Genomic analyses implicate noncoding de novo variants in congenital heart disease

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

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SIMILAR CONTENT BEING VIEWED BY OTHERS FUNCTIONAL DISSECTION OF HUMAN CARDIAC ENHANCERS AND NONCODING DE NOVO VARIANTS IN CONGENITAL HEART DISEASE Article 20 February 2024 COPY NUMBER

VARIATION-ASSOCIATED LNCRNAS MAY CONTRIBUTE TO THE ETIOLOGIES OF CONGENITAL HEART DISEASE Article Open access 17 February 2023 GENOMIC FRONTIERS IN CONGENITAL HEART DISEASE Article 16 July

2021 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

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1189–1204.e6 (2019). CAS  PubMed  PubMed Central  Google Scholar  Download references 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 * These authors

contributed equally: Felix Richter, Sarah U. Morton, Seong Won Kim, Alexander Kitaygorodsky, Lauren K. Wasson, Kathleen M. Chen. * 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 * Graduate School

of Biomedical Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA Felix Richter & Kathryn B. Manheimer * Department of Pediatrics, Harvard Medical School, Boston, MA,

USA Sarah U. Morton * Division of Newborn Medicine, Boston Children’s Hospital, Boston, MA, USA Sarah U. Morton * 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 * Departments of Systems Biology and Biomedical

Informatics, Columbia University, New York, NY, USA Alexander Kitaygorodsky, Hongjian Qi & Yufeng Shen * Flatiron Institute, Simons Foundation, New York, NY, USA Kathleen M. Chen, Jian

Zhou & Olga G. Troyanskaya * Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ, USA Jian Zhou & Olga G. Troyanskaya * Lyda Hill Department of

Bioinformatics, University of Texas Southwestern Medical Center, Dallas, TX, USA Jian Zhou * 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 * Center for External Innovation, Takeda Pharmaceuticals USA, Cambridge, MA, USA Jason Homsy * Sema4, Stamford, CT, USA Kathryn B.

Manheimer & Eric E. Schadt * 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 * Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY, USA Eric E. Schadt * Heart Development and Structural

Diseases Branch, Division of Cardiovascular Sciences, NHLBI/NIH, Bethesda, MD, USA Jonathan R. Kaltman * Boston Children’s Hospital, Boston, MA, USA Jane W. Newburger * Cardiorespiratory

Unit, Great Ormond Street Hospital, London, UK Alessandro Giardini * Division of Cardiology, Children’s Hospital of Philadelphia, Philadelphia, PA, USA Elizabeth Goldmuntz * Department of

Pediatrics, The Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA Elizabeth Goldmuntz * Departments of Pediatrics and Genetics, Yale University School of

Medicine, New Haven, CT, USA Martina Brueckner * Children’s Hospital Los Angeles, Los Angeles, CA, USA Richard Kim * Department of Pediatrics, University of Rochester, Rochester, NY, USA

George A. Porter Jr. * Department of Pediatrics, Stanford University, Palo Alto, CA, USA Daniel Bernstein * Departments of Pediatrics and Medicine, Columbia University Medical Center, New

York, NY, USA Wendy K. Chung * Gladstone Institute of Cardiovascular Disease and University of California San Francisco, San Francisco, CA, USA Deepak Srivastava * Division of Pediatric

Cardiology, University of Utah School of Medicine, Salt Lake City, UT, USA Martin Tristani-Firouzi * Department of Computer Science, Princeton University, Princeton, NJ, USA Olga G.

Troyanskaya * Environmental Genomics and Systems Biology Division, Lawrence Berkeley National Lab, Berkeley, CA, USA Diane E. Dickel * Department of Cardiology, Brigham and Women’s Hospital,

Boston, MA, USA Christine E. Seidman * Mindich Child Health and Development Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA Bruce D. Gelb * Department of Pediatrics,

Icahn School of Medicine at Mount Sinai, New York, NY, USA Bruce D. Gelb Authors * Felix Richter View author publications You can also search for this author inPubMed Google Scholar * Sarah

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also search for this author inPubMed Google Scholar * Bruce D. Gelb View author publications You can also search for this author inPubMed Google Scholar 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. ETHICS DECLARATIONS COMPETING INTERESTS The authors declare no competing interests. ADDITIONAL INFORMATION PUBLISHER’S

NOTE Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. 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 (_β_paternal_age_x_ + _β_maternal_age_x_ +

_β_intercept + _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 (_P__Bonferroni_ = 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. SUPPLEMENTARY INFORMATION SUPPLEMENTARY INFORMATION Supplementary Note and Fig. 1 REPORTING SUMMARY

SUPPLEMENTARY TABLE Supplementary Tables 1–16 RIGHTS AND PERMISSIONS Reprints and permissions ABOUT THIS ARTICLE CITE THIS ARTICLE 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 Download citation * Received: 09 March

2019 * Accepted: 22 May 2020 * Published: 29 June 2020 * Issue Date: August 2020 * DOI: https://doi.org/10.1038/s41588-020-0652-z SHARE THIS ARTICLE Anyone you share the following link with

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