Identification of copy number variations and common deletion polymorphisms in cattle
1 Laboratory of Genomic Diversity, Department of Life Science, Sogang University, Shinsu-dong, Mapo-gu, Seoul 121-742, Republic of Korea
2 Department of Genetic Epidemiology, SNP Genetics, Inc., Room 1407, Complex B, WooLim Lion's Valley, 371-28, Gasan-Dong, Geumcheon-Gu, Seoul 153-801, Republic of Korea
Citation and License
BMC Genomics 2010, 11:232 doi:10.1186/1471-2164-11-232Published: 9 April 2010
Recently, the discovery of copy number variation (CNV) led researchers to think that there are more variations of genomic DNA than initially believed. Moreover, a certain CNV region has been found to be associated with the onset of diseases. Therefore, CNV is now known as an important genomic variation in biological mechanisms. However, most CNV studies have only involved the human genome. The study of CNV involving other animals, including cattle, is severely lacking.
In our study of cattle, we used Illumina BovineSNP50 BeadChip (54,001 markers) to obtain each marker's signal intensity (Log R ratio) and allelic intensity (B allele frequency), which led to our discovery of 855 bovine CNVs from 265 cows. For these animals, the average number of CNVs was 3.2, average size was 149.8 kb, and median size was 171.5 kb. Taking into consideration some overlapping regions among the identified bovine CNVs, 368 unique CNV regions were detected. Among them, there were 76 common CNVRs with > 1% CNV frequency. Together, these CNVRs contained 538 genes. Heritability errors of 156 bovine pedigrees and comparative pairwise analyses were analyzed to detect 448 common deletion polymorphisms. Identified variations in this study were successfully validated using visual examination of the genoplot image, Mendelian inconsistency, another CNV identification program, and quantitative PCR.
In this study, we describe a map of bovine CNVs and provide important resources for future bovine genome research. This result will contribute to animal breeding and protection from diseases with the aid of genomic information.