Genome-wide copy number variation in Hanwoo, Black Angus, and Holstein cattle

Abstract

Hanwoo, Korean native cattle, is indigenous to the Korean peninsula. They have been used mainly as draft animals for about 5,000 years; however, in the last 30 years, their main role has been changed to meat production by selective breeding which has led to substantial increases in their productivity. Massively parallel sequencing technology has recently made possible the systematic identification of structural variations in cattle genomes. In particular, copy number variation (CNV) has been recognized as an important genetic variation complementary to single-nucleotide polymorphisms that can be used to account for variations of economically important traits in cattle. Here we report genome-wide copy number variation regions (CNVRs) in Hanwoo cattle obtained by comparing the whole genome sequence of Hanwoo with Black Angus and Holstein sequence datasets. We identified 1,173 and 963 putative CNVRs representing 16.7 and 7.8 Mbp from comparisons between Black Angus and Hanwoo and between Holstein and Hanwoo, respectively. The potential functional roles of the CNVRs were assessed by Gene Ontology enrichment analysis. The results showed that response to stimulus, immune system process, and cellular component organization were highly enriched in the genic-CNVRs that overlapped with annotated cattle genes. Of the 11 CNVRs that were selected for validation by quantitative real-time PCR, 9 exhibited the expected copy number differences. The results reported in this study show that genome-wide CNVs were detected successfully using massively parallel sequencing technology. The CNVs may be a valuable resource for further studies to correlate CNVs and economically important traits in cattle.

Copy number variations in Hanwoo and Yanbian cattle genomes using the massively parallel sequencing data

“…Despite the higher possibility, fewer amounts of CNVRs were detected in this study; the number of CNVs indicates that HAN may have a closer genetic relationship with YAN than the European-origin breeds. The CNVRs were unevenly distributed across the genome (Figure 1), which is consistent with the findings of previous studies on cattle (Fadista et al, 2010;Stothard et al, 2011;Bickhart et al, 2012;Hou et al, 2012;Choi et al, 2013;Jiang et al, 2013;Choi et al, 2014).…”

“…The CNVRs varied in size from 1,621 bp to 161,921 bp, and the average and median sizes corresponded to 6,147 bp and 5,085 bp, respectively (Table 2). These results further support that NGS-based CNV detection can significantly improve resolution of detected CNVs, even though we applied strict parameters to accurately detect CNVs, which produces a trade-off between resolution and specificity of CNVR detection (Bae et al, 2010;Fadista et al, 2010;Hou et al, 2011;Stothard et al, 2011;Hou et al, 2012;Choi et al, 2013;Choi et al, 2014 and 90.3%) were smaller than 10 kbp (Figure 2A). …”

“…All of the generated best-hit files were used to run the cnv-seq.pl script with a default P-value = 0.001. In order to detect reliable CNVRs, we applied strict parameters in the CNV-seq program (log 2 threshold = 0.7, window size = 5, minimum-windows-required = 10), which successfully produced a higher validation rate in a previous study (Choi et al, 2013).…”

“…All of the sequence reads were then converted into best-hit files for HAN and YAN, respectively, as input files for running the CNV-seq program. CNV-seq was run by applying strict custom criteria that were used in a previous study that showed a highly successful validation rate (Choi et al, 2013).…”

“…In particular, the array-based approaches granted the opportunity to detect genome-wide CNVs at a population scale using multiple cattle breeds (Fadista et al, 2010;Hou et al, 2011;Hou et al, 2012;Jiang et al, 2013). Furthermore, recent advances in NGS technologies have successfully been used to detect CNVs with higher resolution and accuracy in cattle genomes (Stothard et al, 2011;Bickhart et al, 2012;Choi et al, 2013;Choi et al, 2014); however, sequencing is still too expensive to perform genome-wide population-scale CNV analyses.…”