Genomic analyses implicate noncoding de novo variants in congenital heart disease

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

doi:10.1038/s41588-020-0652-z

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Genomic analyses implicate noncoding de novo variants in congenital heart disease

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Author(s): Felix Richter ¹ ^, ³¹ , Sarah U. Morton ² ^, ³ ^, ³¹ , Seong Won Kim ⁴ ^, ³¹ , Alexander Kitaygorodsky ⁵ ^, ³¹ , Lauren K. Wasson ⁴ ^, ³¹ , Kathleen M. Chen ⁶ ^, ³¹ , Jian Zhou ⁶ ^, ⁷ ^, ⁸ , Hongjian Qi ⁵ , Nihir Patel ⁹ , Steven R. DePalma ⁴ , Michael Parfenov ⁴ , Jason Homsy ⁴ ^, ¹⁰ , Joshua M. Gorham ⁴ , Kathryn B. Manheimer ¹¹ , Matthew Velinder ¹² , Andrew Farrell ¹² , Gabor Marth ¹² , Eric E. Schadt ⁹ ^, ¹¹ ^, ¹³ , Jonathan R. Kaltman ¹⁴ , Jane W. Newburger ¹⁵ , Alessandro Giardini ¹⁶ , Elizabeth Goldmuntz ¹⁷ ^, ¹⁸ , Martina Brueckner ¹⁹ , Richard Kim ²⁰ , George A. Porter Jr. ²¹ , Daniel Bernstein ²² , Wendy K. Chung ²³ , Deepak Srivastava ²⁴ ^, ³² , Martin Tristani-Firouzi ²⁵ ^, ³² , Olga G. Troyanskaya ⁶ ^, ⁷ ^, ²⁶ ^, ³² , Diane E. Dickel ²⁷ ^, ³² , Yufeng Shen ⁵ ^, ³² , Jonathan G. Seidman ⁴ ^, ³² , Christine E. Seidman ⁴ ^, ²⁸ ^, ³² , Bruce D. Gelb ⁹ ^, ²⁹ ^, ³⁰ ^, ³² ^, ^*

Publication date (Electronic): 29 June 2020

Journal: Nature genetics

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Abstract

A genetic etiology is identified for one third of congenital heart disease (CHD) patients, including 8% 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 1,611 unaffected trios. Neural network prediction of noncoding DNV transcriptional impact identified a burden of DNVs in CHD ( n = 2,238 DNVs) compared to controls ( n = 4,177; P = 8.7 × 10 ⁻⁴). Independent analyses of enhancers showed excess DNVs in associated genes (27 genes vs. 3.7 expected, P = 1 × 10 ⁻⁵). We observed significant overlap between these transcription-based approaches (OR = 2.5, 95% CI 1.1–5.0, P = 5.4 × 10 ⁻³). CHD DNVs altered transcription levels in five of 31 enhancers assayed. Finally, we observed 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 observed for damaging coding DNVs.

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Fast and accurate short read alignment with Burrows–Wheeler transform

Heng Li, Richard Durbin (2009)

Motivation: The enormous amount of short reads generated by the new DNA sequencing technologies call for the development of fast and accurate read alignment programs. A first generation of hash table-based methods has been developed, including MAQ, which is accurate, feature rich and fast enough to align short reads from a single individual. However, MAQ does not support gapped alignment for single-end reads, which makes it unsuitable for alignment of longer reads where indels may occur frequently. The speed of MAQ is also a concern when the alignment is scaled up to the resequencing of hundreds of individuals. Results: We implemented Burrows-Wheeler Alignment tool (BWA), a new read alignment package that is based on backward search with Burrows–Wheeler Transform (BWT), to efficiently align short sequencing reads against a large reference sequence such as the human genome, allowing mismatches and gaps. BWA supports both base space reads, e.g. from Illumina sequencing machines, and color space reads from AB SOLiD machines. Evaluations on both simulated and real data suggest that BWA is ∼10–20× faster than MAQ, while achieving similar accuracy. In addition, BWA outputs alignment in the new standard SAM (Sequence Alignment/Map) format. Variant calling and other downstream analyses after the alignment can be achieved with the open source SAMtools software package. Availability: http://maq.sourceforge.net Contact: rd@sanger.ac.uk

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The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data.

Aaron McKenna, Matthew Hanna, Eric R. Banks … (2010)

Next-generation DNA sequencing (NGS) projects, such as the 1000 Genomes Project, are already revolutionizing our understanding of genetic variation among individuals. However, the massive data sets generated by NGS--the 1000 Genome pilot alone includes nearly five terabases--make writing feature-rich, efficient, and robust analysis tools difficult for even computationally sophisticated individuals. Indeed, many professionals are limited in the scope and the ease with which they can answer scientific questions by the complexity of accessing and manipulating the data produced by these machines. Here, we discuss our Genome Analysis Toolkit (GATK), a structured programming framework designed to ease the development of efficient and robust analysis tools for next-generation DNA sequencers using the functional programming philosophy of MapReduce. The GATK provides a small but rich set of data access patterns that encompass the majority of analysis tool needs. Separating specific analysis calculations from common data management infrastructure enables us to optimize the GATK framework for correctness, stability, and CPU and memory efficiency and to enable distributed and shared memory parallelization. We highlight the capabilities of the GATK by describing the implementation and application of robust, scale-tolerant tools like coverage calculators and single nucleotide polymorphism (SNP) calling. We conclude that the GATK programming framework enables developers and analysts to quickly and easily write efficient and robust NGS tools, many of which have already been incorporated into large-scale sequencing projects like the 1000 Genomes Project and The Cancer Genome Atlas.

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Simple combinations of lineage-determining transcription factors prime cis-regulatory elements required for macrophage and B cell identities.

Sven Heinz, Christopher Benner, Nathanael Spann … (2010)

Genome-scale studies have revealed extensive, cell type-specific colocalization of transcription factors, but the mechanisms underlying this phenomenon remain poorly understood. Here, we demonstrate in macrophages and B cells that collaborative interactions of the common factor PU.1 with small sets of macrophage- or B cell lineage-determining transcription factors establish cell-specific binding sites that are associated with the majority of promoter-distal H3K4me1-marked genomic regions. PU.1 binding initiates nucleosome remodeling, followed by H3K4 monomethylation at large numbers of genomic regions associated with both broadly and specifically expressed genes. These locations serve as beacons for additional factors, exemplified by liver X receptors, which drive both cell-specific gene expression and signal-dependent responses. Together with analyses of transcription factor binding and H3K4me1 patterns in other cell types, these studies suggest that simple combinations of lineage-determining transcription factors can specify the genomic sites ultimately responsible for both cell identity and cell type-specific responses to diverse signaling inputs. Copyright 2010 Elsevier Inc. All rights reserved.

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Author and article information

Journal

Journal ID (nlm-journal-id): 9216904

Journal ID (pubmed-jr-id): 2419

Journal ID (nlm-ta): Nat Genet

Journal ID (iso-abbrev): Nat. Genet.

Title: Nature genetics

ISSN (Print): 1061-4036

ISSN (Electronic): 1546-1718

Publication date Nihms-submitted: 27 May 2020

Publication date (Electronic): 29 June 2020

Publication date (Print): August 2020

Publication date PMC-release: 29 December 2020

Volume: 52

Issue: 8

Pages: 769-777

Affiliations

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

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

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

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

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

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

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

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

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

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

[11 ]Sema4, a Mount Sinai venture, Stamford, CT, USA.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[31 ]These authors contributed equally to this work.

[32 ]These authors jointly directed this project.

Author notes

AUTHOR 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.

[* ] bruce.gelb@ 123456mssm.edu

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Manuscript ID: NIHMS1597372

DOI: 10.1038/s41588-020-0652-z

PMC ID: 7415662

PubMed ID: 32601476

SO-VID: c5370eed-c37c-473d-b6f1-f0d02cab6a0e

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Genomic analyses implicate noncoding de novo variants in congenital heart disease

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Arabidopsis genomics

Most cited references 82

Fast and accurate short read alignment with Burrows–Wheeler transform

The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data.

Simple combinations of lineage-determining transcription factors prime cis-regulatory elements required for macrophage and B cell identities.

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