Identifying Human Genome-Wide CNV, LOH and UPD by Targeted Sequencing of Selected Regions

Yu Wang, Wei Li, Yingying Xia, Chongzhi Wang, Y Tom Tang, Wenying Guo, Jinliang Li, Xia Zhao, Yepeng Sun, Juan Hu, Hefu Zhen, Xiandong Zhang, Chao Chen, Yujian Shi, Lin Li, Hongzhi Cao, Hongli Du and Jian Li

PLOS ONE, 2015, vol. 10, issue 4, 1-18

Abstract: Copy-number variations (CNV), loss of heterozygosity (LOH), and uniparental disomy (UPD) are large genomic aberrations leading to many common inherited diseases, cancers, and other complex diseases. An integrated tool to identify these aberrations is essential in understanding diseases and in designing clinical interventions. Previous discovery methods based on whole-genome sequencing (WGS) require very high depth of coverage on the whole genome scale, and are cost-wise inefficient. Another approach, whole exome genome sequencing (WEGS), is limited to discovering variations within exons. Thus, we are lacking efficient methods to detect genomic aberrations on the whole genome scale using next-generation sequencing technology. Here we present a method to identify genome-wide CNV, LOH and UPD for the human genome via selectively sequencing a small portion of genome termed Selected Target Regions (SeTRs). In our experiments, the SeTRs are covered by 99.73%~99.95% with sufficient depth. Our developed bioinformatics pipeline calls genome-wide CNVs with high confidence, revealing 8 credible events of LOH and 3 UPD events larger than 5M from 15 individual samples. We demonstrate that genome-wide CNV, LOH and UPD can be detected using a cost-effective SeTRs sequencing approach, and that LOH and UPD can be identified using just a sample grouping technique, without using a matched sample or familial information.

Date: 2015
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0123081

DOI: 10.1371/journal.pone.0123081

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