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Deep learning enables genetic analysis of the human thoracic aorta

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Abstract

Enlargement or aneurysm of the aorta predisposes to dissection, an important cause of sudden death. We trained a deep learning model to evaluate the dimensions of the ascending and descending thoracic aorta in 4.6 million cardiac magnetic resonance images from the UK Biobank. We then conducted genome-wide association studies in 39,688 individuals, identifying 82 loci associated with ascending and 47 with descending thoracic aortic diameter, of which 14 loci overlapped. Transcriptome-wide analyses, rare-variant burden tests and human aortic single nucleus RNA sequencing prioritized genes including SVIL, which was strongly associated with descending aortic diameter. A polygenic score for ascending aortic diameter was associated with thoracic aortic aneurysm in 385,621 UK Biobank participants (hazard ratio = 1.43 per s.d., confidence interval 1.32–1.54, P = 3.3 × 10−20). Our results illustrate the potential for rapidly defining quantitative traits with deep learning, an approach that can be broadly applied to biomedical images.

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Fig. 1: Study overview.
Fig. 2: Genome-wide association study results for ascending and descending thoracic aorta diameter.
Fig. 3: Gene-level association tests.
Fig. 4: snRNA-seq analyses in human aorta.
Fig. 5: Cumulative incidence of thoracic aortic aneurysm or dissection stratified by polygenic score.

Data availability

UK Biobank data is made available to researchers from universities and other research institutions with genuine research inquiries, following IRB and UK Biobank approval. Full GWAS summary statistics for ascending and descending thoracic aortic measurements are available at the Broad Institute Cardiovascular Disease Knowledge Portal (http://www.broadcvdi.org). Single nucleus RNA sequencing data are publicly available at the Broad Institute’s Single Cell Portal (accession no. SCP1265, https://singlecell.broadinstitute.org/single_cell) and at the National Center for Biotechnology Information’s Gene Expression Omnibus Database (accession no. GSE165824). The dbGAP accession number for aortic phenotypes used in FHS replication is phs000007.v30.p11. All other data are contained within the article and its supplementary information, or are available upon reasonable request to the corresponding author.

Code availability

The code used to identify connected components is available as a Go library at https://github.com/carbocation/genomisc/tree/master/overlay and a README is provided in that folder to demonstrate library usage.

Change history

  • 10 December 2021

    In the version of this article initially published online, the link for Supplementary Tables 1–21 was missing and has been restored as of 10 December 2021.

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Acknowledgements

This work was supported by the Fondation Leducq grant no. 14CVD01 (P.T.E.); by grants from the National Institutes of Health no.1R01HL092577 (P.T.E.), no. R01HL128914 (P.T.E.), no. K24HL105780 (P.T.E.), no. R01HL134893 (J.E.H.), no. R01HL140224 (J.E.H.), no. 5K01HL140187 (N.R.T.), no. T32HL007208 (S.K.), no. R01HL128914 (E.J.B.), no. 2R01HL092577 (E.J.B.), no. 1R01HL141434 (E.J.B.), no. 2U54HL120163 (E.J.B.), no. 1R01HL139731 (S.A.L.), no. T32HL007208 (E.L.C.), no. K08HL159346 (J.P.P.); by a grant from the American Heart Association Strategically Focused Research Networks (P.T.E.); by the American Heart Association grants no. 18SFRN34110082 (E.J.B.), no. 18SFRN34110082 (A.W.H.), no. 18SFRN34110082 (L.-C.W.), no. 18SFRN34250007 (S.A.L.); by a John S LaDue Memorial Fellowship (J.P.P.); by a Sarnoff Scholar Award (J.P.P.); by a Career Award for Medical Scientists from the Burroughs Wellcome Fund (A.G.B.); and by the Fredman Fellowship for Aortic Disease (M.E.L.) and the Toomey Fund for Aortic Dissection Research (M.E.L.). The Precision Cardiology Laboratory is a joint effort between the Broad Institute and Bayer AG. The rapid autopsy effort was funded by the Susan Eid Tumor Heterogeneity Initiative.

Author information

Affiliations

Authors

Contributions

J.P.P. and P.T.E. conceived of the study. J.P.P. and M.N. annotated images. J.P.P., M.D.C., S.J.F., S.F.F., S.H.C., H.L., E.L.C. and M.N. conducted bioinformatic analyses. E.L.C., A.A., A.-D.A., N.R.T., D.J. and J.R.S. contributed to the rapid autopsy human aorta analysis. H.L., R.S.V., E.J.B. and U.H. contributed to the GWAS replication. J.P.P., M.E.L. and P.T.E. wrote the paper. S.K., A.G.B., L.-C.W., P.B., A.W.H., C.R., S.K.V., R.M.G., C.M.S., J.E.H., S.A.L. and A.A.P. contributed to the analysis plan or provided critical revisions.

Corresponding author

Correspondence to Patrick T. Ellinor.

Ethics declarations

Competing interests

J.P.P. and A.G.B. have served as consultants for Maze Therapeutics. A.-D.A. and C.M.S. are employees of Bayer US LLC (a subsidiary of Bayer AG), and may own stock in Bayer AG. D.J. is supported by grants from Genentech, Eisai, EMD Serono, Takeda, Amgen, Celgene, Placon Therapeutics, Syros, Petra Pharma, InventisBio, Infinity Pharmaceuticals and Novartis. D.J. has also received personal fees from Genentech, Eisai, EMD Serono, Ipsen, Syros, Relay Therapeutics, MapKure, Vibliome, Petra Pharma and Novartis. A.A.P. is employed as a Venture Partner at GV; he is also supported by a grant from Bayer AG to the Broad Institute focused on machine learning for clinical trial design. J.E.H. is supported by a grant from Bayer AG focused on machine learning and cardiovascular disease and a research grant from Gilead Sciences. J.E.H. has received research supplies from EcoNugenics. P.B. is supported by grants from Bayer AG and IBM applying machine learning in cardiovascular disease. P.T.E. is supported by a grant from Bayer AG to the Broad Institute focused on the genetics and therapeutics of cardiovascular diseases. P.T.E. has also served on advisory boards or consulted for Bayer AG, Quest Diagnostics, MyoKardia and Novartis. S.A.L. receives sponsored research support from Bristol Myers Squibb/Pfizer, Bayer AG, Boehringer Ingelheim and Fitbit, and has consulted for Bristol Myers Squibb/Pfizer and Bayer AG, and participates in a research collaboration with IBM. The Broad Institute has filed for a patent on an invention from P.T.E., M.E.L. and J.P.P. related to a genetic risk predictor for aortic disease.

Additional information

Peer review information Nature Genetics thanks Chayakrit Krittanawong, Julie De Backer and Richard Redon for their contribution to the peer review of this work.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 Aortic size by age and sex.

The length of the minor elliptical axis of aorta at its maximum size during the cardiac cycle (that is, the diameter) is shown for the ascending aorta (left) and the descending aorta (right). The x-axis represents the participant’s age at the time of cardiac MRI, and the y-axis represents the size of aorta. Each point represents one person’s measurements; men are plotted in turquoise and women in red. Sex-specific locally weighted scatterplot smoothing (LOESS) curves are overplotted. Each point represents one of the 42,518 participants who passed imaging quality control for at least one of the ascending or descending aorta measurements: 40,363 had accepted measurements for ascending aorta, and 41,415 had accepted measurements for descending aorta.

Extended Data Fig. 2 GWAS sample flow diagram.

The GWAS sample flow diagram depicts the sample filtering process that led to the specific samples being chosen for the ascending and descending aortic diameter GWAS.

Extended Data Fig. 3 GWAS QQ plots.

Quantile-quantile plots showing the theoretical distribution of P values under a uniform distribution (x-axis) versus the observed distribution within the sample (y-axis) are displayed for the ascending and descending aorta GWAS summary statistics. The plots are stratified by minor allele frequency (‘maf_bin’): ‘common’ denotes SNPs with MAF > 0.05, low frequency with 0.005 < MAF ≤ 0.05, and rare with 0.001 < MAF ≤ 0.005. Variants with MAF < 0.001 were excluded from the analysis.

Extended Data Fig. 4 GWAS replication in the Framingham Heart Study.

a,b, For lead SNPs from the main UK Biobank GWAS that could be identified in a GWAS from FHS, each SNP is plotted based on the UK Biobank Z score (x-axis) and the FHS Z score (y-axis). 72 SNPs for ascending aortic diameter (a) and 41 SNPs for descending aortic diameter (b) could be identified in FHS and are plotted here. SNPs where the direction of effect is in agreement between FHS and UK Biobank are plotted in blue, while those with opposite direction of effect are marked in red.

Extended Data Fig. 5 Genetic correlation with continuous traits.

The genetic correlation between continuous traits and the ascending (top) and descending (bottom) thoracic aorta in the UK Biobank are represented in volcano plots. Of the 281 tested traits, genetic correlation with 257 traits was computable in the ascending aorta and with 256 traits in the descending aorta. The x-axis represents the magnitude of genetic correlation, while the y-axis represents the -log10 of the genetic correlation P value, based on ldsc. Traits achieving Bonferroni significance are colored red (for positive genetic correlation) or blue (for negative genetic correlation). The top 10 positively and negatively associated traits are labeled. The underlying data are available in Supplementary Table 10.

Extended Data Fig. 6 Cell type-specific gene expression at the WWP2 locus.

Cell-type specificity of genes with expression data within 500 kb of the lead SNP near WWP2. As with Fig. 4, the size of each square represents the average log2(Expr) for a gene across all nuclei in a given cluster. The color represents the log fold-change comparing the expression of the given gene in each cluster to all other clusters based on a formal differential expression model. A dot represents significant up- or down-regulation in the given cluster based on a Benjamini-Hochberg correction for multiple testing at FDR < 0.01. Expr, normalized nucleus-level expression calculated as the number of counts of a gene divided by the total number of counts in the nucleus and multiplied by 10,000; FC, fold-change.

Extended Data Fig. 7 MAGMA gene set associations.

Gene sets enriched in MAGMA analysis of the GWAS of the ascending (top) and descending (bottom) thoracic aorta are represented in volcano plots. The x-axis represents the magnitude of estimated effect of a pathway-based gene set on the aortic trait, while the y-axis represents the -log10 of the MAGMA association P value. Pathways achieving Bonferroni significance are colored red and labeled. The underlying data are available in Supplementary Tables 17 and 18.

Supplementary information

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Supplementary Note, Figs. 1–5.

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Supplementary Tables 1–21.

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Pirruccello, J.P., Chaffin, M.D., Chou, E.L. et al. Deep learning enables genetic analysis of the human thoracic aorta. Nat Genet 54, 40–51 (2022). https://doi.org/10.1038/s41588-021-00962-4

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