cardiac T2* values Ͻ10 ms (compared with Ͼ10 ms) was 160 (95% confidence interval, 39 to 653). Heart failure occurred in 47% of patients within 1 year of a cardiac T2* Ͻ6 ms with a relative risk of 270 (95% confidence interval, 64 to 1129). The area under the receiver-operating characteristic curve for predicting heart failure was significantly greater for cardiac T2* (0.948) than for liver T2* (0.589; PϽ0.001) or serum ferritin (0.629; PϽ0.001). Cardiac T2* was Ͻ10 ms in 98% of scans in patients who developed heart failure. The relative risk for arrhythmia with cardiac T2* values Ͻ20 ms (compared with Ͼ20 ms) was 4.6 (95% confidence interval, 2.66 to 7.95). Arrhythmia occurred in 14% of patients within 1 year of a cardiac T2* of Ͻ6 ms. The area under the receiver-operating characteristic curve for predicting arrhythmia was significantly greater for cardiac T2* (0.747) than for liver T2* (0.514; PϽ0.001) or serum ferritin (0.518; PϽ0.001). The cardiac T2* was Ͻ20 ms in 83% of scans in patients who developed arrhythmia. Conclusions-Cardiac T2* magnetic resonance identifies patients at high risk of heart failure and arrhythmia from myocardial siderosis in thalassemia major and is superior to serum ferritin and liver iron. Using cardiac T2* for the early identification and treatment of patients at risk is a logical means of reducing the high burden of cardiac mortality in myocardial siderosis. Clinical Trial Registration-URL: http://www.clinicaltrials.gov.

As modeling becomes a more widespread practice in the life-and biomedical sciences, we require reliable tools to calibrate models against ever more complex and detailed data. Here we present an approximate Bayesian computation framework and software environment, ABC-SysBio, which enables parameter estimation and model selection in the Bayesian formalism using Sequential Monte-Carlo approaches. We outline the underlying rationale, discuss the computational and practical issues, and provide detailed guidance as to how the important tasks of parameter inference and model selection can be carried out in practice. Unlike other available packages, ABC-SysBio is highly suited for investigating in particular the challenging problem of fitting stochastic models to data. Although computationally expensive, the additional insights gained in the Bayesian formalism more than make up for this cost, especially in complex problems.

Motivation: The integration of multiple datasets remains a key challenge in systems biology and genomic medicine. Modern high-throughput technologies generate a broad array of different data types, providing distinct—but often complementary—information. We present a Bayesian method for the unsupervised integrative modelling of multiple datasets, which we refer to as MDI (Multiple Dataset Integration). MDI can integrate information from a wide range of different datasets and data types simultaneously (including the ability to model time series data explicitly using Gaussian processes). Each dataset is modelled using a Dirichlet-multinomial allocation (DMA) mixture model, with dependencies between these models captured through parameters that describe the agreement among the datasets.Results: Using a set of six artificially constructed time series datasets, we show that MDI is able to integrate a significant number of datasets simultaneously, and that it successfully captures the underlying structural similarity between the datasets. We also analyse a variety of real Saccharomyces cerevisiae datasets. In the two-dataset case, we show that MDI’s performance is comparable with the present state-of-the-art. We then move beyond the capabilities of current approaches and integrate gene expression, chromatin immunoprecipitation–chip and protein–protein interaction data, to identify a set of protein complexes for which genes are co-regulated during the cell cycle. Comparisons to other unsupervised data integration techniques—as well as to non-integrative approaches—demonstrate that MDI is competitive, while also providing information that would be difficult or impossible to extract using other methods.Availability: A Matlab implementation of MDI is available from http://www2.warwick.ac.uk/fac/sci/systemsbiology/research/software/.Contact: D.L.Wild@warwick.ac.ukSupplementary information: Supplementary data are available at Bioinformatics online.

1Genome-wide association studies (GWAS) have identified thousands of genomic regions 2 affecting complex diseases. The next challenge is to elucidate the causal genes and mechanisms 3 involved. One approach is to use statistical colocalization to assess shared genetic aetiology 4 across multiple related traits (e.g. molecular traits, metabolic pathways and complex diseases) 5 to identify causal pathways, prioritize causal variants and evaluate pleiotropy. We propose 6HyPrColoc (Hypothesis Prioritisation in multi-trait Colocalization), an efficient deterministic 7Bayesian algorithm using GWAS summary statistics that can detect colocalization across vast 8 numbers of traits simultaneously (e.g. 100 traits can be jointly analysed in around 1 second). 9We performed a genome-wide multi-trait colocalization analysis of coronary heart disease 10 (CHD) and fourteen related traits. HyPrColoc identified 43 regions in which CHD colocalized 11 with ≥1 trait, including 5 potentially new CHD loci. Across the 43 loci, we further integrated 12 gene and protein expression quantitative trait loci to identify candidate causal genes. 13

Motivation: The growing field of systems biology has driven demand for flexible tools to model and simulate biological systems. Two established problems in the modeling of biological processes are model selection and the estimation of associated parameters. A number of statistical approaches, both frequentist and Bayesian, have been proposed to answer these questions.Results: Here we present a Python package, ABC-SysBio, that implements parameter inference and model selection for dynamical systems in an approximate Bayesian computation (ABC) framework. ABC-SysBio combines three algorithms: ABC rejection sampler, ABC SMC for parameter inference and ABC SMC for model selection. It is designed to work with models written in Systems Biology Markup Language (SBML). Deterministic and stochastic models can be analyzed in ABC-SysBio.Availability: http://abc-sysbio.sourceforge.netContact: christopher.barnes@imperial.ac.uk; ttoni@imperial.ac.uk

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Genome-wide association studies (GWAS) have identified thousands of genomic regions affecting complex diseases. The next challenge is to elucidate the causal genes and mechanisms involved. One approach is to use statistical colocalization to assess shared genetic aetiology across multiple related traits (e.g. molecular traits, metabolic pathways and complex diseases) to identify causal pathways, prioritize causal variants and evaluate pleiotropy. We propose HyPrColoc (Hypothesis Prioritisation for multi-trait Colocalization), an efficient deterministic Bayesian algorithm using GWAS summary statistics that can detect colocalization across vast numbers of traits simultaneously (e.g. 100 traits can be jointly analysed in around 1 s). We perform a genome-wide multi-trait colocalization analysis of coronary heart disease (CHD) and fourteen related traits, identifying 43 regions in which CHD colocalized with ≥1 trait, including 5 previously unknown CHD loci. Across the 43 loci, we further integrate gene and protein expression quantitative trait loci to identify candidate causal genes.