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Massively parallel sequencing of the mouse exome to accurately identify rare, induced mutations: an immediate source for thousands of new mouse models

Immunogenomics Laboratory , Australian National University , GPO Box 334, Canberra City, Australian Capital Territory, 2601 , Australia.
Open Biology (Impact Factor: 4.56). 05/2012; 2(5):120061. DOI: 10.1098/rsob.120061
Source: PubMed

ABSTRACT Accurate identification of sparse heterozygous single-nucleotide variants (SNVs) is a critical challenge for identifying the causative mutations in mouse genetic screens, human genetic diseases and cancer. When seeking to identify causal DNA variants that occur at such low rates, they are overwhelmed by false-positive calls that arise from a range of technical and biological sources. We describe a strategy using whole-exome capture, massively parallel DNA sequencing and computational analysis, which identifies with a low false-positive rate the majority of heterozygous and homozygous SNVs arising de novo with a frequency of one nucleotide substitution per megabase in progeny of N-ethyl-N-nitrosourea (ENU)-mutated C57BL/6j mice. We found that by applying a strategy of filtering raw SNV calls against known and platform-specific variants we could call true SNVs with a false-positive rate of 19.4 per cent and an estimated false-negative rate of 21.3 per cent. These error rates are small enough to enable calling a causative mutation from both homozygous and heterozygous candidate mutation lists with little or no further experimental validation. The efficacy of this approach is demonstrated by identifying the causative mutation in the Ptprc gene in a lymphocyte-deficient strain and in 11 other strains with immune disorders or obesity, without the need for meiotic mapping. Exome sequencing of first-generation mutant mice revealed hundreds of unphenotyped protein-changing mutations, 52 per cent of which are predicted to be deleterious, which now become available for breeding and experimental analysis. We show that exome sequencing data alone are sufficient to identify induced mutations. This approach transforms genetic screens in mice, establishes a general strategy for analysing rare DNA variants and opens up a large new source for experimental models of human disease.

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    • "This method has allowed the successful mutation identification of a handful of mutants in flies, worms, zebrafish, and mice, but the number of complementation groups that were mapped per report is limited. It thus remains unclear how scalable this approach is or what its success rate is when one attempts to apply WGS to identify their mutant of interest (Doitsidou et al. 2010; Earley and Jones 2011; Andrews et al. 2012; Leshchiner et al. 2012; Bull et al. 2013; Henke et al. 2013). The drawback of combining meiotic mapping and WGS is that (1) recombination mapping requires several generations of back-crossing and is less straightforward when recessive lethal mutations are being mapped, and (2) in order to sequence multiple animals per genotype, animals are typically pooled and sequenced on one lane of the Illumina sequencer at a low coverage (43–53), which fails to identify many SNVs that are present in the genome. "
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    • "Sequence analysis was performed by the Bioinformatics Unit of the Australian Phenomics Facility. A custom analysis pipeline was used to align the sequence reads with the reference genome (C57BL/6 NCBI m37), filter the raw single nucleotide variant (SNV) calls and generate a list of candidate SNVs as described [19]. Deep-sequencing datasets were deposited into the National Center for Biotechnology Information (NCBI) Sequence Read Archive (http://www.ncbi.nlm.nih.gov/sra) with the study accession number SRP020643. "
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