Data-driven approach to detect common copy-number variations and frequency profiles in a population-based Korean cohort

Abstract

To date, hundreds of thousands of copy-number variation (CNV) data have been reported using various platforms. The proportion of Asians in these data is, however, relatively small as compared with that of other ethnic groups, such as Caucasians and Yorubas. Because of limitations in platform resolution and the high noise level in signal intensity, in most CNV studies (particularly those using single nucleotide polymorphism arrays), the average number of CNVs in an individual is less than the number of known CNVs. In this study, we ascertained reliable, common CNV regions (CNVRs) and identified actual frequency rates in the Korean population to provide more CNV information. We performed two-stage analyses for detecting structural variations with two platforms. We discovered 576 common CNVRs (88 CNV segments on average in an individual), and 87% (501 of 576) of these CNVRs overlapped by ≥1 bp with previously validated CNV events. Interestingly, from the frequency analysis of CNV profiles, 52 of 576 CNVRs had a frequency rate of <1% in the 8842 individuals. Compared with other common CNV studies, this study found six common CNVRs that were not reported in previous CNV studies. In conclusion, we propose the data-driven detection approach to discover common CNVRs including those of unreported in the previous Korean CNV study while minimizing false positives. Through our approach, we successfully discovered more common CNVRs than previous Korean CNV study and conducted frequency analysis. These results will be a valuable resource for the effective level of CNVs in the Korean population.