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Genomic analyses implicate noncoding de novo variants in congenital heart disease

Nature Genetics volume 52, pages 769–777 (2020)Cite this article

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Abstract

A genetic etiology is identified for one-third of patients with congenital heart disease (CHD), with 8% of cases attributable to coding de novo variants (DNVs). To assess the contribution of noncoding DNVs to CHD, we compared genome sequences from 749 CHD probands and their parents with those from 1,611 unaffected trios. Neural network prediction of noncoding DNV transcriptional impact identified a burden of DNVs in individuals with CHD (n = 2,238 DNVs) compared to controls (n = 4,177; P = 8.7 × 10−4). Independent analyses of enhancers showed an excess of DNVs in associated genes (27 genes versus 3.7 expected, P = 1 × 10−5). We observed significant overlap between these transcription-based approaches (odds ratio (OR) = 2.5, 95% confidence interval (CI) 1.1–5.0, P = 5.4 × 10−3). CHD DNVs altered transcription levels in 5 of 31 enhancers assayed. Finally, we observed a DNV burden in RNA-binding-protein regulatory sites (OR = 1.13, 95% CI 1.1–1.2, P = 8.8 × 10−5). Our findings demonstrate an enrichment of potentially disruptive regulatory noncoding DNVs in a fraction of CHD at least as high as that observed for damaging coding DNVs.

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Fig. 1: Analysis schematic.
Fig. 2: Enrichment of noncoding de novo variants with functionally relevant HeartENN scores.
Fig. 3: Genes with multiple de novo variants in prioritized human fetal heart enhancers.
Fig. 4: Massively parallel reporter assays for selected de novo variants.
Fig. 5: Enrichment of variants in RNA-binding-protein category annotations.

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Data availability

Whole-genome sequencing data are deposited in the database of Genotypes and Phenotypes (dbGaP) under accession numbers phs001194.v2.p2 and phs001138.v2.p2.

Code availability

Documentation, links, and availability of source code and select supplementary data are detailed at https://github.com/frichter/wgs_chd_analysis. The DNV identification pipeline is available at https://github.com/ShenLab/igv-classifier and https://github.com/frichter/dnv_pipeline. The HeartENN algorithmic framework is available at https://github.com/FunctionLab/selene/archive/0.4.8.tar.gz. HeartENN model weights and scripts for burden tests are available at https://github.com/frichter/wgs_chd_analysis. All source code is distributed under the Massachusetts Institute of Technology license.

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Acknowledgements

We are enormously grateful to the patients and families who participated in this research. We thank the following for patient recruitment: A. Julian, M. MacNeal, Y. Mendez, T. Mendiz-Ramdeen and C. Mintz (Icahn School of Medicine at Mount Sinai); N. Cross (Yale School of Medicine); J. Ellashek and N. Tran (Children’s Hospital of Los Angeles); B. McDonough, J. Geva and M. Borensztein (Harvard Medical School); K. Flack, L. Panesar and N. Taylor (University College London); E. Taillie (University of Rochester School of Medicine and Dentistry); S. Edman, J. Garbarini, J. Tusi and S. Woyciechowski (Children’s Hospital of Philadelphia); D. Awad, C. Breton, K. Celia, C. Duarte, D. Etwaru, N. Fishman, E. Griffin, M. Kaspakoval, J. Kline, R. Korsin, A. Lanz, E. Marquez, D. Queen, A. Rodriguez, J. Rose, J. K. Sond, D. Warburton, A. Wilpers and R. Yee (Columbia Medical School); D. Gruber (Cohen Children’s Medical Center, Northwell Health). These data were generated by the PCGC, under the auspices of the Bench to Bassinet Program (https://benchtobassinet.com) of the NHLBI. The results analyzed and published here are based in part on data generated by Gabriella Miller Kids First Pediatric Research Program projects phs001138.v1.p2/phs001194.v1.p2, and were accessed from the Kids First Data Resource Portal (https://kidsfirstdrc.org/) and/or dbGaP (www.ncbi.nlm.nih.gov/gap). This manuscript was prepared in collaboration with investigators of the PCGC and has been reviewed and/or approved by the PCGC. PCGC investigators are listed at https://benchtobassinet.com/?page_id=119. This work was supported in part through the computational resources and staff expertise provided by Scientific Computing at the Icahn School of Medicine at Mount Sinai. We are grateful to all of the families at the participating Simons Simplex Collection (SSC) sites, as well as the principal investigators (A. Beaudet, R. Bernier, J. Constantino, E. Cook, E. Fombonne, D. Geschwind, R. Goin-Kochel, E. Hanson, D. Grice, A. Klin, D. Ledbetter, C. Lord, C. Martin, D. Martin, R. Maxim, J. Miles, O. Ousley, K. Pelphrey, B. Peterson, J. Piggot, C. Saulnier, M. State, W. Stone, J. Sutcliffe, C. Walsh, Z. Warren and E. Wijsman). We appreciate the access obtained to phenotypic and/or genetic data on SFARI Base. Approved researchers can obtain the SSC population dataset described in this study (https://www.sfari.org/resource/simons-simplex-collection) by applying at https://base.sfari.org. This work was supported by the Mount Sinai Medical Scientist Training Program (5T32GM007280 to F.R.), National Institute of Dental and Craniofacial Research Interdisciplinary Training in Systems and Developmental Biology and Birth Defects (T32HD075735 to F.R.), Harvard Medical School Epigenetic and Gene Dynamics Award (S.U.M. and C.E.S.), American Heart Association Post-Doctoral Fellowship (S.U.M.), and Howard Hughes Medical Institute (C.E.S.). Research conducted at the E.O. Lawrence Berkeley National Laboratory was supported by National Institutes of Health (NIH) grants (UM1HL098166 and R24HL123879) and performed under Department of Energy Contract DE-AC02-05CH11231, University of California. O.T. is a CIFAR fellow and this work was partially supported by NIH grant R01GM071966. The PCGC program is funded by the NHLBI, NIH, US Department of Health and Human Services through grants UM1HL128711, UM1HL098162, UM1HL098147, UM1HL098123, UM1HL128761 and U01HL131003. The PCGC Kids First study includes data sequenced by the Broad Institute (U24 HD090743-01).

Author information

Author notes

  1. These authors contributed equally: Felix Richter, Sarah U. Morton, Seong Won Kim, Alexander Kitaygorodsky, Lauren K. Wasson, Kathleen M. Chen.

  2. These authors jointly supervised this work: Deepak Srivastava, Martin Tristani-Firouzi, Olga G. Troyanskaya, Diane E. Dickel, Yufeng Shen, Jonathan G. Seidman, Christine E. Seidman, Bruce D. Gelb.

Authors and Affiliations

  1. Graduate School of Biomedical Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA

    Felix Richter & Kathryn B. Manheimer

  2. Department of Pediatrics, Harvard Medical School, Boston, MA, USA

    Sarah U. Morton

  3. Division of Newborn Medicine, Boston Children’s Hospital, Boston, MA, USA

    Sarah U. Morton

  4. Department of Genetics, Harvard Medical School, Boston, MA, USA

    Seong Won Kim, Lauren K. Wasson, Steven R. DePalma, Michael Parfenov, Jason Homsy, Joshua M. Gorham, Jonathan G. Seidman & Christine E. Seidman

  5. Departments of Systems Biology and Biomedical Informatics, Columbia University, New York, NY, USA

    Alexander Kitaygorodsky, Hongjian Qi & Yufeng Shen

  6. Flatiron Institute, Simons Foundation, New York, NY, USA

    Kathleen M. Chen, Jian Zhou & Olga G. Troyanskaya

  7. Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ, USA

    Jian Zhou & Olga G. Troyanskaya

  8. Lyda Hill Department of Bioinformatics, University of Texas Southwestern Medical Center, Dallas, TX, USA

    Jian Zhou

  9. Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA

    Nihir Patel, Eric E. Schadt & Bruce D. Gelb

  10. Center for External Innovation, Takeda Pharmaceuticals USA, Cambridge, MA, USA

    Jason Homsy

  11. Sema4, Stamford, CT, USA

    Kathryn B. Manheimer & Eric E. Schadt

  12. Department of Human Genetics, Utah Center for Genetic Discovery, University of Utah School of Medicine, Salt Lake City, UT, USA

    Matthew Velinder, Andrew Farrell & Gabor Marth

  13. Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY, USA

    Eric E. Schadt

  14. Heart Development and Structural Diseases Branch, Division of Cardiovascular Sciences, NHLBI/NIH, Bethesda, MD, USA

    Jonathan R. Kaltman

  15. Boston Children’s Hospital, Boston, MA, USA

    Jane W. Newburger

  16. Cardiorespiratory Unit, Great Ormond Street Hospital, London, UK

    Alessandro Giardini

  17. Division of Cardiology, Children’s Hospital of Philadelphia, Philadelphia, PA, USA

    Elizabeth Goldmuntz

  18. Department of Pediatrics, The Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA

    Elizabeth Goldmuntz

  19. Departments of Pediatrics and Genetics, Yale University School of Medicine, New Haven, CT, USA

    Martina Brueckner

  20. Children’s Hospital Los Angeles, Los Angeles, CA, USA

    Richard Kim

  21. Department of Pediatrics, University of Rochester, Rochester, NY, USA

    George A. Porter Jr.

  22. Department of Pediatrics, Stanford University, Palo Alto, CA, USA

    Daniel Bernstein

  23. Departments of Pediatrics and Medicine, Columbia University Medical Center, New York, NY, USA

    Wendy K. Chung

  24. Gladstone Institute of Cardiovascular Disease and University of California San Francisco, San Francisco, CA, USA

    Deepak Srivastava

  25. Division of Pediatric Cardiology, University of Utah School of Medicine, Salt Lake City, UT, USA

    Martin Tristani-Firouzi

  26. Department of Computer Science, Princeton University, Princeton, NJ, USA

    Olga G. Troyanskaya

  27. Environmental Genomics and Systems Biology Division, Lawrence Berkeley National Lab, Berkeley, CA, USA

    Diane E. Dickel

  28. Department of Cardiology, Brigham and Women’s Hospital, Boston, MA, USA

    Christine E. Seidman

  29. Mindich Child Health and Development Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA

    Bruce D. Gelb

  30. Department of Pediatrics, Icahn School of Medicine at Mount Sinai, New York, NY, USA

    Bruce D. Gelb

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  1. Felix Richter
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  2. Sarah U. Morton
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Contributions

F.R., S.U.M., S.W.K., A.K., L.K.W., K.M.C., J.R.K., O.G.T., D.E.D., Y.S., J.G.S., C.E.S. and B.D.G. conceived and designed the experiments/analyses. J.R.K., J.W.N., A.G., E.G., M.B., R.K., G.A.P., D.B., W.K.C., D.S., M.T.-F., J.G.S., C.E.S. and B.D.G. contributed to cohort ascertainment, phenotypic characterization and recruitment. F.R., S.U.M., A.K., H.Q., N.P., S.R.D., M.P., J.H., J.M.G., K.B.M., M.V., A.F., G.M., W.K.C., Y.S., J.G.S., C.E.S. and B.D.G. contributed to whole-genome sequencing production, validation and analysis. F.R., S.U.M., A.K., K.M.C., H.Q., E.E.S., O.G.T., Y.S., J.G.S., C.E.S. and B.D.G. contributed to statistical analyses. F.R., K.M.C., J.Z., O.G.T. and B.D.G. developed the HeartENN model. S.U.M., S.W.K., L.K.W., D.E.D., J.G.S. and C.E.S. generated and analyzed fetal heart and iPSC data. F.R., S.U.M., S.W.K., A.K., L.K.W., K.M.C., Y.S., J.G.S., C.E.S. and B.D.G. wrote and reviewed the manuscript. All authors read and approved the manuscript.

Corresponding author

Correspondence to Bruce D. Gelb.

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Extended data

Extended Data Fig. 1 Other pipelines identified 94% of DNVs in control trios.

Overlaps with DNVs identified in 1,470 control trios with two other pipelines9,10. Of note, a third analysis of these trios did not include de novo calls42. For consistency with other pipelines, only SNVs were included and variants in LCRs, blacklists, segmental duplications, and repeats were excluded. Together, 94% of de novo SNVs were called by at least one other pipeline.

Extended Data Fig. 2 Correlation between parental age at proband birth and DNVs/trio.

Multiple linear regression (βpaternal_agex + βmaternal_agex + βintercept + ε) was fitted on 763 CHD and 1,611 unaffected individuals to calculate the associations between paternal and maternal age for SNVs, indels, and combined. Regression coefficients and P-values are shown, uncorrected for multiple hypotheses. Sequencing metric comparisons between the centers, colored by cases (n = 763) and controls (n = 1,611), found moderate bias in DNV quantity, so the background statistical parameter throughout the manuscript is total number of DNVs. Box plots show medians and interquartile ranges.

Extended Data Fig. 3 De novo variant (DNV) CHD-unaffected burden.

The number of DNVs in 184 noncoding annotations (points) genome-wide and within 10 kb of TSSs for 6 gene sets (facets) was counted in CHD (n = 749) and Simons unaffected (n = 1,611) individuals. The P value threshold (1.5 x 10-4, horizontal blue line) is 0.05 divided by the product of the number of effective annotations (n = 47) and number of gene sets (n = 7). The P value (y-axis) was calculated with a two-sided Fisher’s exact test, the odds ratio (x-axis) was DNVsannotation,CHD/DNVstotal,CHD vs. DNVsannotation,unaffected/DNVstotal, unaffected. No annotations surpassed the P value threshold. CHD, congenital heart disease; HHE, high heart expression.

Extended Data Fig. 4 HeartENN performance was comparable to DeepSEA.

HearENN ROC AUC mean = 0.93 and AUPRC mean = 0.34. ROC AUC, receiver operator characteristics area under the curve; AUPRC, area under the precision recall curve.

Extended Data Fig. 5 Determining an absolute functional difference score range.

a, Comparison of HGMD disease mutations (blue, n = 1,564) and polymorphism (gray, n = 642) DeepSEA absolute functional difference scores at varying functional cut-offs illustrates a similar distribution and functionally impactful range ≥0.1 (arrow) for disease mutations. No statistical significance testing was performed. b, The similarity of null distributions for DeepSEA (gray, downsampled to 184 features) and HeartENN (heart) HGMD polymorphism scores suggested that the DeepSEA functional score range was also applicable to HeartENN (gray and red n = 642). Scores of 0 set off to left (as 10-4).

Extended Data Fig. 6 Support for HeartENN ≥ 0.1 functional ranking.

For all DNVs (n = 170,171), overlap between HeartENN ≥0.1 (n = 6,415) and other noncoding scores was assessed with a two-sided Fisher’s exact test (left panel). Case–control burden for these other noncoding scores (right panel) was statistically significant for CADD ≥15 (PBonferroni = 0.019) with a two-sided Fisher’s exact test (cases n = 56,164 and controls n = 114,065). For both panels, unadjusted P-values are tabulated, and red indicates a Benjamini-Hochberg-adjusted P value false discovery rate (FDR) < 0.05.

Extended Data Fig. 7 Relationship between sequence length inserted into the pMRPA1 plasmid and the transcript reads/plasmid copies in MPRAs.

The length of the sequences inserted into the pMPRA1 plasmid (x-axis) ranged from 300 to 1,600 bp. After transfection of four libraries (color coded as per key) into the iPSC–CMs, the resulting ratios of transcript reads (mRNA) per plasmid copies (DNA) are graphed on the y-axis, showing no systematic relationship between insert length and transcriptional level.

Extended Data Fig. 8 DNVs with a trend towards decreased expression by MPRA assay.

Box plots for two DNVs for which two MPRA replicates were significantly different but overall statistical significance across all replicates was not attained. Boxplots show the median fold change (FC), first and third quartiles (lower and upper hinges), and range of values (whiskers and outlying points). Statistical significance was assessed with two-sided t-test Benjamini-Hochberg-adjusted P-values. Each boxplot has at least 3 independent experiments with 4 technical replicates each.

Extended Data Fig. 9 Fraction of DNVs in each of the canonical variant classes.

The fraction was calculated separately within CHD and unaffected subjects for each of the three methods (including overlaps) and the total number of variants in each group (right table).

Extended Data Fig. 10 DNV enrichment in phenotype subgroups.

a, Enrichment of DNVs with predicted functional impacts (score ≥0.1) for HeartENN (left) and DeepSEA (right) within phenotype subgroups. b, Enrichment of de novo SNVs with H3K36me3 marks implicated in RNA-binding protein disruption in different subgroups for the most significant (left) and highest effect size (right) hits. Both a and b were performed with a two-sided Fisher’s exact test (unadjusted P-values and 95% C.I.s shown) comparing the fraction of DNVs in each subgroup (HeartENN ≥ 0.1, DeepSEA ≥ 0.1, etc.) to the same control cohort. For HeartENN, there were n = 4,177 control DNVs with HeartENN ≥ 0.1 and n = 109,888 control DNVs with HeartENN < 0.1. NDD, neurodevelopmental disorder; ECA, extracardiac anomaly.

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Richter, F., Morton, S.U., Kim, S.W. et al. Genomic analyses implicate noncoding de novo variants in congenital heart disease. Nat Genet 52, 769–777 (2020). https://doi.org/10.1038/s41588-020-0652-z

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