Mammalian Genome

, Volume 20, Issue 7, pp 424–436 | Cite as

Mutation discovery in the mouse using genetically guided array capture and resequencing

  • Mark D’Ascenzo
  • Carl Meacham
  • Jacob Kitzman
  • Christina Middle
  • Jim Knight
  • Roger Winer
  • Miroslav Kukricar
  • Todd Richmond
  • Thomas J. Albert
  • Anne Czechanski
  • Leah Rae Donahue
  • Jason Affourtit
  • Jeffrey A. Jeddeloh
  • Laura Reinholdt
Article

Abstract

Forward genetics (phenotype-driven approaches) remain the primary source for allelic variants in the mouse. Unfortunately, the gap between observable phenotype and causative genotype limits the widespread use of spontaneous and induced mouse mutants. As alternatives to traditional positional cloning and mutation detection approaches, sequence capture and next-generation sequencing technologies can be used to rapidly sequence subsets of the genome. Application of these technologies to mutation detection efforts in the mouse has the potential to significantly reduce the time and resources required for mutation identification by abrogating the need for high-resolution genetic mapping, long-range PCR, and sequencing of individual PCR amplimers. As proof of principle, we used array-based sequence capture and pyrosequencing to sequence an allelic series from the classically defined Kit locus (~200 kb) from each of five noncomplementing Kit mutants (one known allele and four unknown alleles) and have successfully identified and validated a nonsynonymous coding mutation for each allele. These data represent the first documentation and validation that these new technologies can be used to efficiently discover causative mutations. Importantly, these data also provide a specific methodological foundation for the development of large-scale mutation detection efforts in the laboratory mouse.

Keywords

Target Interval Allele Ratio Sequence Capture Mutation Discovery Homopolymeric Tract 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgments

We thank H. Halvensleben, C. Erickson, L. Freeberg, T Millard, and L. Lincoln for providing technology and logistical support operations at Roche NimbleGen; C. Turcotte and the 454 Sequencing center for library construction and sequencing support; X. Zheng and L. Dannenberg for providing project coordination support; and C. Birkenmeier and M. Berry for providing pedigree and historical data from the Kit alleles used in this study (The Jackson Laboratory). This work was supported by Roche Applied Sciences and a Cancer Center Core Grant to The Jackson Laboratory (CA34196).

Supplementary material

335_2009_9200_MOESM1_ESM.doc (50 kb)
Supplementary material 1 (DOC 50 kb)

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Copyright information

© Springer Science+Business Media, LLC 2009

Authors and Affiliations

  • Mark D’Ascenzo
    • 1
  • Carl Meacham
    • 2
  • Jacob Kitzman
    • 1
  • Christina Middle
    • 1
  • Jim Knight
    • 2
  • Roger Winer
    • 2
  • Miroslav Kukricar
    • 2
  • Todd Richmond
    • 1
  • Thomas J. Albert
    • 1
  • Anne Czechanski
    • 3
  • Leah Rae Donahue
    • 3
  • Jason Affourtit
    • 2
  • Jeffrey A. Jeddeloh
    • 1
  • Laura Reinholdt
    • 3
  1. 1.Roche NimbleGenMadisonUSA
  2. 2.454 Life SciencesBranfordUSA
  3. 3.The Jackson LaboratoryBar HarborUSA

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