Title |
Unlocking the Bottleneck in Forward Genetics Using Whole-Genome Sequencing and Identity by Descent to Isolate Causative Mutations
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Published in |
PLoS Genetics, January 2013
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DOI | 10.1371/journal.pgen.1003219 |
Pubmed ID | |
Authors |
Katherine R. Bull, Andrew J. Rimmer, Owen M. Siggs, Lisa A. Miosge, Carla M. Roots, Anselm Enders, Edward M. Bertram, Tanya L. Crockford, Belinda Whittle, Paul K. Potter, Michelle M. Simon, Ann-Marie Mallon, Steve D. M. Brown, Bruce Beutler, Christopher C. Goodnow, Gerton Lunter, Richard J. Cornall |
Abstract |
Forward genetics screens with N-ethyl-N-nitrosourea (ENU) provide a powerful way to illuminate gene function and generate mouse models of human disease; however, the identification of causative mutations remains a limiting step. Current strategies depend on conventional mapping, so the propagation of affected mice requires non-lethal screens; accurate tracking of phenotypes through pedigrees is complex and uncertain; out-crossing can introduce unexpected modifiers; and Sanger sequencing of candidate genes is inefficient. Here we show how these problems can be efficiently overcome using whole-genome sequencing (WGS) to detect the ENU mutations and then identify regions that are identical by descent (IBD) in multiple affected mice. In this strategy, we use a modification of the Lander-Green algorithm to isolate causative recessive and dominant mutations, even at low coverage, on a pure strain background. Analysis of the IBD regions also allows us to calculate the ENU mutation rate (1.54 mutations per Mb) and to model future strategies for genetic screens in mice. The introduction of this approach will accelerate the discovery of causal variants, permit broader and more informative lethal screens to be used, reduce animal costs, and herald a new era for ENU mutagenesis. |
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Germany | 1 | 13% |
France | 1 | 13% |
United States | 1 | 13% |
Unknown | 3 | 38% |
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Members of the public | 3 | 38% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
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United States | 5 | 6% |
Germany | 2 | 2% |
Argentina | 1 | 1% |
France | 1 | 1% |
Greece | 1 | 1% |
China | 1 | 1% |
Unknown | 76 | 87% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Researcher | 26 | 30% |
Student > Ph. D. Student | 17 | 20% |
Professor > Associate Professor | 9 | 10% |
Student > Postgraduate | 5 | 6% |
Student > Master | 5 | 6% |
Other | 10 | 11% |
Unknown | 15 | 17% |
Readers by discipline | Count | As % |
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Agricultural and Biological Sciences | 44 | 51% |
Biochemistry, Genetics and Molecular Biology | 14 | 16% |
Medicine and Dentistry | 4 | 5% |
Immunology and Microbiology | 3 | 3% |
Business, Management and Accounting | 1 | 1% |
Other | 4 | 5% |
Unknown | 17 | 20% |