VarCover: Allele Min-Set Cover Software.

To facilitate reference-material selection for clinical genetic testing laboratories, we developed VarCover, open-source software hosted on GitHub, which accepts a file of variants and returns an approximately minimum set (min-set) of samples covering the targeted alleles. VarCover employs the SetCoverPy package, sample weights, and preselection of singleton-possessing samples to efficiently solve the min-set cover problem. As a test case, we attempted to find a min-set of reference samples from the 1000 Genomes Project to cover 237 variants considered putatively pathogenic (of which 12 were classified as pathogenic or likely pathogenic) in the original 56 medically actionable genes recommended by the American College of Medical Genetics and Genomics (ACMG). The number of samples, number of alleles, and processing time were measured in subsets of the 237 target alleles. VarCover identified 140 reference-material samples from the 1000 Genomes Project covering the 237 alleles in the 56 ACMG-recommended genes. Sample weights derived from the minor allele frequency spectrum increased the number of alleles in the solution set. Preselection of samples that possessed singleton target alleles reduced computational processing time when the target set size exceeded 100 alleles. VarCover provides a simple programmatic interface for identifying an approximately min-set of reference samples, thereby reducing clinical laboratory effort and molecular genetic test-validation costs.

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

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 (approximately 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.