A tailored approach to fusion transcript identification increases diagnosis of rare inherited disease

Autoři: Gavin R. Oliver aff001;  Xiaojia Tang aff001;  Laura E. Schultz-Rogers aff001;  Noemi Vidal-Folch aff003;  W. Garrett Jenkinson aff001;  Tanya L. Schwab aff004;  Krutika Gaonkar aff001;  Margot A. Cousin aff001;  Asha Nair aff001;  Shubham Basu aff001;  Pritha Chanana aff001;  Devin Oglesbee aff003;  Eric W. Klee aff001
Působiště autorů: Department of Health Sciences Research, Mayo Clinic, Rochester, Minnesota, United States of America aff001;  Center for Individualized Medicine, Mayo Clinic, Rochester, Minnesota, United States of America aff002;  Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota, United States of America aff003;  Department of Biochemistry and Molecular Biology, Mayo Clinic, Rochester, Minnesota, United States of America aff004;  Department of Medical Genetics, Mayo Clinic, Rochester, Minnesota, United States of America aff005;  Department of Clinical Genomics, Mayo Clinic, Rochester, Minnesota, United States of America aff006
Vyšlo v časopise: PLoS ONE 14(10)
Kategorie: Research Article
doi: https://doi.org/10.1371/journal.pone.0223337



RNA sequencing has been proposed as a means of increasing diagnostic rates in studies of undiagnosed rare inherited disease. Recent studies have reported diagnostic improvements in the range of 7.5–35% by profiling splicing, gene expression quantification and allele specific expression. To-date however, no study has systematically assessed the presence of gene-fusion transcripts in cases of germline disease. Fusion transcripts are routinely identified in cancer studies and are increasingly recognized as having diagnostic, prognostic or therapeutic relevance. Isolated reports exist of fusion transcripts being detected in cases of developmental and neurological phenotypes, and thus, systematic application of fusion detection to germline conditions may further increase diagnostic rates. However, current fusion detection methods are unsuited to the investigation of germline disease due to performance biases arising from their development using tumor, cell-line or in-silico data.


We describe a tailored approach to fusion candidate identification and prioritization in a cohort of 47 undiagnosed, suspected inherited disease patients. We modify an existing fusion transcript detection algorithm by eliminating its cell line-derived filtering steps, and instead, prioritize candidates using a custom workflow that integrates genomic and transcriptomic sequence alignment, biological and technical annotations, customized categorization logic, and phenotypic prioritization.


We demonstrate that our approach to fusion transcript identification and prioritization detects genuine fusion events excluded by standard analyses and efficiently removes phenotypically unimportant candidates and false positive events, resulting in a reduced candidate list enriched for events with potential phenotypic relevance. We describe the successful genetic resolution of two previously undiagnosed disease cases through the detection of pathogenic fusion transcripts. Furthermore, we report the experimental validation of five additional cases of fusion transcripts with potential phenotypic relevance.


The approach we describe can be implemented to enable the detection of phenotypically relevant fusion transcripts in studies of rare inherited disease. Fusion transcript detection has the potential to increase diagnostic rates in rare inherited disease and should be included in RNA-based analytical pipelines aimed at genetic diagnosis.

Klíčová slova:

Blood – Cell fusion – Diagnostic medicine – Genetics of disease – Multiple alignment calculation – Polymerase chain reaction – RNA sequencing – Sequence alignment


1. Sawyer SL, Hartley T, Dyment DA, Beaulieu CL, Schwartzentruber J, Smith A, et al. Utility of whole-exome sequencing for those near the end of the diagnostic odyssey: time to address gaps in care. Clin Genet. 2016;89(3):275–84. doi: 10.1111/cge.12654 26283276

2. Posey JE, Rosenfeld JA, James RA, Bainbridge M, Niu Z, Wang X, et al. Molecular diagnostic experience of whole-exome sequencing in adult patients. Genetics in medicine: official journal of the American College of Medical Genetics. 2016;18(7):678–85.

3. Yang YP, Muzny DM, Reid JG, Bainbridge MN, Willis A, Ward PA, et al. Clinical Whole-Exome Sequencing for the Diagnosis of Mendelian Disorders. New Engl J Med. 2013;369(16):1502–11. doi: 10.1056/NEJMoa1306555 24088041

4. Kremer LS, Wortmann SB, Prokisch H. "Transcriptomics": molecular diagnosis of inborn errors of metabolism via RNA-sequencing. J Inherit Metab Dis. 2018;41(3):525–32. doi: 10.1007/s10545-017-0133-4 29372369

5. Byron SA, Van Keuren-Jensen KR, Engelthaler DM, Carpten JD, Craig DW. Translating RNA sequencing into clinical diagnostics: opportunities and challenges. Nat Rev Genet. 2016;17(5):257–71. doi: 10.1038/nrg.2016.10 26996076

6. Cummings BB, Marshall JL, Tukiainen T, Lek M, Donkervoort S, Foley AR, et al. Improving genetic diagnosis in Mendelian disease with transcriptome sequencing. Sci Transl Med. 2017;9(386).

7. Kremer LS, Bader DM, Mertes C, Kopajtich R, Pichler G, Iuso A, et al. Genetic diagnosis of Mendelian disorders via RNA sequencing. Nat Commun. 2017;8. doi: 10.1038/s41467-017-00021-9

8. Fresard L, Smail C, Ferraro NM, Teran NA, Li X, Smith KS, et al. Identification of rare-disease genes using blood transcriptome sequencing and large control cohorts. Nat Med. 2019;25(6):911–9. doi: 10.1038/s41591-019-0457-8 31160820

9. Dai X, Theobard R, Cheng H, Xing M, Zhang J. Fusion genes: A promising tool combating against cancer. Biochim Biophys Acta Rev Cancer. 2018;1869(2):149–60. doi: 10.1016/j.bbcan.2017.12.003 29357299

10. van Heesch S, Simonis M, van Roosmalen MJ, Pillalamarri V, Brand H, Kuijk EW, et al. Genomic and Functional Overlap between Somatic and Germline Chromosomal Rearrangements. Cell Rep. 2014;9(6):2001–10. doi: 10.1016/j.celrep.2014.11.022 25497101

11. Nothwang HG, Kim HG, Aoki J, Geisterfer M, Kubart S, Wegner RD, et al. Functional hemizygosity of PAFAH1B3 due to a PAFAH1B3-CLK2 fusion gene in a female with mental retardation, ataxia and atrophy of the brain. Hum Mol Genet. 2001;10(8):797–806. doi: 10.1093/hmg/10.8.797 11285245

12. Ramocki MB, Dowling J, Grinberg I, Kimonis VE, Cardoso C, Gross A, et al. Reciprocal fusion transcripts of two novel Zn-finger genes in a female with absence of the corpus callosum, ocular colobomas and a balanced translocation between chromosomes 2p24 and 9q32. European Journal of Human Genetics. 2003;11(7):527–34. doi: 10.1038/sj.ejhg.5200995 12825074

13. Di Gregorio E, Bianchi FT, Schiavi A, Chiotto AM, Rolando M, Verdun di Cantogno L, et al. A de novo X;8 translocation creates a PTK2-THOC2 gene fusion with THOC2 expression knockdown in a patient with psychomotor retardation and congenital cerebellar hypoplasia. J Med Genet. 2013;50(8):543–51. doi: 10.1136/jmedgenet-2013-101542 23749989

14. Yue Y, Grossmann B, Holder SE, Haaf T. De novo t(7;10)(q33;q23) translocation and closely juxtaposed microdeletion in a patient with macrocephaly and developmental delay. Hum Genet. 2005;117(1):1–8. doi: 10.1007/s00439-005-1273-4 15834588

15. Backx L, Seuntjens E, Devriendt K, Vermeesch J, Van Esch H. A balanced translocation t(6;14)(q25.3;q13.2) leading to reciprocal fusion transcripts in a patient with intellectual disability and agenesis of corpus callosum. Chromosome Res. 2011;19:S59–S.

16. Hackmann K, Matko S, Gerlach EM, von der Hagen M, Klink B, Schrock E, et al. Partial deletion of GLRB and GRIA2 in a patient with intellectual disability. European Journal of Human Genetics. 2013;21(1):112–4. doi: 10.1038/ejhg.2012.97 22669415

17. Moyses-Oliveira M, Guilherme RS, Meloni VA, Di Battista A, de Mello CB, Bragagnolo S, et al. X-linked intellectual disability related genes disrupted by balanced X-autosome translocations. Am J Med Genet B. 2015;168(8):669–77.

18. Mayo S, Monfort S, Rosello M, Orellana C, Oltra S, Caro-Llopis A, et al. Chimeric Genes in Deletions and Duplications Associated with Intellectual Disability. Int J Genomics. 2017;2017:4798474. doi: 10.1155/2017/4798474 28630856

19. Zhou X, Chen Q, Schaukowitch K, Kelsoe JR, Geyer MA. Insoluble DISC1-Boymaw fusion proteins generated by DISC1 translocation. Mol Psychiatry. 2010;15(7):669–72. doi: 10.1038/mp.2009.127 20351725

20. Rippey C, Walsh T, Gulsuner S, Brodsky M, Nord AS, Gasperini M, et al. Formation of chimeric genes by copy-number variation as a mutational mechanism in schizophrenia. Am J Hum Genet. 2013;93(4):697–710. doi: 10.1016/j.ajhg.2013.09.004 24094746

21. Boone PM, Yuan B, Campbell IM, Scull JC, Withers MA, Baggett BC, et al. The Alu-rich genomic architecture of SPAST predisposes to diverse and functionally distinct disease-associated CNV alleles. Am J Hum Genet. 2014;95(2):143–61. doi: 10.1016/j.ajhg.2014.06.014 25065914

22. Ceroni F, Sagar A, Simpson NH, Gawthrope AJ, Newbury DF, Pinto D, et al. A deletion involving CD38 and BST1 results in a fusion transcript in a patient with autism and asthma. Autism Res. 2014;7(2):254–63. doi: 10.1002/aur.1365 24634087

23. Bertelsen B, Melchior L, Jensen LR, Groth C, Nazaryan L, Debes NM, et al. A t(3;9)(q25.1;q34.3) translocation leading to OLFM1 fusion transcripts in Gilles de la Tourette syndrome, OCD and ADHD. Psychiatry Res. 2015;225(3):268–75. doi: 10.1016/j.psychres.2014.12.028 25595337

24. Mansouri MR, Carlsson B, Davey E, Nordenskjold A, Wester T, Anneren G, et al. Molecular genetic analysis of a de novo balanced translocation t(6;17)(p21.31;q11.2) associated with hypospadias and anorectal malformation. Hum Genet. 2006;119(1–2):162–8. doi: 10.1007/s00439-005-0122-9 16395596

25. Borsani G, Piovani G, Zoppi N, Bertini V, Bini R, Notarangelo L, et al. Cytogenetic and molecular characterization of a de-novo t(2p;7p) translocation involving TNS3 and EXOC6B genes in a boy with a complex syndromic phenotype. Eur J Med Genet. 2008;51(4):292–302. doi: 10.1016/j.ejmg.2008.02.006 18424204

26. Kumar S, Vo AD, Qin FJ, Li H. Comparative assessment of methods for the fusion transcripts detection from RNA-Seq data. Sci Rep-Uk. 2016;6.

27. Peng ZY, Yuan CF, Zellmer L, Liu SQ, Xu NZ, Liao DJ. Hypothesis: Artifacts, Including Spurious Chimeric RNAs with a Short Homologous Sequence, Caused by Consecutive Reverse Transcriptions and Endogenous Random Primers. J Cancer. 2015;6(6):555–67. doi: 10.7150/jca.11997 26000048

28. Akiva P, Toporik A, Edelheit S, Peretz Y, Diber A, Shemesh R, et al. Transcription-mediated gene fusion in the human genome. Genome Res. 2006;16(1):30–6. doi: 10.1101/gr.4137606 16344562

29. He Y, Yuan C, Chen L, Lei M, Zellmer L, Huang H, et al. Transcriptional-Readthrough RNAs Reflect the Phenomenon of "A Gene Contains Gene(s)" or "Gene(s) within a Gene" in the Human Genome, and Thus Are Not Chimeric RNAs. Genes (Basel). 2018;9(1).

30. Yuan C, Han Y, Zellmer L, Yang W, Guan Z, Yu W, et al. It Is Imperative to Establish a Pellucid Definition of Chimeric RNA and to Clear Up a Lot of Confusion in the Relevant Research. Int J Mol Sci. 2017;18(4).

31. Babiceanu M, Qin FJ, Xie ZQ, Jia YM, Lopez K, Janus N, et al. Recurrent chimeric fusion RNAs in non-cancer tissues and cells. Nucleic Acids Res. 2016;44(6):2859–72. doi: 10.1093/nar/gkw032 26837576

32. Aigner J, Villatoro S, Rabionet R, Roquer J, Jimenez-Conde J, Marti E, et al. A common 56-kilobase deletion in a primate-specific segmental duplication creates a novel butyrophilin-like protein. Bmc Genet. 2013;14:61. doi: 10.1186/1471-2156-14-61 23829304

33. Yuan H, Qin F, Movassagh M, Park H, Golden W, Xie Z, et al. A chimeric RNA characteristic of rhabdomyosarcoma in normal myogenesis process. Cancer Discov. 2013;3(12):1394–403. doi: 10.1158/2159-8290.CD-13-0186 24089019

34. Cousin MA, Smith MJ, Sigafoos AN, Jin JJ, Murphree MI, Boczek NJ, et al. Utility of DNA, RNA, Protein, and Functional Approaches to Solve Cryptic Immunodeficiencies. J Clin Immunol. 2018;38(3):307–19. doi: 10.1007/s10875-018-0499-6 29671115

35. Kim D, Salzberg SL. TopHat-Fusion: an algorithm for discovery of novel fusion transcripts. Genome Biol. 2011;12(8).

36. Altschul SF, Gish W, Miller W, Myers EW, Lipman DJ. Basic Local Alignment Search Tool. J Mol Biol. 1990;215(3):403–10. doi: 10.1016/S0022-2836(05)80360-2 2231712

37. Zerbino DR, Achuthan P, Akanni W, Amode MR, Barrell D, Bhai J, et al. Ensembl 2018. Nucleic Acids Res. 2018;46(D1):D754–D61. doi: 10.1093/nar/gkx1098 29155950

38. Carithers LJ, Ardlie K, Barcus M, Branton PA, Britton A, Buia SA, et al. A Novel Approach to High-Quality Postmortem Tissue Procurement: The GTEx Project. Biopreserv Biobank. 2015;13(5):311–9. doi: 10.1089/bio.2015.0032 26484571

39. Hamosh A, Scott AF, Amberger J, Bocchini C, Valle D, McKusick VA. Online Mendelian Inheritance in Man (OMIM), a knowledgebase of human genes and genetic disorders. Nucleic Acids Res. 2002;30(1):52–5. doi: 10.1093/nar/30.1.52 11752252

40. Stelzer G, Rosen N, Plaschkes I, Zimmerman S, Twik M, Fishilevich S, et al. The GeneCards Suite: From Gene Data Mining to Disease Genome Sequence Analyses. Curr Protoc Bioinformatics. 2016;54:1 30 1–1 3. doi: 10.1002/cpbi.5 27322403

41. Godard P, Page M. PCAN: phenotype consensus analysis to support disease-gene association. BMC Bioinformatics. 2016;17(1):518. doi: 10.1186/s12859-016-1401-2 27923364

42. Landrum MJ, Lee JM, Benson M, Brown G, Chao C, Chitipiralla S, et al. ClinVar: public archive of interpretations of clinically relevant variants. Nucleic Acids Res. 2016;44(D1):D862–D8. doi: 10.1093/nar/gkv1222 26582918

43. Fabregat A, Jupe S, Matthews L, Sidiropoulos K, Gillespie M, Garapati P, et al. The Reactome Pathway Knowledgebase. Nucleic Acids Res. 2018;46(D1):D649–D55. doi: 10.1093/nar/gkx1132 29145629

44. Szklarczyk D, Morris JH, Cook H, Kuhn M, Wyder S, Simonovic M, et al. The STRING database in 2017: quality-controlled protein-protein association networks, made broadly accessible. Nucleic Acids Res. 2017;45(D1):D362–D8. doi: 10.1093/nar/gkw937 27924014

45. Oliver GR, Blackburn PR, Ellingson MS, Conboy E, Pinto EVF, Webley M, et al. RNA-Seq detects a SAMD12-EXT1 fusion transcript and leads to the discovery of an EXT1 deletion in a child with multiple osteochondromas. Mol Genet Genomic Med. 2019:e00560. doi: 10.1002/mgg3.560 30632316

46. Philippe C, Porter DE, Emerton ME, Wells DE, Simpson AH, Monaco AP. Mutation screening of the EXT1 and EXT2 genes in patients with hereditary multiple exostoses. Am J Hum Genet. 1997;61(3):520–8. doi: 10.1086/515505 9326317

47. Wuyts W, Van Hul W. Molecular basis of multiple exostoses: mutations in the EXT1 and EXT2 genes. Hum Mutat. 2000;15(3):220–7. doi: 10.1002/(SICI)1098-1004(200003)15:3<220::AID-HUMU2>3.0.CO;2-K 10679937

48. Szuhai K, Jennes I, de Jong D, Bovee JV, Wiweger M, Wuyts W, et al. Tiling resolution array-CGH shows that somatic mosaic deletion of the EXT gene is causative in EXT gene mutation negative multiple osteochondromas patients. Hum Mutat. 2011;32(2):E2036–49. doi: 10.1002/humu.21423 21280143

49. Sarrion P, Sangorrin A, Urreizti R, Delgado A, Artuch R, Martorell L, et al. Mutations in the EXT1 and EXT2 genes in Spanish patients with multiple osteochondromas. Sci Rep-Uk. 2013;3.

50. Richards S, Aziz N, Bale S, Bick D, Das S, Gastier-Foster J, et al. Standards and guidelines for the interpretation of sequence variants: a joint consensus recommendation of the American College of Medical Genetics and Genomics and the Association for Molecular Pathology. Genet Med. 2015;17(5):405–24. doi: 10.1038/gim.2015.30 25741868

51. Jennes I, Pedrini E, Zuntini M, Mordenti M, Balkassmi S, Asteggiano CG, et al. Multiple osteochondromas: mutation update and description of the multiple osteochondromas mutation database (MOdb). Hum Mutat. 2009;30(12):1620–7. doi: 10.1002/humu.21123 19810120

52. Boettger LM, Handsaker RE, Zody MC, McCarroll SA. Structural haplotypes and recent evolution of the human 17q21.31 region. Nat Genet. 2012;44(8):881–5. doi: 10.1038/ng.2334 22751096

53. Chase J, Fiebig A, Ernst T, Grand F, Reiter A, Erben P, et al. A Polymorphic Constitutional Tfg-Gpr128 Fusion in Healthy Individuals Identified by Targeted Array Cgh. Haematol-Hematol J. 2009;94:218–.

54. Boone PM, Yuan B, Campbell IM, Scul JC, Withers MA, Baggett BC, et al. The Alu-Rich Genomic Architecture of SPAST Predisposes to Diverse and Functionally Distinct Disease-Associated CNV Alleles. Am J Hum Genet. 2014;95(2):143–61. doi: 10.1016/j.ajhg.2014.06.014 25065914

55. Boczek NJ, Hopp K, Benoit L, Kraft D, Cousin MA, Blackburn PR, et al. Characterization of three ciliopathy pedigrees expands the phenotype associated with biallelic C2CD3 variants. Eur J Hum Genet. 2018;26(12):1797–809. doi: 10.1038/s41431-018-0222-3 30097616

Článek vyšel v časopise


2019 Číslo 10
Nejčtenější tento týden