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Letter
Nature 461, 272-276 (10 September 2009) | doi:10.1038/nature08250; Received 5 June 2009; Accepted 29 June 2009; Published online 16 August 2009
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Targeted capture and massively parallel sequencing of 12 human exomes
Sarah B. Ng1, Emily H. Turner1, Peggy D. Robertson1, Steven D. Flygare1, Abigail W. Bigham2, Choli Lee1, Tristan Shaffer1, Michelle Wong1, Arindam Bhattacharjee4, Evan E. Eichler1,3, Michael Bamshad2, Deborah A. Nickerson1 & Jay Shendure1
- Department of Genome Sciences,
- Department of Pediatrics, University of Washington,
- Howard Hughes Medical Institute, Seattle, Washington 98195, USA
- Agilent Technologies, Santa Clara, California 95051, USA
Correspondence to: Sarah B. Ng1Jay Shendure1 Correspondence and requests for materials should be addressed to J.S. (Email: shendure@u.washington.edu) or S.B.N. (Email: sarahng@u.washington.edu).
Abstract
Genome-wide association studies suggest that common genetic variants explain only a modest fraction of heritable risk for common diseases, raising the question of whether rare variants account for a significant fraction of unexplained heritability1, 2. Although DNA sequencing costs have fallen markedly3, they remain far from what is necessary for rare and novel variants to be routinely identified at a genome-wide scale in large cohorts. We have therefore sought to develop second-generation methods for targeted sequencing of all protein-coding regions ('exomes'), to reduce costs while enriching for discovery of highly penetrant variants. Here we report on the targeted capture and massively parallel sequencing of the exomes of 12 humans. These include eight HapMap individuals representing three populations4, and four unrelated individuals with a rare dominantly inherited disorder, Freeman–Sheldon syndrome (FSS)5. We demonstrate the sensitive and specific identification of rare and common variants in over 300 megabases of coding sequence. Using FSS as a proof-of-concept, we show that candidate genes for Mendelian disorders can be identified by exome sequencing of a small number of unrelated, affected individuals. This strategy may be extendable to diseases with more complex genetics through larger sample sizes and appropriate weighting of non-synonymous variants by predicted functional impact.
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