Comparison of a novel potentiator of CFTR channel activity to ivacaftor in ameliorating mucostasis caused by cigarette smoke in primary human bronchial airway epithelial cells.

BACKGROUND: Cystic Fibrosis causing mutations in the gene CFTR, reduce the activity of the CFTR channel protein, and leads to mucus aggregation, airway obstruction and poor lung function. A role for CFTR in the pathogenesis of other muco-obstructive airway diseases such as Chronic Obstructive Pulmonary Disease (COPD) has been well established. The CFTR modulatory compound, Ivacaftor (VX-770), potentiates channel activity of CFTR and certain CF-causing mutations and has been shown to ameliorate mucus obstruction and improve lung function in people harbouring these CF-causing mutations. A pilot trial of Ivacaftor supported its potential efficacy for the treatment of mucus obstruction in COPD. These findings prompted the search for CFTR potentiators that are more effective in ameliorating cigarette-smoke (CS) induced mucostasis. METHODS: Small molecule potentiators, previously identified in CFTR binding studies, were tested for activity in augmenting CFTR channel activity using patch clamp electrophysiology in HEK-293 cells, a fluorescence-based assay of membrane potential in Calu-3 cells and in Ussing chamber studies of primary bronchial epithelial cultures. Addition of cigarette smoke extract (CSE) to the solutions bathing the apical surface of Calu-3 cells and primary bronchial airway cultures was used to model COPD. Confocal studies of the velocity of fluorescent microsphere movement on the apical surface of CSE exposed airway epithelial cultures, were used to assess the effect of potentiators on CFTR-mediated mucociliary movement. RESULTS: We showed that SK-POT1, like VX-770, was effective in augmenting the cyclic AMP-dependent channel activity of CFTR. SK-POT-1 enhanced CFTR channel activity in airway epithelial cells previously exposed to CSE and ameliorated mucostasis on the surface of primary airway cultures. CONCLUSION: Together, this evidence supports the further development of SK-POT1 as an intervention in the treatment of COPD.

Bayesian profile regression for clustering analysis involving a longitudinal response and explanatory variables.

The identification of sets of co-regulated genes that share a common function is a key question of modern genomics. Bayesian profile regression is a semi-supervised mixture modelling approach that makes use of a response to guide inference toward relevant clusterings. Previous applications of profile regression have considered univariate continuous, categorical, and count outcomes. In this work, we extend Bayesian profile regression to cases where the outcome is longitudinal (or multivariate continuous) and provide PReMiuMlongi, an updated version of PReMiuM, the R package for profile regression. We consider multivariate normal and Gaussian process regression response models and provide proof of principle applications to four simulation studies. The model is applied on budding yeast data to identify groups of genes co-regulated during the Saccharomyces cerevisiae cell cycle. We identify 4 distinct groups of genes associated with specific patterns of gene expression trajectories, along with the bound transcriptional factors, likely involved in their co-regulation process.

Comparison of a novel potentiator of CFTR channel activity to ivacaftor in ameliorating mucostasis caused by cigarette smoke in primary human bronchial airway epithelial cells.

Background: Cystic Fibrosis causing mutations in the gene CFTR , reduce the activity of the CFTR channel protein, and leads to mucus aggregation, airway obstruction and poor lung function. A role for CFTR in the pathogenesis of other muco-obstructive airway diseases such as Chronic Obstructive Pulmonary Disease (COPD) has been well established. The CFTR modulatory compound, Ivacaftor (VX-770), potentiates channel activity of CFTR and certain CF-causing mutations and has been shown to ameliorate mucus obstruction and improve lung function in people harbouring these CF-causing mutations. A pilot trial of Ivacaftor supported its potential efficacy for the treatment of mucus obstruction in COPD. These findings prompted the search for CFTR potentiators that are more effective in ameliorating cigarette-smoke (CS) induced mucostasis. Methods: A novel small molecule potentiator (SK-POT1), previously identified in CFTR binding studies, was tested for its activity in augmenting CFTR channel activity using patch clamp electrophysiology in HEK-293 cells, a fluorescence-based assay of membrane potential in Calu-3 cells and in Ussing chamber studies of primary bronchial epithelial cultures. Addition of cigarette smoke extract (CSE) to the solutions bathing the apical surface of Calu-3 cells and primary bronchial airway cultures was used to model COPD. Confocal studies of the velocity of fluorescent microsphere movement on the apical surface of CSE exposed airway epithelial cultures, were used to assess the effect of potentiators on CFTR-mediated mucociliary movement. Results: We showed that SK-POT1, like VX-770, was effective in augmenting the cyclic AMP-dependent channel activity of CFTR. SK-POT-1 enhanced CFTR channel activity in airway epithelial cells previously exposed to CSE and ameliorated mucostasis on the surface of primary airway cultures. Conclusion: Together, this evidence supports the further development of SK-POT1 as an intervention in the treatment of COPD.

Effect on life expectancy of temporal sequence in a multimorbidity cluster of psychosis, diabetes, and congestive heart failure among 1·7 million individuals in Wales with 20-year follow-up: a retrospective cohort study using linked data.

BACKGROUND: To inform targeted public health strategies, it is crucial to understand how coexisting diseases develop over time and their associated impacts on patient outcomes and health-care resources. This study aimed to examine how psychosis, diabetes, and congestive heart failure, in a cluster of physical-mental health multimorbidity, develop and coexist over time, and to assess the associated effects of different temporal sequences of these diseases on life expectancy in Wales. METHODS: In this retrospective cohort study, we used population-scale, individual-level, anonymised, linked, demographic, administrative, and electronic health record data from the Wales Multimorbidity e-Cohort. We included data on all individuals aged 25 years and older who were living in Wales on Jan 1, 2000 (the start of follow-up), with follow-up continuing until Dec 31, 2019, first break in Welsh residency, or death. Multistate models were applied to these data to model trajectories of disease in multimorbidity and their associated effect on all-cause mortality, accounting for competing risks. Life expectancy was calculated as the restricted mean survival time (bound by the maximum follow-up of 20 years) for each of the transitions from the health states to death. Cox regression models were used to estimate baseline hazards for transitions between health states, adjusted for sex, age, and area-level deprivation (Welsh Index of Multiple Deprivation [WIMD] quintile). FINDINGS: Our analyses included data for 1 675 585 individuals (811 393 [48·4%] men and 864 192 [51·6%] women) with a median age of 51·0 years (IQR 37·0-65·0) at cohort entry. The order of disease acquisition in cases of multimorbidity had an important and complex association with patient life expectancy. Individuals who developed diabetes, psychosis, and congestive heart failure, in that order (DPC), had reduced life expectancy compared with people who developed the same three conditions in a different order: for a 50-year-old man in the third quintile of the WIMD (on which we based our main analyses to allow comparability), DPC was associated with a loss in life expectancy of 13·23 years (SD 0·80) compared with the general otherwise healthy or otherwise diseased population. Congestive heart failure as a single condition was associated with mean a loss in life expectancy of 12·38 years (0·00), and with a loss of 12·95 years (0·06) when preceded by psychosis and 13·45 years (0·13) when followed by psychosis. Findings were robust in people of older ages, more deprived populations, and women, except that the trajectory of psychosis, congestive heart failure, and diabetes was associated with higher mortality in women than men. Within 5 years of an initial diagnosis of diabetes, the risk of developing psychosis or congestive heart failure, or both, was increased. INTERPRETATION: The order in which individuals develop psychosis, diabetes, and congestive heart failure as combinations of conditions can substantially affect life expectancy. Multistate models offer a flexible framework to assess temporal sequences of diseases and allow identification of periods of increased risk of developing subsequent conditions and death. FUNDING: Health Data Research UK.

Clinical and functional efficacy of elexacaftor/tezacaftor/ivacaftor in people with cystic fibrosis carrying the N1303K mutation.

BACKGROUND: Elexacaftor/tezacaftor/ivacaftor (ELX/TEZ/IVA) significantly improves health outcomes in people with cystic fibrosis (pwCF) carrying one or two F508del mutations. According to in vitro assays performed in FRT cells, 178 additional mutations respond to ELX/TEZ/IVA. The N1303K mutation is not included in this list of mutations. Recent in vitro data suggested that ELX/TEZ/IVA increases N1303K-CFTR activity. Based on the in vitro response, eight patients commenced treatment with ELX/TEZ/IVA. METHODS: Two homozygotes; and six compound heterozygotes N1303K/nonsense or frameshift mutation pwCF were treated off label with ELX/TEZ/IVA. Clinical data before and 8 weeks after starting treatment were prospectively collected. The response to ELX/TEZ/IVA was assessed in intestinal organoids derived from 5 study patients and an additional patient carrying N1303K that is not receiving treatment. RESULTS: Compared to the values before commencing treatment, mean forced expiratory volume in 1 second increased by 18.4 percentage points and 26.5% relative to baseline, mean BMI increased by 0.79 Kg/m2, and mean lung clearance index decreased by 3.6 points and 22.2%. There was no significant change in sweat chloride. Nasal potential difference normalized in four patients and remained abnormal in three. Results in 3D intestinal organoids and 2D nasal epithelial cultures showed a response in CFTR channel activity. CONCLUSIONS: This report supports the previously reported in vitro data, performed in human nasal and bronchial epithelial cells and intestinal organoids, that pwCF who carry the N1303K mutation have a significant clinical benefit by ELX/TEZ/IVA treatment.

Correlation of Electrophysiological and Fluorescence-Based Measurements of Modulator Efficacy in Nasal Epithelial Cultures Derived from People with Cystic Fibrosis.

It has been suggested that in vitro studies of the rescue effect of CFTR modulator drugs in nasal epithelial cultures derived from people with cystic fibrosis have the potential to predict clinical responses to the same drugs. Hence, there is an interest in evaluating different methods for measuring in vitro modulator responses in patient-derived nasal cultures. Commonly, the functional response to CFTR modulator combinations in these cultures is assessed by bioelectric measurements, using the Ussing chamber. While this method is highly informative, it is time-consuming. A fluorescence-based, multi-transwell method for assaying regulated apical chloride conductance (Fl-ACC) promises to provide a complementary approach to theratyping in patient-derived nasal cultures. In the present work, we compared Ussing chamber measurements and fluorescence-based measurements of CFTR-mediated apical conductance in matching, fully differentiated nasal cultures derived from CF patients, homozygous for F508del (n = 31) or W1282X (n = 3), or heterozygous for Class III mutations G551D or G178R (n = 5). These cultures were obtained through a bioresource called the Cystic Fibrosis Canada-Sick Kids Program in Individual CF Therapy (CFIT). We found that the Fl-ACC method was effective in detecting positive responses to interventions for all genotypes. There was a correlation between patient-specific drug responses measured in cultures harbouring F508del, as measured using the Ussing chamber technique and the fluorescence-based assay (Fl-ACC). Finally, the fluorescence-based assay has the potential for greater sensitivity for detecting responses to pharmacological rescue strategies targeting W1282X.

Cost-effectiveness analysis of genetic tools to predict treatment response in patients with cystic fibrosis.

BACKGROUND: Cystic fibrosis (CF) transmembrane conductance regulator (CFTR) modulator therapies show variable efficacy for patients with CF. Patient-derived predictive tools may identify individuals likely to respond to CFTRs, but are not in routine use. We aimed to determine the cost-utility of predictive tool-guided treatment with CFTRs as add-on to standard of care (SoC) for individuals with CF. METHODS: This economic evaluation compared two strategies using an individual level simulation: (i) Treat All, where all patients received CFTRs plus SoC and (ii) Test→Treat, where patients who tested positive on predictive tools received CFTRs plus SoC and those who tested negative received SoC only. We simulated 50,000 individuals over their lifetime, and estimated costs (2020 CAD) per quality-adjusted life year (QALY) from the healthcare payer's perspective, discounted at 1.5% annually. The model was populated using Canadian CF registry data and published literature. Probabilistic and deterministic sensitivity were conducted. RESULTS: The Treat All and Test→Treat and strategies yielded 22.41 and 21.36 QALYs, and cost $4.21 M and $3.15 M respectively. Results of probabilistic sensitivity analysis showed that Test→Treat was highly cost-effective compared to Treat All in 100% of simulations at cost-effectiveness thresholds as high as $500,000 per QALY. Test→Treat may save between $931 K to $1.1 M per QALY lost, depending on sensitivity and specificity of predictive tools. CONCLUSION: The use of predictive tools could optimize the health benefits of CFTR modulators while reducing costs. Our findings support the use of pre-treatment predictive testing and may help inform coverage and reimbursement policies for individuals with CF.

Dose-response prediction for in-vitro drug combination datasets: a probabilistic approach.

In this paper we propose PIICM, a probabilistic framework for dose-response prediction in high-throughput drug combination datasets. PIICM utilizes a permutation invariant version of the intrinsic co-regionalization model for multi-output Gaussian process regression, to predict dose-response surfaces in untested drug combination experiments. Coupled with an observation model that incorporates experimental uncertainty, PIICM is able to learn from noisily observed cell-viability measurements in settings where the underlying dose-response experiments are of varying quality, utilize different experimental designs, and the resulting training dataset is sparsely observed. We show that the model can accurately predict dose-response in held out experiments, and the resulting function captures relevant features indicating synergistic interaction between drugs.

Semi-Supervised Non-Parametric Bayesian Modelling of Spatial Proteomics.

Understanding sub-cellular protein localisation is an essential component in the analysis of context specific protein function. Recent advances in quantitative mass-spectrometry (MS) have led to high resolution mapping of thousands of proteins to sub-cellular locations within the cell. Novel modelling considerations to capture the complex nature of these data are thus necessary. We approach analysis of spatial proteomics data in a non-parametric Bayesian framework, using K-component mixtures of Gaussian process regression models. The Gaussian process regression model accounts for correlation structure within a sub-cellular niche, with each mixture component capturing the distinct correlation structure observed within each niche. The availability of marker proteins (i.e. proteins with a priori known labelled locations) motivates a semi-supervised learning approach to inform the Gaussian process hyperparameters. We moreover provide an efficient Hamiltonian-within-Gibbs sampler for our model. Furthermore, we reduce the computational burden associated with inversion of covariance matrices by exploiting the structure in the covariance matrix. A tensor decomposition of our covariance matrices allows extended Trench and Durbin algorithms to be applied to reduce the computational complexity of inversion and hence accelerate computation. We provide detailed case-studies on Drosophila embryos and mouse pluripotent embryonic stem cells to illustrate the benefit of semi-supervised functional Bayesian modelling of the data.

Inferring differential subcellular localisation in comparative spatial proteomics using BANDLE.

The steady-state localisation of proteins provides vital insight into their function. These localisations are context specific with proteins translocating between different subcellular niches upon perturbation of the subcellular environment. Differential localisation, that is a change in the steady-state subcellular location of a protein, provides a step towards mechanistic insight of subcellular protein dynamics. High-accuracy high-throughput mass spectrometry-based methods now exist to map the steady-state localisation and re-localisation of proteins. Here, we describe a principled Bayesian approach, BANDLE, that uses these data to compute the probability that a protein differentially localises upon cellular perturbation. Extensive simulation studies demonstrate that BANDLE reduces the number of both type I and type II errors compared to existing approaches. Application of BANDLE to several datasets recovers well-studied translocations. In an application to cytomegalovirus infection, we obtain insights into the rewiring of the host proteome. Integration of other high-throughput datasets allows us to provide the functional context of these data.

Consensus clustering for Bayesian mixture models.

BACKGROUND: Cluster analysis is an integral part of precision medicine and systems biology, used to define groups of patients or biomolecules. Consensus clustering is an ensemble approach that is widely used in these areas, which combines the output from multiple runs of a non-deterministic clustering algorithm. Here we consider the application of consensus clustering to a broad class of heuristic clustering algorithms that can be derived from Bayesian mixture models (and extensions thereof) by adopting an early stopping criterion when performing sampling-based inference for these models. While the resulting approach is non-Bayesian, it inherits the usual benefits of consensus clustering, particularly in terms of computational scalability and providing assessments of clustering stability/robustness. RESULTS: In simulation studies, we show that our approach can successfully uncover the target clustering structure, while also exploring different plausible clusterings of the data. We show that, when a parallel computation environment is available, our approach offers significant reductions in runtime compared to performing sampling-based Bayesian inference for the underlying model, while retaining many of the practical benefits of the Bayesian approach, such as exploring different numbers of clusters. We propose a heuristic to decide upon ensemble size and the early stopping criterion, and then apply consensus clustering to a clustering algorithm derived from a Bayesian integrative clustering method. We use the resulting approach to perform an integrative analysis of three 'omics datasets for budding yeast and find clusters of co-expressed genes with shared regulatory proteins. We validate these clusters using data external to the analysis. CONCLUSTIONS: Our approach can be used as a wrapper for essentially any existing sampling-based Bayesian clustering implementation, and enables meaningful clustering analyses to be performed using such implementations, even when computational Bayesian inference is not feasible, e.g. due to poor exploration of the target density (often as a result of increasing numbers of features) or a limited computational budget that does not along sufficient samples to drawn from a single chain. This enables researchers to straightforwardly extend the applicability of existing software to much larger datasets, including implementations of sophisticated models such as those that jointly model multiple datasets.

Transcriptional, epigenetic and metabolic signatures in cardiometabolic syndrome defined by extreme phenotypes.

BACKGROUND: This work is aimed at improving the understanding of cardiometabolic syndrome pathophysiology and its relationship with thrombosis by generating a multi-omic disease signature. METHODS/RESULTS: We combined classic plasma biochemistry and plasma biomarkers with the transcriptional and epigenetic characterisation of cell types involved in thrombosis, obtained from two extreme phenotype groups (morbidly obese and lipodystrophy) and lean individuals to identify the molecular mechanisms at play, highlighting patterns of abnormal activation in innate immune phagocytic cells. Our analyses showed that extreme phenotype groups could be distinguished from lean individuals, and from each other, across all data layers. The characterisation of the same obese group, 6 months after bariatric surgery, revealed the loss of the abnormal activation of innate immune cells previously observed. However, rather than reverting to the gene expression landscape of lean individuals, this occurred via the establishment of novel gene expression landscapes. NETosis and its control mechanisms emerge amongst the pathways that show an improvement after surgical intervention. CONCLUSIONS: We showed that the morbidly obese and lipodystrophy groups, despite some differences, shared a common cardiometabolic syndrome signature. We also showed that this could be used to discriminate, amongst the normal population, those individuals with a higher likelihood of presenting with the disease, even when not displaying the classic features.

Topological approximate Bayesian computation for parameter inference of an angiogenesis model.

MOTIVATION: Inferring the parameters of models describing biological systems is an important problem in the reverse engineering of the mechanisms underlying these systems. Much work has focused on parameter inference of stochastic and ordinary differential equation models using Approximate Bayesian Computation (ABC). While there is some recent work on inference in spatial models, this remains an open problem. Simultaneously, advances in topological data analysis (TDA), a field of computational mathematics, have enabled spatial patterns in data to be characterized. RESULTS: Here, we focus on recent work using TDA to study different regimes of parameter space for a well-studied model of angiogenesis. We propose a method for combining TDA with ABC to infer parameters in the Anderson-Chaplain model of angiogenesis. We demonstrate that this topological approach outperforms ABC approaches that use simpler statistics based on spatial features of the data. This is a first step toward a general framework of spatial parameter inference for biological systems, for which there may be a variety of filtrations, vectorizations and summary statistics to be considered. AVAILABILITY AND IMPLEMENTATION: All code used to produce our results is available as a Snakemake workflow from github.com/tt104/tabc_angio.

Noise-augmented directional clustering of genetic association data identifies distinct mechanisms underlying obesity.

Clustering genetic variants based on their associations with different traits can provide insight into their underlying biological mechanisms. Existing clustering approaches typically group variants based on the similarity of their association estimates for various traits. We present a new procedure for clustering variants based on their proportional associations with different traits, which is more reflective of the underlying mechanisms to which they relate. The method is based on a mixture model approach for directional clustering and includes a noise cluster that provides robustness to outliers. The procedure performs well across a range of simulation scenarios. In an applied setting, clustering genetic variants associated with body mass index generates groups reflective of distinct biological pathways. Mendelian randomization analyses support that the clusters vary in their effect on coronary heart disease, including one cluster that represents elevated body mass index with a favourable metabolic profile and reduced coronary heart disease risk. Analysis of the biological pathways underlying this cluster identifies inflammation as potentially explaining differences in the effects of increased body mass index on coronary heart disease.

Tailored Bayes: a risk modeling framework under unequal misclassification costs.

Risk prediction models are a crucial tool in healthcare. Risk prediction models with a binary outcome (i.e., binary classification models) are often constructed using methodology which assumes the costs of different classification errors are equal. In many healthcare applications, this assumption is not valid, and the differences between misclassification costs can be quite large. For instance, in a diagnostic setting, the cost of misdiagnosing a person with a life-threatening disease as healthy may be larger than the cost of misdiagnosing a healthy person as a patient. In this article, we present Tailored Bayes (TB), a novel Bayesian inference framework which "tailors" model fitting to optimize predictive performance with respect to unbalanced misclassification costs. We use simulation studies to showcase when TB is expected to outperform standard Bayesian methods in the context of logistic regression. We then apply TB to three real-world applications, a cardiac surgery, a breast cancer prognostication task, and a breast cancer tumor classification task and demonstrate the improvement in predictive performance over standard methods.

A new platform for high-throughput therapy testing on iPSC-derived lung progenitor cells from cystic fibrosis patients.

For those people with cystic fibrosis carrying rare CFTR mutations not responding to currently available therapies, there is an unmet need for relevant tissue models for therapy development. Here, we describe a new testing platform that employs patient-specific induced pluripotent stem cells (iPSCs) differentiated to lung progenitor cells that can be studied using a dynamic, high-throughput fluorescence-based assay of CFTR channel activity. Our proof-of-concept studies support the potential use of this platform, together with a Canadian bioresource that contains iPSC lines and matched nasal cultures from people with rare mutations, to advance patient-oriented therapy development. Interventions identified in the high-throughput, stem cell-based model and validated in primary nasal cultures from the same person have the potential to be advanced as therapies.

A Bayesian semi-parametric model for thermal proteome profiling.

The thermal stability of proteins can be altered when they interact with small molecules, other biomolecules or are subject to post-translation modifications. Thus monitoring the thermal stability of proteins under various cellular perturbations can provide insights into protein function, as well as potentially determine drug targets and off-targets. Thermal proteome profiling is a highly multiplexed mass-spectrommetry method for monitoring the melting behaviour of thousands of proteins in a single experiment. In essence, thermal proteome profiling assumes that proteins denature upon heating and hence become insoluble. Thus, by tracking the relative solubility of proteins at sequentially increasing temperatures, one can report on the thermal stability of a protein. Standard thermodynamics predicts a sigmoidal relationship between temperature and relative solubility and this is the basis of current robust statistical procedures. However, current methods do not model deviations from this behaviour and they do not quantify uncertainty in the melting profiles. To overcome these challenges, we propose the application of Bayesian functional data analysis tools which allow complex temperature-solubility behaviours. Our methods have improved sensitivity over the state-of-the art, identify new drug-protein associations and have less restrictive assumptions than current approaches. Our methods allows for comprehensive analysis of proteins that deviate from the predicted sigmoid behaviour and we uncover potentially biphasic phenomena with a series of published datasets.

The RNA landscape of the human placenta in health and disease.

The placenta is the interface between mother and fetus and inadequate function contributes to short and long-term ill-health. The placenta is absent from most large-scale RNA-Seq datasets. We therefore analyze long and small RNAs (~101 and 20 million reads per sample respectively) from 302 human placentas, including 94 cases of preeclampsia (PE) and 56 cases of fetal growth restriction (FGR). The placental transcriptome has the seventh lowest complexity of 50 human tissues: 271 genes account for 50% of all reads. We identify multiple circular RNAs and validate 6 of these by Sanger sequencing across the back-splice junction. Using large-scale mass spectrometry datasets, we find strong evidence of peptides produced by translation of two circular RNAs. We also identify novel piRNAs which are clustered on Chr1 and Chr14. PE and FGR are associated with multiple and overlapping differences in mRNA, lincRNA and circRNA but fewer consistent differences in small RNAs. Of the three protein coding genes differentially expressed in both PE and FGR, one encodes a secreted protein FSTL3 (follistatin-like 3). Elevated serum levels of FSTL3 in pregnant women are predictive of subsequent PE and FGR. To aid visualization of our placenta transcriptome data, we develop a web application ( https://www.obgyn.cam.ac.uk/placentome/ ).

A fast and efficient colocalization algorithm for identifying shared genetic risk factors across multiple traits.

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.

MR-Clust: clustering of genetic variants in Mendelian randomization with similar causal estimates.

MOTIVATION: Mendelian randomization is an epidemiological technique that uses genetic variants as instrumental variables to estimate the causal effect of a risk factor on an outcome. We consider a scenario in which causal estimates based on each variant in turn differ more strongly than expected by chance alone, but the variants can be divided into distinct clusters, such that all variants in the cluster have similar causal estimates. This scenario is likely to occur when there are several distinct causal mechanisms by which a risk factor influences an outcome with different magnitudes of causal effect. We have developed an algorithm MR-Clust that finds such clusters of variants, and so can identify variants that reflect distinct causal mechanisms. Two features of our clustering algorithm are that it accounts for differential uncertainty in the causal estimates, and it includes 'null' and 'junk' clusters, to provide protection against the detection of spurious clusters. RESULTS: Our algorithm correctly detected the number of clusters in a simulation analysis, outperforming methods that either do not account for uncertainty or do not include null and junk clusters. In an applied example considering the effect of blood pressure on coronary artery disease risk, the method detected four clusters of genetic variants. A post hoc hypothesis-generating search suggested that variants in the cluster with a negative effect of blood pressure on coronary artery disease risk were more strongly related to trunk fat percentage and other adiposity measures than variants not in this cluster. AVAILABILITY AND IMPLEMENTATION: MR-Clust can be downloaded from https://github.com/cnfoley/mrclust. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.