Imperial College London


Faculty of MedicineNational Heart & Lung Institute

Honorary Clinical Research Fellow







Sydney StreetRoyal Brompton Campus





Publication Type

5 results found

Ware J, Tadros R, Francis C, Xu X, Matthews P, watkins H, Bezzina Cet al., 2021, Shared genetic pathways contribute to risk of hypertrophic and dilated cardiomyopathies with opposite directions of effect, Nature Genetics, Vol: 53, Pages: 128-134, ISSN: 1061-4036

The heart muscle diseases hypertrophic (HCM) and dilated (DCM) cardiomyopathies are leading causes of sudden death and heart failure in young otherwise healthy individuals. We conducted genome-wide association studies (GWAS) and multi-trait analyses in HCM (1,733 cases), DCM (5,521 cases), and nine left ventricular (LV) traits in 19,260 UK Biobank participants with structurally-normal hearts. We identified 16 loci associated with HCM, 13 with DCM, and 23 with LV traits. We show strong genetic correlations between LV traits and cardiomyopathies, with opposing effects in HCM and DCM. Two-sample Mendelian randomization supports a causal association linking increased contractility with HCM risk. A polygenic risk score (PRS) explains a significant portion of phenotypic variability in carriers of HCM-causing rare variants. Our findings thus provide evidence that PRS may account for variability in Mendelian diseases. More broadly, we provide insights into how genetic pathways may lead to distinct disorders through opposing genetic effects.

Journal article

Bai W, Suzuki H, Huang J, Francis C, Wang S, Tarroni G, Guitton F, Aung N, Fung K, Petersen SE, Piechnik SK, Neubauer S, Evangelou E, Dehghan A, O'Regan DP, Wilkins MR, Guo Y, Matthews PM, Rueckert Det al., 2020, A population-based phenome-wide association study of cardiac and aortic structure and function, Nature Medicine, Vol: 26, Pages: 1654-1662, ISSN: 1078-8956

Differences in cardiac and aortic structure and function are associated with cardiovascular diseases and a wide range of other types of disease. Here we analyzed cardiovascular magnetic resonance images from a population-based study, the UK Biobank, using an automated machine-learning-based analysis pipeline. We report a comprehensive range of structural and functional phenotypes for the heart and aorta across 26,893 participants, and explore how these phenotypes vary according to sex, age and major cardiovascular risk factors. We extended this analysis with a phenome-wide association study, in which we tested for correlations of a wide range of non-imaging phenotypes of the participants with imaging phenotypes. We further explored the associations of imaging phenotypes with early-life factors, mental health and cognitive function using both observational analysis and Mendelian randomization. Our study illustrates how population-based cardiac and aortic imaging phenotypes can be used to better define cardiovascular disease risks as well as heart–brain health interactions, highlighting new opportunities for studying disease mechanisms and developing image-based biomarkers.

Journal article

Renard M, Francis C, Ghosh R, Scott AF, Witmer PD, Ades LC, Andelfinger GU, Arnaud P, Boileau C, Callewaert BL, Guo D, Hanna N, Lindsay ME, Morisaki H, Morisaki T, Pachter N, Robert L, Van Laer L, Dietz HC, Loeys BL, Milewicz DM, De Backer Jet al., 2018, Clinical Validity of Genes for Heritable Thoracic Aortic Aneurysm and Dissection, JOURNAL OF THE AMERICAN COLLEGE OF CARDIOLOGY, Vol: 72, Pages: 605-615, ISSN: 0735-1097

Journal article

Whiffin N, walsh R, Govind R, Edwards M, Ahmad M, Zhang X, Tayal U, Buchan R, Midwinter W, Wilk A, Najgebauer H, Francis C, Wilkinson S, Monk T, Brett L, O'Regan D, Prasad S, Morris-Rosendahl D, Barton P, Edwards E, Ware J, Cook Set al., 2018, CardioClassifier: disease- and gene-specific computational decision support for clinical genome interpretation, Genetics in Medicine, Vol: 20, Pages: 1246-1254, ISSN: 1098-3600

PurposeInternationally adopted variant interpretation guidelines from the American College of Medical Genetics and Genomics (ACMG) are generic and require disease-specific refinement. Here we developed CardioClassifier (, a semiautomated decision-support tool for inherited cardiac conditions (ICCs).MethodsCardioClassifier integrates data retrieved from multiple sources with user-input case-specific information, through an interactive interface, to support variant interpretation. Combining disease- and gene-specific knowledge with variant observations in large cohorts of cases and controls, we refined 14 computational ACMG criteria and created three ICC-specific rules.ResultsWe benchmarked CardioClassifier on 57 expertly curated variants and show full retrieval of all computational data, concordantly activating 87.3% of rules. A generic annotation tool identified fewer than half as many clinically actionable variants (64/219 vs. 156/219, Fisher’s P = 1.1  ×  10−18), with important false positives, illustrating the critical importance of disease and gene-specific annotations. CardioClassifier identified putatively disease-causing variants in 33.7% of 327 cardiomyopathy cases, comparable with leading ICC laboratories. Through addition of manually curated data, variants found in over 40% of cardiomyopathy cases are fully annotated, without requiring additional user-input data.ConclusionCardioClassifier is an ICC-specific decision-support tool that integrates expertly curated computational annotations with case-specific data to generate fast, reproducible, and interactive variant pathogenicity reports, according to best practice guidelines.

Journal article

Biffi C, Simoes Monteiro de Marvao A, Attard M, Dawes T, Whiffin N, Bai W, Shi W, Francis C, Meyer H, Buchan R, Cook S, Rueckert D, O'Regan DPet al., 2017, Three-dimensional Cardiovascular Imaging-Genetics: A Mass Univariate Framework, Bioinformatics, ISSN: 1367-4803

Motivation: Left ventricular (LV) hypertrophy is a strong predictor of cardiovascular outcomes, but its genetic regulation remains largely unexplained. Conventional phenotyping relies on manual calculation of LV mass and wall thickness, but advanced cardiac image analysis presents an opportunity for highthroughput mapping of genotype-phenotype associations in three dimensions (3D).Results: High-resolution cardiac magnetic resonance images were automatically segmented in 1,124 healthy volunteers to create a 3D shape model of the heart. Mass univariate regression was used to plot a 3D effect-size map for the association between wall thickness and a set of predictors at each vertex in the mesh. The vertices where a significant effect exists were determined by applying threshold-free cluster enhancement to boost areas of signal with spatial contiguity. Experiments on simulated phenotypic signals and SNP replication show that this approach offers a substantial gain in statistical power for cardiac genotype-phenotype associations while providing good control of the false discovery rate. This framework models the effects of genetic variation throughout the heart and can be automatically applied to large population cohorts.Availability: The proposed approach has been coded in an R package freely available at together with the clinical data used in this work.

Journal article

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