Springer Nature is making SARS-CoV-2 and COVID-19 research free. View research | View latest news | Sign up for updates

Robust identification of mosaic variants in congenital heart disease


Mosaicism due to somatic mutations can cause multiple diseases including cancer, developmental and overgrowth syndromes, neurodevelopmental disorders, autoinflammatory diseases, and atrial fibrillation. With the increased use of next generation sequencing technology, multiple tools have been developed to identify low-frequency variants, specifically from matched tumor-normal tissues in cancer studies. To investigate whether mosaic variants are implicated in congenital heart disease (CHD), we developed a pipeline using the cancer somatic variant caller MuTect to identify mosaic variants in whole-exome sequencing (WES) data from a cohort of parent/affected child trios (n = 715) and a cohort of healthy individuals (n = 416). This is a novel application of the somatic variant caller designed for cancer to WES trio data. We identified two cases with mosaic KMT2D mutations that are likely pathogenic for CHD, but conclude that, overall, mosaicism detectable in peripheral blood or saliva does not account for a significant portion of CHD etiology.

This is a preview of subscription content, log in to check access.

Fig. 1


  1. Alioto T, Buchhalter I, Derdak S et al (2015) A comprehensive assessment of somatic mutation detection in cancer using whole-genome sequencing. Nat Commun. https://doi.org/10.1038/ncomms10001

  2. Aretz S, Stienen D, Friedrichs N et al (2007) Somatic APC mosaicism: a frequent cause of familial adenomatous polyposis (FAP). Hum Mutat 28:985–992. https://doi.org/10.1002/humu.20549

  3. Banka S, Howard E, Bunstone S et al (2013) MLL2 mosaic mutations and intragenic deletion-duplications in patients with Kabuki syndrome. Clin Genet 83:467–471. https://doi.org/10.1111/j.1399-0004.2012.01955.x

  4. Behjati S, Maschietto M, Williams RD et al (2014) A pathogenic mosaic TP53 mutation in two germ layers detected by next generation sequencing. PLoS ONE 9:e96531. https://doi.org/10.1371/journal.pone.0096531

  5. Biesecker LG, Spinner NB (2013) A genomic view of mosaicism and human disease. Nat Rev Genet 14:307–320. https://doi.org/10.1038/nrg3424

  6. Chen Z, Moran K, Richards-Yutz J et al (2014) Enhanced sensitivity for detection of low-level germline mosaic RB1 mutations in sporadic retinoblastoma cases using deep semiconductor sequencing. Hum Mutat 35:384–391. https://doi.org/10.1002/humu.22488

  7. Chen L, Liu P, Evans TC, Ettwiller LM (2017) DNA damage is a pervasive cause of sequencing errors, directly confounding variant identification. Science 355:752–756. https://doi.org/10.1101/070334

  8. Cibulskis K, Lawrence M, Carter S et al (2013) Sensitive deduction of somatic point mutations in impure and heterogeneous cancer samples. Nat Biotechnol 31:213-219. https://doi.org/10.1038/nbt.2514

  9. Costello M, Pugh TJ, Fennell TJ et al (2013) Discovery and characterization of artifactual mutations in deep coverage targeted capture sequencing data due to oxidative DNA damage during sample preparation. Nucleic Acids Res 41:1–12. https://doi.org/10.1093/nar/gks1443

  10. DePristo MA, Banks E, Poplin R et al (2011) A framework for variation discovery and genotyping using next-generation DNA sequencing data. Nat Genet 43:491–498. https://doi.org/10.1038/ng.806

  11. Endler G, Greinix H, Winkler K et al (1999) Genetic fingerprinting in mouthwashes of patients after allogeneic bone marrow transplantation. Bone Marrow Transplant 24:95–98. https://doi.org/10.1038/sj.bmt.1701815

  12. Erickson RP (2010) Somatic gene mutation and human disease other than cancer: an update. Mutat Res 705:96–106. https://doi.org/10.1016/j.mrrev.2010.04.002

  13. Frank SA (2014) Somatic mosaicism and disease. Curr Biol 24:R577–R581. https://doi.org/10.1016/j.cub.2014.05.021

  14. Freed D, Pevsner J (2016) The contribution of mosaic variants to autism spectrum disorder. PLoS Genet 12:1–20. https://doi.org/10.1371/journal.pgen.1006245

  15. Glessner J, Bick AG, Ito K et al (2014) Increased frequency of de novo copy number variations in congenital heart disease by integrative analysis of SNP array and exome sequence data. Circ Res. https://doi.org/10.1161/CIRCRESAHA.115.304458

  16. Homsy J, Zaidi S, Shen Y et al (2015) De novo mutations in congenital heart disease with neurodevelopmental and other congenital anomalies. Science 350:1262–1266

  17. Iossifov I, O’roak BJ BJ, Sanders SJ et al (2014) The contribution of de novo coding mutations to autism spectrum disorder. Nature 13:216–221. https://doi.org/10.15154/1149697

  18. Johnston JJ, Finn EM et al (2011) A mosaic activating mutation in AKT1 associated with the proteus syndrome. N Engl J Med 365(7):611–619. https://doi.org/10.1056/NEJMoa1104017

  19. Ju YS, Martincorena I, Gerstung M et al (2017a) Somatic mutations reveal asymmetric cellular dynamics in the early human embryo. Nature 543:714–718. https://doi.org/10.1038/nature21703

  20. Ju YS, Martincorena I, Gerstung M et al (2017b) Somatic mutations reveal asymmetric cellular dynamics in the early human embryo. Nature 543:714–718. https://doi.org/10.1038/nature21703

  21. Kircher M, Witten DM, Jain P et al (2014) A general framework for estimating the relative pathogenicity of human genetic variants. Nat Genet 46:310–315. https://doi.org/10.1038/ng.2892

  22. Koboldt DC, Zhang Q, Larson DE et al (2012) VarScan 2: somatic mutation and copy number alteration discovery in cancer by exome sequencing. Genome Res 22:568–576. https://doi.org/10.1101/gr.129684.111

  23. Kurosaka S, Kashina A (2009) Cell biology of embryonic migration. Birth Defects Res C Embryo Today. https://doi.org/10.1002/bdrc.20125.Cell

  24. Larson DE, Harris CC, Chen K et al (2012) SomaticSniper: identification of somatic point mutations in whole genome sequencing data. Bioinformatics 28:311–317. https://doi.org/10.1093/bioinformatics/btr665

  25. Lim ET, Uddin M, De Rubeis S et al (2017) Rates, distribution and implications of postzygotic mosaic mutations in autism spectrum disorder. Nat Nano Sci. https://doi.org/10.1038/nn.4598

  26. Liu S, Hong X, Shen C et al (2015) Kabuki syndrome: a Chinese case series and systematic review of the spectrum of mutations. BMC Med Genet 16:26. https://doi.org/10.1186/s12881-015-0171-4

  27. Mazaika E, Homsy J (2014) Digital droplet PCR: CNV analysis and other applications. Curr Protoc Hum Genet 82:7.24.1–7.24.13. https://doi.org/10.1002/0471142905.hg0724s82

  28. McKenna A, Hanna M, Banks E et al (2010) The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data. Genome Res 20:1297–1303. https://doi.org/10.1101/gr.107524.110

  29. Mermel CH, Schumacher SE, Hill B et al (2011) GISTIC2.0 facilitates sensitive and confident localization of the targets of focal somatic copy-number alteration in human cancers. Genome Biol 12:R41. https://doi.org/10.1186/gb-2011-12-4-r41

  30. Notini AJ, Craig JM, White SJ (2009) Copy number variation and mosaicism. Cytogenet Genome Res 123:270–277. https://doi.org/10.1159/000184717

  31. O’Roak BJ, Deriziotis P, Lee C et al (2011) Exome sequencing in sporadic autism spectrum disorders identifies severe de novo mutations. Nat Genet 43:585–589. https://doi.org/10.1038/ng.835

  32. Pansuriya TC, van Eijk R, D’Adamo P et al (2011) Somatic mosaic IDH1 and IDH2 mutations are associated with enchondroma and spindle cell hemangioma in Ollier disease and Maffucci syndrome. Nat Genet 43:1256–1261. https://doi.org/10.1038/ng.1004

  33. Piotrowski A, Bruder CEG, Andersson R et al (2008) Somatic mosaicism for copy number variation in differentiated human tissues. Hum Mutat 29:1118–1124. https://doi.org/10.1002/humu.20815

  34. Poduri A, Evrony GD, Cai X, Walsh CA (2013) Somatic Mutation, Genomic Variation, and Neurological Disease. Science 341:43–51. https://doi.org/10.1038/ng.2331

  35. Priest JR, Gawad C, Kahlig KM et al (2016) Early somatic mosaicism is a rare cause of long-QT syndrome. Proc Natl Acad Sci 113:11555–11560. https://doi.org/10.1073/pnas.1607187113

  36. Roth A, Ding J, Morin R et al (2012) JointSNVMix: a probabilistic model for accurate detection of somatic mutations in normal/tumour paired next-generation sequencing data. Bioinformatics 28:907–913. https://doi.org/10.1093/bioinformatics/bts053

  37. Rushlow D, Piovesan B, Zhang K et al (2009) Detection of mosaic RB1 mutations in families with retinoblastoma. Hum Mutat 30:842–851. https://doi.org/10.1002/humu.20940

  38. Saito M, Nishikomori R, Kambe N et al (2008) Disease-associated CIAS1 mutations induce monocyte death, revealing low-level mosaicism in mutation-negative cryopyrin-associated periodic syndrome patients. Blood 111:2132–2141. https://doi.org/10.1182/blood-2007-06-094201

  39. Sanders SJ, Murtha MT, Gupta AR et al (2012) De novo mutations revealed by whole-exome sequencing are strongly associated with autism. Nature 485:237–241. https://doi.org/10.1038/nature10945

  40. Saunders CT, Wong WSW, Swamy S et al (2012) Strelka: accurate somatic small-variant calling from sequenced tumor-normal sample pairs. Bioinformatics 28:1811–1817. https://doi.org/10.1093/bioinformatics/bts271

  41. Taylor TH, Gitlin SA, Patrick JL et al (2014) The origin, mechanisms, incidence and clinical consequences of chromosomal mosaicism in humans. Hum Reprod Update 20:571–581. https://doi.org/10.1093/humupd/dmu016

  42. Van Der Auwera GA, Carneiro MO, Hartl C, et al (2014) From FastQ data to high confidence variant calls: the Genome analysis toolkit best practices pipeline. Curr Protoc Bioinformatics 43:11.10.1–11.10.33. https://doi.org/10.1002/0471250953.bi1110s43

  43. Wang Q, Jia P, Li F et al (2013) Detecting somatic point mutations in cancer genome sequencing data: a comparison of mutation callers. Genome Med 5:91. https://doi.org/10.1186/gm495

  44. Winberg J, Berggren H, Malm T et al (2015) No evidence for mosaic pathogenic copy number variations in cardiac tissue from patients with congenital heart malformations. Eur J Med Genet 58:129–133. https://doi.org/10.1016/j.ejmg.2015.01.003

  45. Yamada Y, Nomura N, Yamada K et al (2014) The spectrum of Z E B2 mutations causing the Mowat–Wilson syndrome in Japanese populations. Am J Med Genet Part A. https://doi.org/10.1002/ajmg.a.36551

  46. Zhang X, Hill RS et al (2014) Somatic mutations in cerebral cortical malformations. N Engl Med 371(8):733–743. https://doi.org/10.1056/nejmoa1314432

Download references


The authors are grateful to the patients and families who participated in this research and team members who supported subject recruitment and sequencing D.Awad, C. Breton, K. Celia, C. Duarte, D. Etwaru, N.Rishman, M. Daspakova, 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]; B. McDonough, A. Monafo, J. Stryker [Harvard Medical School]; N. Cross [Yale School of Medicince]; S. M. Edman, J.L. Garbarini, J.E. Tusi, S.H. Woyciechowski (Children’s Hospital of Philadelphia); J. Ellashek and N. Tran (Children’s Hospital of Los Angeles); K. Flack, L. Panesar, N. Taylor (University College London); D. Gruber and N. Stellato (Steve and Alexandra Cohen Children’s Medical Center of New York); D. Guevara, A. Julian, M. MacNeal, C. Mintz (Icahn School of Medicine at Mount Sinai); and E. Taillie (University of Rochester School of Medicine and Dentistry]). We also thank the Simons Foundation for Autism Research for the contribution of control exome trios.


Funding was provided by the National Heart, Blood and Lung Institute from the following grants: U01-HL098147, U01-HL098123, U01-HL098162, U01-HL098153, U01-HL098163, and U01-HL098188.

Author information

Correspondence to Bruce D. Gelb.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the Institutional Review Boards listed and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. Institutional IRBs: Boston Children’s Hospital, Brigham and Women’s Hospital, Great Ormond Street Hospital, Children’s Hospital of Los Angeles, Children’s Hospital of Philadelphia, Columbia University Medical Center, Icahn School of Medicine at Mount Sinai, Rochester School of Medicine and Dentistry, Steven and Alexandra Cohen Children’s Medical Center of New York, and Yale School of Medicine.

Informed consent

Informed consent was obtained from all individual participants or their parent/guardian included in this study.

Data availability

The PCGC datasets analyzed during the current study are available from dbGAP (https://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?study_id=phs000571.v1.p1). Approved researchers can obtain the SSC population dataset used as controls in this study by applying at SFARI Base (https://base.sfari.org/).

Electronic supplementary material

Below is the link to the electronic supplementary material.

Online Resource Figure 1: Depth of coverage (DOC) distribution for cases (red), original control bams (blue) and down sampled (ds) control bams (green). The mean DOC is 60x for both cases and down sampled controls allowing us to compare the number of mosaic variants in each cohort (PDF 433 kb)

Online ResourceFigure 2: Distribution of the fraction of WES capture intervals with depth > = 15 (DIR 15 fraction) for WES cases (red), original control bams (blue) and down sampled (ds) control bams (green) (PDF 381 kb)

Online Resource Figure 3: Schematic illustrating how MuTect somatic variant caller was used with WES trio data to identify de novo variants in the child. For each trio we ran MuTect twice, designating the child as ‘tumor’ and each parent as ‘normal.’ The intersection of variants is the set of de novo variants in the child that are then filtered for mosaic variants (PDF 222 kb)

Online Resource Figure 4: A) Comparison of alternate allele depth in child versus total depth in child for variants that confirmed as mosaic and homozygous reference based on ddPCR results. Red squares represent confirmed mosaic variants. Blue diamonds represent homozygous loci. Dashed lines represent new filtering parameters to increase the positive predictive value. B) Comparison of minimum parental read depth compared to total depth in child (PDF 418 kb)

Online Resource Figure 5: Counts of variant types of CHD cases (blue) and controls (red) (PDF 276 kb)

Online Resource Table 1: Phenotypic data for samples in PCGC cases and SSC controls (XLSX 106 kb)

Online Resource Table 2: Filtering parameters for mosaic variants (PDF 60 kb)

Online Resource Table 3: Confirmation results from digital droplet PCR for mosaic variants (PDF 48 kb)

Online Resource Table 4: Mosaic variants identified in cases and controls with CADD annotations (XLSX 74 kb)

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Manheimer, K.B., Richter, F., Edelmann, L.J. et al. Robust identification of mosaic variants in congenital heart disease. Hum Genet 137, 183–193 (2018). https://doi.org/10.1007/s00439-018-1871-6

Download citation