Global assessment of genomic variation in cattle by genome resequencing and high-throughput genotyping

Background: Integration of genomic variation with phenotypic information is an effective approach for uncovering genotype-phenotype associated traits. This requires the accurate identification of the different types of variants in individual genomes. Results: Here we have generated 15 fold coverage of a Holstein bull genome sequence. By integrating this data with SNP and CGH array technologies, the whole spectrum of genomic variation was analyzed. Performance of resequencing SNP detection pipelines was assessed by combining SNPs identified in IBD and CNV regions with results from SNP array. A significant genome-wide correlation of SNP-indel densities was identified. Coding indels were found to be enriched for sizes multiple of 3 and tended to be enriched near the termini of proteins. For larger indels, a combination of split-read and read-pair approaches proved to be complementary in the way each method find variants with different signatures. CNVs were detected with read depth of sequenced reads, SNP and CGH arrays, which was important for detecting resolution and coverage biases in each individual platform. Conclusions: Our results depicted the most detailed map of genomic variation in an individual bovine genome, with structural variation surpassing sequence variation as the main component of genomic variability. Algorithms implementing mapping quality achieved better SNP detecting accuracy without losing significant sensitivity compared with methods without mapping quality. SNP detection within CNVs tended to be less reliable. At this level of sequencing coverage, an ensemble of platforms and tools should be used to maximize the detection of sequence and structural variants.