Genomic analyses implicate noncoding de novo variants in congenital heart disease

Felix Richter; Sarah U. Morton; Seong Won Kim; Alexander Kitaygorodsky; Lauren K. Wasson; Kathleen M. Chen; Jian Zhou; Hongjian Qi; Nihir Patel; Steven R. DePalma; Michael Parfenov; Jason Homsy; Joshua M. Gorham; Kathryn B. Manheimer; Matthew Velinder; Andrew Farrell; Gabor Marth; Eric E. Schadt; Jonathan R. Kaltman; Jane W. Newburger; Alessandro Giardini; Elizabeth Goldmuntz; Martina Brueckner; Richard Kim; George A. Porter; Daniel Bernstein; Wendy K. Chung; Deepak Srivastava; Martin Tristani-Firouzi; Olga G. Troyanskaya; Diane E. Dickel; Yufeng Shen; Jonathan G. Seidman; Christine E. Seidman; Bruce D. Gelb

doi:10.1038/s41588-020-0652-z

Genomic analyses implicate noncoding de novo variants in congenital heart disease

Felix Richter, Sarah U. Morton, Seong Won Kim, Alexander Kitaygorodsky, Lauren K. Wasson, Kathleen M. Chen, Jian Zhou, Hongjian Qi, Nihir Patel, Steven R. DePalma, Michael Parfenov, Jason Homsy, Joshua M. Gorham, Kathryn B. Manheimer, Matthew Velinder, Andrew Farrell, Gabor Marth, Eric E. Schadt, Jonathan R. Kaltman, Jane W. NewburgerAlessandro Giardini, Elizabeth Goldmuntz, Martina Brueckner, Richard Kim, George A. Porter, Daniel Bernstein, Wendy K. Chung, Deepak Srivastava, Martin Tristani-Firouzi, Olga G. Troyanskaya, Diane E. Dickel, Yufeng Shen, Jonathan G. Seidman, Christine E. Seidman, Bruce D. Gelb

Research output: Contribution to journal › Article › peer-review

94 Scopus citations

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.

Original language	English
Pages (from-to)	769-777
Number of pages	9
Journal	Nature Genetics
Volume	52
Issue number	8
DOIs	https://doi.org/10.1038/s41588-020-0652-z
State	Published - Aug 1 2020
Externally published	Yes

Funding

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).

Funders	Funder number
Harvard Medical School Epigenetic and Gene Dynamics Award
Mount Sinai Medical Scientist Training Program
National Institute of Dental and Craniofacial Research Interdisciplinary Training in Systems and Developmental Biology and Birth Defects
National Institutes of Health
Howard Hughes Medical Institute
U.S. Department of Energy	DE-AC02-05CH11231
U.S. Department of Energy
U.S. Department of Health and Human Services
National Heart, Lung, and Blood Institute	U01HL131003, UM1HL098147, UM1HL098123, R24HL123879, U01HL098153, UM1HL098162, UM1HL128711, UM1HL098166, U01-HL098163, UM1HL128761
National Heart, Lung, and Blood Institute
National Institute of General Medical Sciences	5T32GM007280
National Institute of General Medical Sciences
National Institute of Dental and Craniofacial Research	T32HD075735
National Institute of Dental and Craniofacial Research
American Heart Association
University of California	R01GM071966
University of California

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10.1038/s41588-020-0652-z

Cite this

Richter, F., Morton, S. U., Kim, S. W., Kitaygorodsky, A., Wasson, L. K., Chen, K. M., Zhou, J., Qi, H., Patel, N., DePalma, S. R., Parfenov, M., Homsy, J., Gorham, J. M., Manheimer, K. B., Velinder, M., Farrell, A., Marth, G., Schadt, E. E., Kaltman, J. R., ... Gelb, B. D. (2020). Genomic analyses implicate noncoding de novo variants in congenital heart disease. Nature Genetics, 52(8), 769-777. https://doi.org/10.1038/s41588-020-0652-z

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title = "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 observed for damaging coding DNVs.",

author = "Felix Richter and Morton, {Sarah U.} and Kim, {Seong Won} and Alexander Kitaygorodsky and Wasson, {Lauren K.} and Chen, {Kathleen M.} and Jian Zhou and Hongjian Qi and Nihir Patel and DePalma, {Steven R.} and Michael Parfenov and Jason Homsy and Gorham, {Joshua M.} and Manheimer, {Kathryn B.} and Matthew Velinder and Andrew Farrell and Gabor Marth and Schadt, {Eric E.} and Kaltman, {Jonathan R.} and Newburger, {Jane W.} and Alessandro Giardini and Elizabeth Goldmuntz and Martina Brueckner and Richard Kim and Porter, {George A.} and Daniel Bernstein and Chung, {Wendy K.} and Deepak Srivastava and Martin Tristani-Firouzi and Troyanskaya, {Olga G.} and Dickel, {Diane E.} and Yufeng Shen and Seidman, {Jonathan G.} and Seidman, {Christine E.} and Gelb, {Bruce D.}",

note = "Publisher Copyright: {\textcopyright} 2020, The Author(s), under exclusive licence to Springer Nature America, Inc.",

year = "2020",

month = aug,

day = "1",

doi = "10.1038/s41588-020-0652-z",

language = "English",

volume = "52",

pages = "769--777",

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Richter, F, Morton, SU, Kim, SW, Kitaygorodsky, A, Wasson, LK, Chen, KM, Zhou, J, Qi, H, Patel, N, DePalma, SR, Parfenov, M, Homsy, J, Gorham, JM, Manheimer, KB, Velinder, M, Farrell, A, Marth, G, Schadt, EE, Kaltman, JR, Newburger, JW, Giardini, A, Goldmuntz, E, Brueckner, M, Kim, R, Porter, GA, Bernstein, D, Chung, WK, Srivastava, D, Tristani-Firouzi, M, Troyanskaya, OG, Dickel, DE, Shen, Y, Seidman, JG, Seidman, CE & Gelb, BD 2020, 'Genomic analyses implicate noncoding de novo variants in congenital heart disease', Nature Genetics, vol. 52, no. 8, pp. 769-777. https://doi.org/10.1038/s41588-020-0652-z

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T1 - Genomic analyses implicate noncoding de novo variants in congenital heart disease

AU - Richter, Felix

AU - Morton, Sarah U.

AU - Kim, Seong Won

AU - Kitaygorodsky, Alexander

AU - Wasson, Lauren K.

AU - Chen, Kathleen M.

AU - Zhou, Jian

AU - Qi, Hongjian

AU - Patel, Nihir

AU - DePalma, Steven R.

AU - Parfenov, Michael

AU - Homsy, Jason

AU - Gorham, Joshua M.

AU - Manheimer, Kathryn B.

AU - Velinder, Matthew

AU - Farrell, Andrew

AU - Marth, Gabor

AU - Schadt, Eric E.

AU - Kaltman, Jonathan R.

AU - Newburger, Jane W.

AU - Giardini, Alessandro

AU - Goldmuntz, Elizabeth

AU - Brueckner, Martina

AU - Kim, Richard

AU - Porter, George A.

AU - Bernstein, Daniel

AU - Chung, Wendy K.

AU - Srivastava, Deepak

AU - Tristani-Firouzi, Martin

AU - Troyanskaya, Olga G.

AU - Dickel, Diane E.

AU - Shen, Yufeng

AU - Seidman, Jonathan G.

AU - Seidman, Christine E.

AU - Gelb, Bruce D.

PY - 2020/8/1

Y1 - 2020/8/1

N2 - 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.

AB - 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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IS - 8

ER -

Genomic analyses implicate noncoding de novo variants in congenital heart disease

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