Abstract
The inner surfaces of the human heart are covered by a complex network of muscular strands that is thought to be avestige of embryonic development.1, 2 The function of these trabeculae in adults and their genetic architecture are unknown. To investigate this, we performed a genome-wide association study using fractal analysis of trabecular morphology as an image-derived phenotype in 18,096 UK Biobank participants. We identified 16 significant loci containing genes associated with haemodynamic phenotypes and regulation of cytoskeletal arborisation.3, 4 Using biomechanical simulations and human observational data, we demonstrate that trabecular morphology is an important determinant of cardiac performance. Through genetic association studies with cardiac disease phenotypes and Mendelian randomisation, we find a causal relationship between trabecular morphology and cardiovascular disease risk. These findings suggest an unexpected role for myocardial trabeculae in the function of the adult heart, identify conserved pathways that regulate structural complexity, and reveal their influence on susceptibility to disease.
The chambers of the mature human heart have a complex inner surface whose function is unknown. Unlike the smooth endothelium of the great vessels, the endocardial surfaces of both ventricles are lined by a fenestrated network of muscular trabeculae which extend into the cavity. Their embryological development is driven by highly-conserved signalling pathways involving the endocardium-myocardium and extra-cellular matrix that regulate myocardial proliferation during cardiac morphogenesis.2, 5–9 Cell lineage tracing suggests that trabeculae have a molecular and developmental identity which is distinct from the compact myocardium.10 The high surface area of trabeculae enables nutrient and oxygen diffusion from blood pool to myocardium before the coronary circulation is established.1 Trabeculae are also vital to formation of the conduction system.11 Theoretical analyses have proposed that their complex structure may contribute to efficient intra-ventricular flow patterns.12–14 While hypertrabeculation is observed as a feature of some genetically-characterised cardiomyopathies,15 the physiological function of trabeculae in adult hearts, their genetic architecture, and potential role in common disease have not been determined.
The distinguishing trait of trabeculae is their branching morphology and the degree of such biological complexity in the heart can be quantified by fractal dimension (FD) analysis of cardiac magnetic resonance (CMR) imaging.8 In a replicated genome-wide association study (GWAS), using FD as an image-derived phenotype, we identify loci linked with trabecular morphology. Knockout models of loci-associated genes showed a marked decrease in trabecular complexity. Using biomechanical modelling and human observational data, we find a causal relationship between myocardial trabeculation and ventricular performance, with Mendelian randomisation showing that reduced trabecular complexity is causally associated with the risk of heart failure.
Data overview
UK Biobank is a prospective cohort study collecting deep genetic and phenotypic data on approximately 500,000 individuals from across the United Kingdom, aged between 40 and 69 at recruitment.16 Of these, 100,000 participants are being recalled for enhanced phenotyping which includes CMR imaging.17 Non-invasive data on a range of haemodynamic parameters are also collected at the time of imaging. Following automated image-quality control18 and exclusion of subjects with missing covariates, 18,096 unrelated participants that formed a well-mixed population of European ethnicity (Extended Data Fig 1a) were used for discovery (Extended Data Table 1 and Fig. 1d). A separate UK Biobank dataset of 6,536 participants and a further independent cohort of 1,129 healthy adults (UK Digital Heart study) were used for validation (Extended Data Fig 1b).19 Disease associations were assessed in 510 patients with dilated cardiomyopathy (DCM) (Extended Data Table 1) of which 307 also had CMR imaging, as well as in summary GWAS data for heart failure of mixed aetiology from 47,309 cases and 930,014 controls, across 26 studies of European ancestry from the HERMES consortium.20
Fractal analysis of trabeculation
We used a fully convolutional network for automated left ventricular segmentation and volumetry of CMR images.21 Using edge detection of the endocardium we derived a scale invariant FD ratio for each slice, where a higher value indicates a greater degree of surface complexity (Fig. 1).22 To account for variations in cardiac size and for consistent anatomical comparisons within and between populations we interpolated the data to 9 slices (Extended Data Fig 2) which were equally divided into basal, mid-ventricular and apical thirds. An identical analytic pipeline was performed in the validation cohorts. We also showed that fractal analysis could be performed on other imaging modalities (Extended Data Fig 3a). In addition, we used motion analysis to determine spatial components of myocardial strain (Extended Data Fig 3b).
Genome-wide association analysis
We first explored variation in FD across slices using principal components analysis and noted multiple modes of variation. (Extended Data Fig 3c). We then used individual slice level GWAS to test association for different modes of variation, followed by meta-analysis across the slices to capture any additional global associations.
We performed a linear model for genetic association of 14,134,301 genetic variants on each of the 9 interpolated slice FD measures (Fig. 2a, Supplementary Data 1) of 18,096 individuals using anthropometric variables and genetic principal components as covariates. These genome-wide association studies showed low inflation and many individual loci passing the commonly used genome-wide association threshold of 5*10−8 after p-value adjustment for multiple testing by the effective number of tests (Teff = 6.6; Extended Data Fig 4a, b; Supplementary Table 1). Figure 2b shows the resulting 16 independent loci from the meta-analysis of the per-slice GWAS summary statistics and the individual slice(s) which the loci are associated with. Four loci were only discovered using this joint meta-analysis approach (Fig. 2b, orange circles); the remaining 12 loci show patterns of association that extend over multiple adjacent slices with varying effect sizes from base to apex (Extended Data Fig 5a). We conducted two additional, analogous association studies including either end-diastolic volume or myocardial strain as covariates. Both studies led to the discovery of the same loci, indicating that FD associations are independent of ventricular size and strain (Extended Data Fig 4c, d).
To replicate our findings, we analysed the genetic associations of the discovered loci with trabeculation-derived FD measurements in two separate cohorts: CMR images of 6,536 UK Biobank participants (released after the initial discovery GWAS) and 1,129 healthy volunteers from the UK Digital Heart Study. We applied the same image analysis pipeline and conducted an equivalent genetic association study on genetic variants in the 16 loci associated in the discovery cohort. In the larger UK Biobank replication cohort, eight of the loci replicated the results observed in the discovery cohort (Supplementary Tables 2 and 3). In the smaller, healthy volunteer replication cohort fewer associations passed the Bonferroni-adjusted p-value threshold (threshold pBon ferroni = 0.003; 2 variants, Supplementary Table 4). In both replication studies, the estimates of effect direction were highly concordant with the original discovery effect sizes (UK Biobank: 97% and UK Digital Heart: 91% of comparisons concordant) and showed correlation of the effect size estimates (r 2 = 0.87 and r 2 = 0.50, respectively; Extended Data Fig 5b, c). Permutation tests generating empirical concordance distributions show that the observed concordances are unlikely to be observed by chance (pempirical < 10−5).
Associations of discovered loci
We systematically analysed the 16 discovered loci with the rich genetic resources of other studies, drawing from both the extensive GWAS Catalog,23 and more recent phenome-wide associations (PheWAS) from UK Biobank.24 Extended Data Table 2 summarises our findings (for details on loci see Supplementary Table 5).
Ten of the 16 loci are also associated with at least one component of heart function, such as pulse rate, QRS duration, left ventricular structure and function (Supplementary Data 2 and Supplementary Table 6). We compared our loci to the extensive GTEx catalog25 of gene expression quantitative trait loci (eQTL; Extended Data Table 2, Supplementary Data 3 and Extended Data Fig 6a)). Nine of the 16 loci showed an overlap with a GTEx locus; in eight cases at least one of the eQTL tissues was either cardiac tissue or skeletal muscle; in one case the only significant tissue was transformed fibroblasts. A particularly strongly annotated association is on chromosome 8, in a region of open chromatin that is an eQTL for the MTSS1 gene (Fig. 2c). This locus is also associated with a variety of cardiac structure and function phenotypes (Extended Data Table 2, rs35006907), and the lead genetic variant located is in a region of open chromatin in heart tissues (ENSR00000868700, ENSEMBL regulatory build, Ensembl release 9926). Representative myocardial borders associated with this locus are depicted in Extended Data Fig 3d.
As well as previously reported associations, we were interested in the functional annotations of our GWAS results. As the per-slice GWAS (Fig. 2b) suggested regionally-driven signals, we conducted genome-wide associations of FD in basal (1-3), mid-ventricular (4-6) and apical (7-9) slices. We analysed all genetic variants of these association results for enrichment in regulatory and functional annotations. The strongest associations of the genetic loci were to open-chromatin regions in fetal heart tissue, particularly in the mid and apical regions (Extended Data Fig 6b).
Overall the discovered loci are mainly linked with either molecular or physiological cardiac phenotypes. Some loci are likely developmental, such as the locus on chromosome 8 associated with MTSS1, affecting many aspects of cardiac function whereas other loci have more specific associations. Amongst the well-annotated loci electrophysiological, haemodynamic and structural traits are common themes, for example rs17608766 which is associated with QRS duration, blood pressure, cardiac anatomy and eQTLs to three genes in skeletal muscle.
Knockout models
To gain further confidence in the role of the trabeculae-associated genes GOSR2 and MTSS1, we assessed in vivo CRISPR- Cas9-mediated gene knock-outs (KO) in medaka (Oryzias latipes). Crispant embryos were phenotypically evaluated at 4 days post fertilization when significant steps of cardiovascular development are complete. Two batches (replicates) of crispants were initially classified into three main categories (Fig. 3a). A significant proportion of embryos were dead after mtss1 KO. In viable embryos, we found retardation of development with a range of severe, sub-lethal to moderate phenotypes for both gosr2 and mtss1 crispants. Features observed on the level of the cardiovascular system were further described using qualitative phenotypic terms including morphological abnormalities, atrioventricular (AV) block, reverse heart looping, and haemorrhage or coagulation (Fig. 3b). Fig. 3c shows an example of a moderately affected mtss1 crispant embryo. To specifically address the endocardial structure, entire heart volumes of mtss1 crispant and control embryos (myl7::EGFP reporter line) were further analyzed at high resolution using light-sheet microscopy. Surface rendering revealed a marked reduction of trabeculation in the mtss1 crispant compared to the control (Fig. 3d).
Cardiac function
To understand the influence of myocardial trabeculation on cardiac function, we used a biomechanical simulation of the heart in a haemodynamic circuit. This allowed us to vary trabecular morphology selectively and observe its effects on ventricular performance in comparison to equivalent observational data in humans.
A visualisation of cardiac mechanics during systole and diastole is provided by plotting a closed loop describing the relationship between left ventricular pressure and left ventricular volume at multiple time points during a complete cardiac cycle (Fig. 4a).27, 28 To understand how trabeculae influence cardiac function we therefore assessed the relationship between FD and pressure-volume parameters of the left ventricle both in human populations and in silico models. We performed this in the UK Biobank participants by analysing non-invasive estimates of central pressures combined with volumetric CMR data. In parallel, we developed a cardiovascular simulation, using finite element analysis of the left ventricle in a haemodynamic circuit. In this simulation, we selectively varied trabecular complexity, under the same initial loading and boundary conditions, to observe the consequent effect on stroke work and contractility. In UK Biobank participants, increasing FD was associated with higher stroke volume, stroke work and cardiac index (standardised β = 0.52, 0.67, 0.12), findings which were concordant with the biomechanical simulation across a range of filling pressures (Fig. 4a). Together these results suggest a causal relationship between trabecular complexity and ventricular performance (Extended Data Fig 7a-c, Supplementary Table 7). Trabeculae also give rise to the ventricular conduction system during embryonic heart development,11 and we found a positive correlation in UK Biobank of QRS duration with FD (Extended Data Fig 3e).
Disease association
Finally, we explored the relationship of trabeculae-associated loci with cardiovascular disease using broad genetic correlation analyses and disease-specific locus and phenotype analyses. We first applied cross-trait LD score regression to screen for genetic correlations between trabecular complexity and 732 traits available on LDhub (Supplementary Data 4).29 The strongest positive and negative genetic correlations were with hypertension phenotypes and diagnosed vascular or heart problems (Extended Data Fig 7e-g), respectively.
We then analysed CMR images (n=307) in patients diagnosed with DCM, a disease of the myocardium that may progress to heart failure. We observed that these patients show higher trabecular FD than controls especially towards the base and apex of the left ventricle (Fig. 4b, linear mixed model -log 10(p) = 2846).
In a logistic regression association analysis between trabeculation-associated loci and DCM, we find two loci with genetic association (pempirical < 0.05) even in this more modestly sized patient cohort (510 DCM cases). We then used summary case-control GWAS data with a clinical diagnosis of heart failure (HF) of any aetiology from the HERMES Consortium to directly explore the associations of trabeculation-linked loci. We found that two of the loci are also associated with HF at a Boferroni-adjusted significance level (p < 0.003, Supplementary Table 8).
For both DCM and HF, we find a negative correlation with trabeculation, i.e. loci associated with decreasing trabeculation are associated with increased susceptibility to disease - with the locus around GOSR2 (Extended Data Fig 7d) showing a strong association in both cohorts. To test the hypothesis that trabecular morphology is causally-related to heart failure, we used a two-sample Mendelian randomisation (MR) framework,30 with the discovered independent loci as instrumental variables.31 We used FD as our exposure variable and HF or DCM as outcomes. We tested a number of MR techniques, each addressing different assumptions (for details refer to Supplementary Note 1.1: Mendelian Randomisation) and found parameter estimates that support a causal relationship between trabecular morphology and both HF (Fig. 4c; Supplementary Table 9 and Extended Data Fig 8a), and DCM (Fig. 4d; Supplementary Table 9 and Extended Data Fig 8b). In both populations an increase in trabeculation leads to decreased risk of disease. The directionality of the MR associations, with trabeculation causally upstream of HF and DCM, was confirmed by MR Steiger test32, Supplementary Table 10). Using MR Egger, we detected weak pleiotropic effects for MR on HF; for MR on DCM, none were observed (Supplementary Table 12). Furthermore, estimates of the F-statistic indicate no weak instrument bias (Supplementary Table 11 and Supplementary Note 1.1: Mendelian Randomisation, Limitations).
Discussion
Myocardial trabeculae were first described by the early human anatomists,33 and although they are remarkably well-conserved in vertebrate evolution,34 beyond a role in facilitating oxygenation of the developing fetal heart their function in adults has remained an enigma.35 Deep learning image analysis enabled us to perform the first reported GWAS of trabecular morphology - using fractal dimension to quantify their characteristic geometric complexity. We found associations with trabecular complexity in loci related to cardiac function and electrocardiographic phenotypes, gene expression variation in cardiac tissues and cardiac development chromatin annotation that were independent of biophysical variables, ventricular volume and myocardial strain.
Two discovered loci (MTSS1, GOSR2) point to molecular pathways involved in cytoskeletal actin dynamics. Variants of MTSS1 are known to be associated with myocardial geometry and cardiac function in mouse models and patient populations.36–38 Interference with mtss1 function in medaka was characterised by a range of phenotypes that included marked reduction in trabeculation. MTSS1 is also highly expressed in cerebellar Purkinje cells where it regulates dendritic complexity by promoting the branching of actin filaments and inhibiting the formation of straight filaments.3 Similarly, truncating mutations in GOSR2 cause cytoskeletal fragmentation with reduced elaboration of neuronal dendritic arbors.4 Dichotomous fractal branching greatly amplifies the surface area of tissues whether for information processing (neurons) or haemodynamic effects (heart),39 suggesting these discovered loci may play a critical role in regulating arborisation traits across different organs.
Observational data in UK Biobank showed that trabecular complexity was associated with increasing stroke work – and biomechanical simulations provided concordant data showing that trabeculae have a load independent effect on left ventricular diastolic filling, contractility and systemic blood pressure. The architecture of trabeculae, at the interface between intra- cardiac flow and the compact myocardium, may therefore be important in explaining individual variation in cardiac efficiency. Furthermore, we found that trabecular morphology in humans was associated with intra-ventricular conduction – a discovery that implicates these complex structures in cardiac electrophysiology as well as mechanical function.40
Our MR analyses support a causal role for trabecular morphology in both mixed aetiology heart failure and DCM. Taken with the observation of higher FD in disease phenotypes and our computational modelling of trabecular function, these findings suggest that trabeculae maintain cardiac performance in both healthy and failing hearts by increasing contractility and stroke work. We also found a number of loci that overlap with well-established cardiac genes (TTN, TNNT2), linked to sarcomeric function and cardiac morphogenesis, that are related to a spectrum of hyper-trabeculation phenotypes.41–43 This suggests that genes linked to primary cardiomyopathies highlight molecular pathways that are important for trabecular formation and cardiac function more generally.44
The triangulation of theoretical models, observational data and genomics45 is persuasive evidence that trabeculae are not simply vestigial features of development but are unexpected determinants of cardiac performance in adult hearts. Understanding the pathways which regulate the development of such complex biological structures provides a foundation for exploring new causal mechanisms in common cardiovascular diseases.
Extended Data
Extended Data Table 1. Participant characteristics.
Characteristics | UKBB (discovery) | UKBB (replication) | UKDH | DCM |
---|---|---|---|---|
Participants | 18,096 | 6,536 | 1,129 | 510 |
Age (years) | 55 ± 7 | 55 ± 7 | 41 ± 13 | 54 ± 15 |
Body surface area (m2) | 1.90 ± 0.21 | 1.90 ± 0.22 | 1.85 ± 0.20 | 1.99 ± 0.28 |
Sex (m/f) | 9402/8694 | 3378/3158 | 621/508 | 357/153 |
Haemodynamics | ||||
Systolic BP (mmHg) | 137 ± 18 | 136 ± 18 | 120 ± 14 | 122 ± 31 |
Diastolic BP (mmHg) | 81 ± 10 | 82 ± 10 | 79 ± 9 | 73 ± 20 |
Heart rate (beats/minute) | 62 ± 10 | 63 ± 11 | 62 ± 21 | 75 ± 24 |
Cardiac output (1/min) | 5.5 ± 1.3 | 5.4 ± 1.3 | 6.0 ± 2.0 | 7.0 ± 3 |
Left ventricular Volumetry | ||||
Ejection fraction (%) | 59.5 ± 6.0 | 59.6 ± 6.3 | 65.3 ± 6.9 | 40 ± 17 |
End-systolic volume (ml/m2) | 31.9 ± 8.6 | 31.6 ± 8.9 | 28.2 ± 7.9 | 79 ± 43 |
End-diastolic volume (ml/m2) | 78.3 ± 13.9 | 77.7 ± 14.0 | 81.2 ± 14.4 | 127 ± 52 |
Mass (g/m2) | 45.2 ± 8.5 | 44.9 ± 8.3 | 62.8 ± 15.3 | 91 ± 39 |
Mean global ED | 1.169 ± 0.028 | 1.169 ± 0.029 | 1.215 ± 0.038 | 1.218* ± 0.075 |
Extended Data Table 2. Annotations of trabeculation-associated loci.
CHR | BP | SNP | nPCG | PheWAS | GWAS | eQTLS | Tissues |
---|---|---|---|---|---|---|---|
1 | 61895257 | rs6587924 | TM2D1 | - | - | - | - |
1 | 155962067 | rs35770803 | ARHGEF2 | - | - | - | - |
1 | 201332020 | rs 1892027 | TNNT2 | - | - | PKP1 | Skeletal muscle, adipose |
2 | 179531078 | rs71394376 | TTN | - | - | - | - |
3 | 73554922 | rs4677294 | PDZRN3 | - | - | PDZRN3, PDZRN3-AS1 | LV, artery, AA, aorta |
3 | 169191428 | rs 1918978 | MECOM | - | - | - | - |
5 | 153871841 | rs 10076436 | HAND1 | - | - | SAP30L | Transformed fibroblasts |
6 | 31081940 | rs3130976 | PSORS1C1 | 84 phenotypes | Nephropathy, adult asthma, CLE | 18 genes | 37 tissues including LV and AA |
6 | 118690014 | rs9320648 | PLN | Pulse rate, diastolic BP (4195, 102) | - | SSXP10, CEP85L, SLC35F1 | 13 tissues including AA |
8 | 11794962 | rs698l46l | DEFB136 | - | C-reactive protein | 28 genes | 35 tissues including LV and AA |
8 | 125859817 | rs35006907 | MTSS1 | - | Ejection fraction, fractional shortening, LV internal dimension in systole and diastole, relative wall thickness, LV internal dimension, atrial fibrillation | MTSS1, LINC00964 | LV, lung, AA |
12 | 115381071 | rs7132327 | TBX3 | - | Global electrical heterogeneity phenotypes, QRS complex, QRS duration, PR segment, PR interval | - | - |
14 | 71990847 | rs71105784 | SIPA1L1 | - | QRS complex, QRS duration, mitral valve prolapse | - | - |
17 | 45013271 | rs 17608766 | GOSR2 | Systolic BP, hypertension (4080, 6150_4, 6150_100, 20002_1065, 103) | Systolic BP, QRS duration, pulse pressure, BP, aortic root size, atrial fibrillation | RPRML, GOSR2, CDC27, RP11-63A1.2, RP11-156P1.3 | Skeletal muscle, testis, adrenal gland |
19 | 7581244 | rs 113394178 | ZNF358 | - | - | ZNF358 | AA |
22 | 33127481 | rs3788488 | TIMP3 | - | - | - | - |
Supplementary Material
Acknowledgments
The research was supported by the Medical Research Council, UK (MC-A651-53301); British Heart Foundation (NH/17/1/32725, RG/19/6/34387, RE/18/4/34215); Wellcome Trust (107469/Z/15/Z); National Institute of Environmental Health Sciences (R01 ES029917-02); Heidelberg University; the Simons Center for Quantitative Biology at Cold Spring Harbor Laboratory; and the National Institute for Health Research Biomedical Research Centre based at Imperial College Healthcare NHS Trust and Imperial College London. P.M.M. also has been in receipt of generous personal and research support from the Edmond J Safra Foundation and Lily Safra, an NIHR Senior Investigator’s Award, the Medical Research Council and the UK Dementia Research Institute. R.T.L. is supported by a UKRI Health Data Research Rutherford Fellowship (MR/S003754/1). A.H. is supported by a BHF PhD Studentship. J.G. is supported by a Research Center for Molecular Medicine (HRCMM) Career Development Fellowship, the MD/PhD program of the Medical Faculty Heidelberg, the Deutsche Herzstiftung e.V. (S/02/17), and by an Add-On Fellowship for Interdisciplinary Science of the Joachim Herz Stiftung.
Research funding for cohorts used in Mendelian Randomisation: NIHR Cardiovascular Biomedical Research Unit of Royal Brompton and Harefield NHS Foundation Trust (DCM cohort); Funding information for HERMES participating studies is detailed in Shah et al. 2020 (https://doi.org/10.1038/s41467-019-13690-5); data aggregation and downstream bioinformatics were funded through grants from the MRC Proximity to Discovery scheme, the NIHR UCLH Biomedical Research Centre, and the EU/EFPIA Innovative Medicines Initiative 2 Joint Undertaking BigData@Heart grant no. 116074.
The authors would like to thank Dr Hideaki Suzuki, previously of the Department of Medicine, Imperial College London, for his work on pre-processing the image data, and Prof Roberto Fumero, at the Politecnico di Milano, Italy, for advice on the finite element modelling. The authors thank Virginie Uhlmann (EMBL-EBI) for advice on radial image registration. We also thank L. Schertel and C. Baader for sgRNA production and the Wittbrodt Laboratory for critical discussion and support. We also acknowledge Ben Statton and Marjola Thanaj at Imperial College London for assisting with data pre-processing.
Footnotes
Competing interests
The authors declare no competing interests.
Author contributions
H.V.M. and T.J.W.D. performed the formal analysis and co-wrote the manuscript; M.S. and M.L.C. performed the in silico modelling; T.J.W.D and A.M. collected and analysed image data; R.T.L, A.H., J.S.W. and S.K.P collected and analysed the clinical data; W.B., P.T., J.C. and D.R. developed the computational phenotyping; J.G., T.T., and J.W. detailed the experimental strategy for the medaka validation; J.G. and T.T. designed and performed CRISPR-Cas9 knock-out experiments, and conducted phenotypic analysis under the guidance of J.W.; J.G. acquired LSM recordings, and analyzed and plotted the medaka knock-out and imaging data.; P.M.M., E.B., S.A.C. and D.P.O. provided critical interpretation of the results; E.B., S.A.C. and D.P.O. conceived the study, managed the project and revised the manuscript. All authors reviewed the final manuscript.
Data availability
The genetic and phenotypic UK Biobank data presented in this work are available to any bona fide researcher upon application to UK Biobank (https://bbams.ndph.ox.ac.uk/ams/). The research was conducted under access number 40616. The GWAS summary level data used in this study are publicly-available (https://www.ebi.ac.uk/gwas/, http://ldsc.broadinstitute.org/ and http://www.hermesconsortium.org/). Figures 1, 3 and 4 contain raw data which are provided as source data unless prior application to UK Biobank is required.
Code availability
The analysis code is freely available on GitHub (10.5281/zenodo.3698268).
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Availability Statement
The genetic and phenotypic UK Biobank data presented in this work are available to any bona fide researcher upon application to UK Biobank (https://bbams.ndph.ox.ac.uk/ams/). The research was conducted under access number 40616. The GWAS summary level data used in this study are publicly-available (https://www.ebi.ac.uk/gwas/, http://ldsc.broadinstitute.org/ and http://www.hermesconsortium.org/). Figures 1, 3 and 4 contain raw data which are provided as source data unless prior application to UK Biobank is required.
The analysis code is freely available on GitHub (10.5281/zenodo.3698268).