Next-Generation Sequencing and Mutational Analysis: Implications for Genes Encoding LINC Complex Proteins

  • Peter L. NagyEmail author
  • Howard J. Worman
Part of the Methods in Molecular Biology book series (MIMB, volume 1840)


Targeted panel, whole exome, or whole genome DNA sequencing using next-generation sequencing (NGS) allows for extensive high-throughput investigation of molecular machines/systems such as the LINC complex. This includes the identification of genetic variants in humans that cause disease, as is the case for some genes encoding LINC complex proteins. The relatively low cost and high speed of the sequencing process results in large datasets at various stages of analysis and interpretation. For those not intimately familiar with the process, interpretation of the data might prove challenging. This review lays out the most important and most commonly used materials and methods of NGS. It also discusses data analysis and potential pitfalls one might encounter because of peculiarities of the laboratory methodology or data analysis pipelines.

Key words

DNA sequencing DNA sequence analysis LINC complex Mutation Next-generation sequencing Polymorphism Sequence variants 



H.J.W. is supported by National Institute of Arthritis and Musculoskeletal and Skin Diseases of the National Institutes of Health under award numbers R01AR048997 and R01AR068636. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.


P.L.N. is an employee of MNG Laboratories and owns equity in the company. H.J.W. serves on the scientific advisory board of MNG Laboratories and receives fees for this service.


  1. 1.
    Gros-Louis F, Dupré N, Dion P et al (2007) Mutations in SYNE1 lead to a newly discovered form of autosomal recessive cerebellar ataxia. Nat Genet 39:80–85CrossRefPubMedGoogle Scholar
  2. 2.
    Noreau A, Bourassa CV, Szuto A et al (2013) SYNE1 mutations in autosomal recessive cerebellar ataxia. JAMA Neurol 70:1296–1231PubMedGoogle Scholar
  3. 3.
    Synofzik M, Smets K, Mallaret M et al (2016) SYNE1 ataxia is a common recessive ataxia with major non-cerebellar features: a large multi-centre study. Brain 2016(139):1378–1393CrossRefGoogle Scholar
  4. 4.
    Mademan I, Harmuth F, Giordano I et al (2016) Multisystemic SYNE1 ataxia: confirming the high frequency and extending the mutational and phenotypic spectrum. Brain 139:e46CrossRefPubMedPubMedCentralGoogle Scholar
  5. 5.
    Attali R, Warwar N, Israel A et al (2009) Mutation of SYNE-1, encoding an essential component of the nuclear lamina, is responsible for autosomal recessive arthrogryposis. Hum Mol Genet 18:3462–3469CrossRefPubMedGoogle Scholar
  6. 6.
    Laquérriere A, Maluenda J, Camus A et al (2014) Mutations in CNTNAP1 and ADCY6 are responsible for severe arthrogryposis multiplex congenita with axoglial defects. Hum Mol Genet 23:2279–2289CrossRefPubMedGoogle Scholar
  7. 7.
    Baumann M, Steichen-Gersdorf E, Krabichler B et al (2017) Homozygous SYNE1 mutation causes congenital onset of muscular weakness with distal arthrogryposis: a genotype-phenotype correlation. Eur J Hum Genet 25:262–266CrossRefPubMedGoogle Scholar
  8. 8.
    Horn HF, Brownstein Z, Lenz DR et al (2013) The LINC complex is essential for hearing. J Clin Invest 123:740–750PubMedPubMedCentralGoogle Scholar
  9. 9.
    Zhang Q, Bethmann C, Worth NF et al (2007) Nesprin-1 and -2 are involved in the pathogenesis of Emery Dreifuss muscular dystrophy and are critical for nuclear envelope integrity. Hum Mol Genet 16:2816–2833CrossRefPubMedPubMedCentralGoogle Scholar
  10. 10.
    Puckelwartz MJ, Kessler EJ, Kim G (2010) Nesprin-1 mutations in human and murine cardiomyopathy. J Mol Cell Cardiol 48:600–608CrossRefPubMedGoogle Scholar
  11. 11.
    Zhou C, Li C, Zhou B et al (2017) Novel nesprin-1 mutations associated with dilated cardiomyopathy cause nuclear envelope disruption and defects in myogenesis. Hum Mol Genet 26:2258–2276CrossRefPubMedPubMedCentralGoogle Scholar
  12. 12.
    Meinke P, Mattioli E, Haque F et al (2014) Muscular dystrophy-associated SUN1 and SUN2 variants disrupt nuclear-cytoskeletal connections and myonuclear organization. PLoS Genet 10:e1004605CrossRefPubMedPubMedCentralGoogle Scholar
  13. 13.
    Puckelwartz MJ, Kessler E, Zhang Y et al (2009) Disruption of nesprin-1 produces an Emery Dreifuss muscular dystrophy-like phenotype in mice. Disruption of nesprin-1 produces an Emery Dreifuss muscular dystrophy-like phenotype in mice. Hum Mol Genet 18:607–620CrossRefPubMedPubMedCentralGoogle Scholar
  14. 14.
    Zhang J, Felder A, Liu Y et al (2010) Nesprin 1 is critical for nuclear positioning and anchorage. Hum Mol Genet 19:329–341CrossRefPubMedPubMedCentralGoogle Scholar
  15. 15.
    Stroud MJ, Feng W, Zhang J et al (2017) Nesprin-1α2 is essential for mouse postnatal viability and nuclear positioning in skeletal muscle. J Cell Biol 216:1915–1924CrossRefPubMedPubMedCentralGoogle Scholar
  16. 16.
    Bione S, Maestrini E, Rivella S et al (1994) Identification of a novel X-linked gene responsible for Emery-Dreifuss muscular dystrophy. Nat Genet 8:323–327CrossRefPubMedPubMedCentralGoogle Scholar
  17. 17.
    Bonne G, Di Barletta MR, Varnous S et al (1999) Mutations in the gene encoding lamin A/C cause autosomal dominant Emery-Dreifuss muscular dystrophy. Nat Genet 21:285–288CrossRefPubMedPubMedCentralGoogle Scholar
  18. 18.
    Bamshad MJ, Ng SB, Bigham AW et al (2011) Exome sequencing as a tool for Mendelian disease gene discovery. Nat Rev Genet 12:745–755CrossRefPubMedPubMedCentralGoogle Scholar
  19. 19.
    Nagy PL, Mansukhani M (2015) The role of clinical genomic testing in diagnosis and discovery of pathogenic mutations. Expert Rev Mol Diagn 15:1101–1105CrossRefPubMedPubMedCentralGoogle Scholar
  20. 20.
    Wang Y, Lichter-Konecki U, Anyane-Yeboa K et al (2016) A mutation abolishing the ZMPSTE24 cleavage site in prelamin A causes a progeroid disorder. J Cell Sci 129:1975–1980CrossRefPubMedPubMedCentralGoogle Scholar
  21. 21.
    Boland JF, Chung CC, Roberson D et al (2013) The new sequencer on the block: comparison of Life Technology’s Proton sequencer to an Illumina HiSeq for whole-exome sequencing. Hum Genet 132:1153–1163CrossRefPubMedPubMedCentralGoogle Scholar
  22. 22.
    English AC, Salerno WJ, Hampton OA et al (2015) Assessing structural variation in a personal genome-towards a human reference diploid genome. BMC Genomics 16:286CrossRefPubMedPubMedCentralGoogle Scholar
  23. 23.
    Mandelker D, Amr SS, Pugh T et al (2014) Comprehensive diagnostic testing for stereocilin: an approach for analyzing medically important genes with high homology. J Mol Diagn 16:639–647CrossRefPubMedGoogle Scholar
  24. 24.
    Kennedy SR, Schmitt MW, Fox EJ et al (2014) Detecting ultralow-frequency mutations by duplex sequencing. Nature Protoc 9:2586–5606CrossRefGoogle Scholar
  25. 25.
    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 20:1297–1303CrossRefPubMedPubMedCentralGoogle Scholar
  26. 26.
    DePristo MA, Banks E, Poplin R et al (2011) A framework for variation discovery and genotyping using next-generation DNA sequencing data. Nat Genet 43:491–498CrossRefPubMedPubMedCentralGoogle Scholar
  27. 27.
    Van der Auwera GA, Carneiro MO et al (2013) From FastQ data to high confidence variant calls: the Genome Analysis Toolkit best practices pipeline. Curr Protoc Bioinformatics 43:11.10.1–11.1033Google Scholar
  28. 28.
    Tsai EA, Shakbatyan R, Evans J et al (2016) Bioinformatics workflow for clinical whole genome sequencing at Partners HealthCare Personalized Medicine. J Pers Med 6:12CrossRefPubMedCentralGoogle Scholar
  29. 29.
    Wang K, Li M, Hakonarson H (2010) ANNOVAR: functional annotation of genetic variants from high-throughput sequencing data. Nucleic Acids Res 38:e164CrossRefPubMedPubMedCentralGoogle Scholar
  30. 30.
    Richards S, Aziz N, Bale S et al (2015) 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 17:405–423CrossRefPubMedPubMedCentralGoogle Scholar
  31. 31.
    Green RC, Berg JS, Grody WW et al (2013) ACMG recommendations for reporting of incidental findings in clinical exome and genome sequencing. Genet Med 15:565–574CrossRefPubMedPubMedCentralGoogle Scholar
  32. 32.
    Topol EJ (2015) The big medical data miss: challenges in establishing an open medical resource. Nat Rev Genet 16:253–254CrossRefPubMedGoogle Scholar
  33. 33.
    Luu TD, Rusu AM, Walter V et al (2012) MSV3d: database of human MisSense Variants mapped to 3D protein structure. Database (Oxford) 2012:bas018CrossRefGoogle Scholar
  34. 34.
    Alfaro JA, Sinha A, Kislinger T et al (2014) Onco-proteogenomics: cancer proteomics joins forces with genomics. Nat Methods 11:1107–1113CrossRefPubMedPubMedCentralGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.MNG LaboratoriesAtlantaUSA
  2. 2.Department of Medicine and Department of Pathology and Cell Biology, Vagelos College of Physicians and SurgeonsColumbia UniversityNew YorkUSA

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