In recent years genetic data analysis has seen a rapid increase in the scale of data to be analyzed. Schadt et al (NRG 11:647-657, 2010) offered that with data sets approaching the petabyte scale, data related challenges such as formatting, management, and transfer are increasingly important topics which need to be addressed. The use of succinct data structures is one method of reducing physical size of a data set without the use of expensive compression techniques. In this work, we consider the use of 2- and 3-bit encoding schemes for genotype data. We compare the computational performance of allele or genotype counting algorithms utilizing genotype data encoded in both schemes.
Using a small scale ENU mutagenesis approach we identified a recessive germline mutant, designated Lampe1 that exhibited growth retardation and spontaneous hepatosteatosis. Low resolution mapping based on 20 intercrossed Lampe1 mice revealed linkage to a ?14 Mb interval on the distal site of chromosome 11 containing a total of 285 genes. Exons and 50 bp flanking sequences within the critical region were enriched with sequence capture microarrays and subsequently analyzed by next-generation sequencing. Using this approach 98.1 percent of the targeted DNA was covered with a depth of 10 or more reads per nucleotide and 3 homozygote mutations were identified. Two mutations represented intronic nucleotide changes whereas one mutation affected a splice donor site in intron 11-12 of Palmitoyl Acetyl-coenzyme A oxygenase-1 (Acox1), causing skipping of exon 12. Phenotyping of Acox1(Lampe1) mutants revealed a progression from hepatosteatosis to steatohepatitis, and ultimately hepatocellular carcinoma. The current approach provides a highly efficient and affordable method to identify causative mutations induced by ENU mutagenesis and animal models relevant to human pathology.
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