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Genetic architecture of cardiac dynamic flow volumes - Nature.com

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Abstract

Cardiac blood flow is a critical determinant of human health. However, the definition of its genetic architecture is limited by the technical challenge of capturing dynamic flow volumes from cardiac imaging at scale. We present DeepFlow, a deep-learning system to extract cardiac flow and volumes from phase-contrast cardiac magnetic resonance imaging. A mixed-linear model applied to 37,653 individuals from the UK Biobank reveals genome-wide significant associations across cardiac dynamic flow volumes spanning from aortic forward velocity to aortic regurgitation fraction. Mendelian randomization reveals a causal role for aortic root size in aortic valve regurgitation. Among the most significant contributing variants, localizing genes (near ELN, PRDM6 and ADAMTS7) are implicated in connective tissue and blood pressure pathways. Here we show that DeepFlow cardiac flow phenotyping at scale, combined with genotyping data, reinforces the contribution of connective tissue genes, blood pressure and root size to aortic valve function.

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Fig. 1: Overview of the concepts defining cardiac dynamic flow volumes analyzed in this study.
Fig. 2: Overview of the software pipeline.
Fig. 3: U-Net architecture of the segmentation model used in DeepFlow.
Fig. 4: Epidemiology of aortic valve regurgitation fraction in the UK Biobank population.
Fig. 5: Heritability and genetic correlation matrix of the studied cardiac measurements.
Fig. 6: Manhattan plots showing the GWAS results derived from a mixed-linear regression model using the SAIGE software.
Fig. 7: Cluster groups and gene interactions of the traits.
Fig. 8: Mendelian randomization results using the Bayesian-based model, CAUSE, to establish causal relationships between aortic area (exposure) and aortic valve regurgitation fraction (outcome).

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Data availability

UK Biobank data is accessible to researchers with genuine research purposes, subject to institutional review board and UK Biobank approval (http://www.ukbiobank.ac.uk/register-apply/). The GWAS summary statistics can be obtained from Zenodo at https://zenodo.org/record/8388416. The Genotype-Tissue Expression v.8 datasets can be found at https://www.gtexportal.org/home/datasets. The STRING database can be accessed at https://string-db.org/. The graphical representations of the Mask-R-CNN and the FPN model architectures can be accessed from Zenodo at https://doi.org/10.5281/zenodo.8384058. Any additional data is either within the article or its supplementary materials. Source data are provided with this paper.

Code availability

The custom software, including model segmentation training and validation, is freely available for non-commercial use (open-source CC-BY-NC license) at https://github.com/Urban90/deepFlow and https://zenodo.org/badge/latestdoi/487159922 (ref. 76).

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Acknowledgements

This research has been conducted using the UK Biobank Resource under applications 63735 and 22282. Figures were created with BioRender.com and LocusZoom.org. We acknowledge the following financial support: Deutsche Forschungsgemeinschaft Walter-Benjamin Program GO 3196/3-1, 707766—809341 (to B.G.); Leducq Foundation Transatlantic Network of Excellence 21CVD02 (to B.G., B.M., and E.A.); GREGoR U01HG011762 and R01HL142015 (to P.C.G.); Novo Nordisk Foundation NNF19OC0054265 (to T.M.S.); National Medical Research Council Singapore NMRC-IRG A-0006207-00-00 (to R.F.) and National University of Singapore NUS-MSRMP 2023 (to S.L.).

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Authors and Affiliations

  1. Departments of Medicine, Genetics, Computer Science and Biomedical Data Science, Stanford University, Stanford, CA, USA

    Bruna Gomes, Aditya Singh, Jack W. O’Sullivan, Theresia M. Schnurr, Pagé C. Goddard, David Amar, J. Weston Hughes, Francois Haddad, Michael Salerno, Stephen B. Montgomery, Victoria N. Parikh & Euan A. Ashley

  2. Department of Cardiology, Pneumology and Angiology, Heidelberg University Hospital, Heidelberg, Germany

    Bruna Gomes, Mykhailo Kostur & Benjamin Meder

  3. Informatics for Life, Heidelberg, Germany

    Bruna Gomes & Benjamin Meder

  4. Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore

    Shaun Loong & Roger Foo

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  1. Bruna Gomes
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Contributions

B.G. and E.A. designed the study. B.G. trained, validated and tested DeepFlow with contribution of A.S. and M.K.; conducted the GWAS and epidemiology-related analyses, prepared the supplementary material and tables; designed the figures with the contribution of A.S. and S.L. B.G., E.A. and V.P. wrote the manuscript with contributions of A.S., D.A., J.S., M.S., S.L., R.F. and F.H. B.M. gave advice in the conception of the project. A.S. performed software implementation of DeepFlow, as well as ensured cross-platform compatibility. A.S. contributed to the time-to-event analyses of clinical outcomes in aortic valve regurgitation. D.A. contributed and advised on the Mendelian randomization analysis. T.M.S., P.C.G., S.B.M., S.L. and R.F. were instrumental in the revision of this work. P.C.G. performed the eQTL colocalization analysis. J.W.H. advised regarding the benchmarking against other deep-learning-based image segmentation models.

Corresponding author

Correspondence to Euan A. Ashley.

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Competing interests

B.M. is part of the Scientific Advisory Board of Fleischhacker GmbH. E.A. is the founder of Personalis, DeepCell, Svexa, Overtone and Parameter Health; advisor to Pacific Biosciences, SequenceBio, Nuevocor and Apple and a nonexecutive director of AstraZeneca. J.S. is a consultant for Google AI. S.B.M. is an advisor to BioMarin, Myome and Tenaya Therapeutics. V.P. is SAB for BioMarin and Lexeo Therapeutics; a consultant for Viz.ai and Nuevocor and has sponsored research from Biomarin, Inc. and Saliogen Therapeutics. M.S. receives research support from Siemens Healthineers and GE Healthcare and is a consultant to Medtronic, G3 and Imagion Biosciences. All other authors have no conflicts of interest to declare.

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Nature Genetics thanks the anonymous reviewers for their contribution to the peer review of this work.

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Supplementary information

Supplementary Information

Supplementary Tables 1–7, Supplementary Figs. 1–12, Supplementary Notes 1–15 and Supplementary Equations.

Reporting Summary

Supplementary Data 1

Annotations and cross-references with GWAS catalog for the independently associated SNVs, as well as genomic risk loci definitions with 12 tab-separated tables including six tables with annotated independently significantly and nominally associated SNVs across phenotypes (filename starting with ‘IndSigSNPs_annotated’; six tables defining the significantly and nominally associated genomic risk loci across phenotypes (filename starting with GenomicRiskLoci). Phenotype names are given in the filename: ‘iaorta_area’ for BSA-indexed aortic area; ‘vmax’ for peak forward aortic velocity; ‘tLVSV’ for BSA-indexed total LVSV; ‘fLVSV’ for BSA-indexed forward LVSV; ‘vmax_adjusted’: peak forward aortic velocity adjusted to the aortic area; ‘ao_regurgitation_fraction’: aortic valve regurgitation fraction for the annotated SNV tables, the columns denote: ‘GenomicLocus’:in which genomic risk locus the SNV is located (each genomic risk locus is assigned a number, shown in the GenomicRiskLoci tables); ‘IndSignSNP_GWAS’: the SNV id following the format chr:pos:affected allele:another allele in hg19, or rsid if available; ‘P value GWAS’: the respective P value in the GWAS; ‘nearestGene’: the nearest gene; ‘CADD’ the CADD score; ‘RDB’: Regulome DB score; ‘snp_PMID’: the SNV rsid described in the literature; ‘chr’: chromosome of the SNV described in the literature; ‘bp’: base pair position of the SNV described in the literature; ‘PMID’: PubMed id; ‘Study’: study name; ‘Trait’: phenotype studied in the GWAS reported in the literature; ‘ReportedGene’: reported gene associated with the SNV in the literature; ‘DateAddedToCatalog’: date the GWAS from the literature was added to the GWAS catalog; ‘P value_PMID’: calculated P value for the SNV reported in the literature. For the genomic risk loci tables: ‘GenomicLocus’: index of the genomic risk locus; ‘Novely’: indicated by an asterisk symbol, if the genomic risk locus is considered novel: either at the genomic level, if associations were found for traits not identified in previous GWAS analyses of comparable traits (aortic area defined in this study versus aortic functional/morphology traits; total LVSV versus left ventricular volumes); or at the phenotypic level, associations were observed for previously unstudied traits; ‘uniqID’: Unique ID of SNVs consisting of chr:position:reference allele:alternative allele; ‘rsID’: rsID of the top lead SNV based on dbSNP build 146; ‘chr’: chromosome of top lead SNV; ‘pos’: position of top lead SNV on hg19; ‘p’: P value of top lead SNV; ‘start’: start position of the locus; ‘end’: end position of the locus; nSNVs: the number of unique candidate SNVs in the genomic locus, including non-GWAS-tagged SNPs (which are available in the 1KG/Phase 3 reference panel. Candidate SNPs are all SNVs that are in LD (r2  < 0.6) with any of independent significant SNVs and either have a P value below the threshold for nominal significance or are only available in 1,000G; ‘nGWASSNPs’: The number of unique GWAS-tagged candidate SNVs in the genomic locus which is available in the GWAS summary statistics input file, which is a subset of ‘nSNPs’; ‘nIndSigSNPs’: the number of the independent (r2 < 0.6) significant SNVs in the genomic locus; ‘IndSigSNPs’: rsID of the independent significant SNVs in the genomic locus; ‘nLeadSNPs’: the number of lead SNVs in the genomic locus, lead SNVs are subset of independent significant SNVs at r2 of 0.1; ‘LeadSNPs’: rsID of lead SNVs in the genomic locus.

Supplementary Data 2

eQTL colocalization results. Table with the eQTL colocalization results across phenotypes with at least one nominally significantly associated SNV. Phenotypes: ‘iaorta_area’: BSA-indexed aortic area; ‘Vmax’: peak forward aortic velocity; ‘tLVSV’: BSA-indexed total LVSV; ‘fLVSV’: BSA-indexed forward LVSV. ‘sentinel’: sentinel GWAS variants (with the strongest association) which were at least 1 megabase apart; ‘gwas’: phenotype analyzed in the GWAS; ‘qtl’ tissue type of the GTEx eQTLs version 8; ‘pval_gwas’: associated P value in the GWAS; ‘pval_eqtl’: GTEx P value of the top significant eQTL variant for the given gene in the given tissue within 10 kb of the GWAS variant; ‘identifier’: gene identifier; ‘gene_name’: name of the gene; ‘gene_biotype’: classification of a gene based on the type of transcript it produces and its associated function; ‘gene_description’: description of the gene; ‘COLOC_n_snps’: number of variants in this region (1 Mb window around the sentinel GWAS variant) that were present in both the GWAS and eQTL data and thus contributed to the colocalization estimate; ‘COLOC_h4’: posterior probability that both the gene eQTL and the trait of interest are driven by the same causal variant.

Supplementary Data 3

Pathway enrichment analysis results with STRING. Six tab-separated tables corresponding to the pathway enrichment analysis clusters (n = 3: ‘blue’, ‘green’ and ‘red’ clusters) per phenotype (BSA-indexed aortic area—‘iaorta_area’; and peak forward aortic velocity—‘Vmax’). ‘#category’: database used in the STRING analysis; ‘term ID’: unique identifier assigned to a term in a controlled vocabulary or ontology; ‘term description’: brief definition of the meaning and scope of the term id; ‘observed gene count’: number of genes included for analysis from GWAS results; ‘background gene count’: number of proteins in the reference genome (PMID 33237311); ‘strength’: strength of an interaction, which is represented by a numeric value between 0 and 1, where 1 indicates the highest level of confidence and 0 indicates the lowest level of confidence, moreover the strength score reflects the degree of experimental support, co-expression correlation, text mining, that support the interaction between the two genes/proteins; ‘FDR’: false discovery rate; ‘matching proteins in your network (IDs)’: identifiers of the gene/proteins in the network; ‘matching proteins in your network (labels)’: names of the genes/proteins in the network.

Supplementary Data 4

Tissue enrichment results using the DEPICT software. Four tab-separated files with the tissue enrichment results using the DEPICT software for the phenotypes aortic area (‘iaorta_area’), aortic peak forward velocity (‘Vmax’), aortic regurgitation fraction (‘regurgitation_fraction’) and aortic peak forward velocity adjusted to aortic area (‘Vmax_adjusted’). The column names indicate as defined by the DEPICT software: ‘MeSH term’: (Medical Subject Heading; http://www.nlm.nih.gov/mesh/) term for the tissue or cell type annotation; ‘name’: name of the tissue or cell type; ‘MeSH first level term’: description of the tissue or cell type annotation; ‘MeSH second level term’: more general description of the tissue or cell type annotation; ‘nominal P value’: nominal enrichment P value of tissue/cell type annotation (null hypothesis: genes in associated are not highly expressed in the given tissue or cell type): ‘FDR’: estimated FDR for the tissue or cell type; ‘tissue-specific expression z score gene’: genes highly expressed in tissue/cell type and overlapping with associated loci. The z score denotes the level of tissue-specific expression.

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Source Data Fig. 5

Statistical source data.

Source Data Fig. 8

Statistical source data.

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Gomes, B., Singh, A., O’Sullivan, J.W. et al. Genetic architecture of cardiac dynamic flow volumes. Nat Genet (2023). https://ift.tt/zFTRnN6

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