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Targeted capture and massively parallel sequencing of 12 human exomes

Author

Listed:
  • Sarah B. Ng

    (Department of Genome Sciences,)

  • Emily H. Turner

    (Department of Genome Sciences,)

  • Peggy D. Robertson

    (Department of Genome Sciences,)

  • Steven D. Flygare

    (Department of Genome Sciences,)

  • Abigail W. Bigham

    (University of Washington)

  • Choli Lee

    (Department of Genome Sciences,)

  • Tristan Shaffer

    (Department of Genome Sciences,)

  • Michelle Wong

    (Department of Genome Sciences,)

  • Arindam Bhattacharjee

    (Agilent Technologies, Santa Clara, California 95051, USA)

  • Evan E. Eichler

    (Department of Genome Sciences,
    Howard Hughes Medical Institute, Seattle, Washington 98195, USA)

  • Michael Bamshad

    (University of Washington)

  • Deborah A. Nickerson

    (Department of Genome Sciences,)

  • Jay Shendure

    (Department of Genome Sciences,)

Abstract

Targeting the human exome DNA sequencing costs have fallen dramatically in recent years, but they are still too high for whole-genome sequencing to be used routinely to identify rare and novel variants in large cohorts. Here Ng et al. demonstrate that targeted capture and massively parallel sequencing could be a cost-effective, reproducible, and robust strategy for the sensitive and specific identification of variants causing protein-coding changes in individual human genomes. Using this 'second generation' approach to sequencing they determine 307 megabases across the exomes (the protein-coding regions of the genome) of 12 individuals. Freeman–Sheldon syndrome is used as a proof-of-concept to show that candidate genes for monogenic disorders can be identified by exome sequencing of a small number of unrelated, affected individuals.

Suggested Citation

  • Sarah B. Ng & Emily H. Turner & Peggy D. Robertson & Steven D. Flygare & Abigail W. Bigham & Choli Lee & Tristan Shaffer & Michelle Wong & Arindam Bhattacharjee & Evan E. Eichler & Michael Bamshad & D, 2009. "Targeted capture and massively parallel sequencing of 12 human exomes," Nature, Nature, vol. 461(7261), pages 272-276, September.
    • Handle: RePEc:nat:nature:v:461:y:2009:i:7261:d:10.1038_nature08250
    DOI: 10.1038/nature08250
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    Cited by:

    1. Elaine T. Lim & Yingleong Chan & Pepper Dawes & Xiaoge Guo & Serkan Erdin & Derek J. C. Tai & Songlei Liu & Julia M. Reichert & Mannix J. Burns & Ying Kai Chan & Jessica J. Chiang & Katharina Meyer & , 2022. "Orgo-Seq integrates single-cell and bulk transcriptomic data to identify cell type specific-driver genes associated with autism spectrum disorder," Nature Communications, Nature, vol. 13(1), pages 1-14, December.
    2. Zura Kakushadze & Willie Yu, 2017. "Mutation Clusters from Cancer Exome," Papers 1707.08504, arXiv.org.
    3. Miao-Xin Li & Johnny S H Kwan & Su-Ying Bao & Wanling Yang & Shu-Leong Ho & Yong-Qiang Song & Pak C Sham, 2013. "Predicting Mendelian Disease-Causing Non-Synonymous Single Nucleotide Variants in Exome Sequencing Studies," PLOS Genetics, Public Library of Science, vol. 9(1), pages 1-11, January.
    4. Thomas J Hoffmann & Bronya J Keats & Noriko Yoshikawa & Catherine Schaefer & Neil Risch & Lawrence R Lustig, 2016. "A Large Genome-Wide Association Study of Age-Related Hearing Impairment Using Electronic Health Records," PLOS Genetics, Public Library of Science, vol. 12(10), pages 1-20, October.
    5. Ni Huang & Insuk Lee & Edward M Marcotte & Matthew E Hurles, 2010. "Characterising and Predicting Haploinsufficiency in the Human Genome," PLOS Genetics, Public Library of Science, vol. 6(10), pages 1-11, October.
    6. Degui Zhi & Rui Chen, 2012. "Statistical Guidance for Experimental Design and Data Analysis of Mutation Detection in Rare Monogenic Mendelian Diseases by Exome Sequencing," PLOS ONE, Public Library of Science, vol. 7(2), pages 1-11, February.
    7. Jason Flannick & Joshua M Korn & Pierre Fontanillas & George B Grant & Eric Banks & Mark A Depristo & David Altshuler, 2012. "Efficiency and Power as a Function of Sequence Coverage, SNP Array Density, and Imputation," PLOS Computational Biology, Public Library of Science, vol. 8(7), pages 1-13, July.
    8. Kirsley Chennen & Thomas Weber & Xavière Lornage & Arnaud Kress & Johann Böhm & Julie Thompson & Jocelyn Laporte & Olivier Poch, 2020. "MISTIC: A prediction tool to reveal disease-relevant deleterious missense variants," PLOS ONE, Public Library of Science, vol. 15(7), pages 1-23, July.

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