Dataset Information

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

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.

SUBMITTER: Wang Y

PROVIDER: S-EPMC4412667 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature

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Publications

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

Li Wei W Xia Yingying Y Wang Chongzhi C Tang Y Tom YT Guo Wenying W Li Jinliang J Zhao Xia X Sun Yepeng Y Hu Juan J Zhen Hefu H Zhang Xiandong X Chen Chao C Shi Yujian Y Li Lin L Cao Hongzhi H Du Hongli H Li Jian J

PloS one 20140101 4

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 appr ...[more]

PMID: 25919136

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Project description:BackgroundSNP genotyping arrays have been developed to characterize single-nucleotide polymorphisms (SNPs) and DNA copy number variations (CNVs). Nonparametric and model-based statistical algorithms have been developed to detect CNVs from SNP data using the marker intensities. However, these algorithms lack specificity to detect small CNVs owing to the high false positive rate when calling CNVs based on the intensity values. Therefore, the resulting association tests lack power even if the CNVs affecting disease risk are common. An alternative procedure called PennCNV uses information from both the marker intensities as well as the genotypes and therefore has increased sensitivity.ResultsBy using the hidden Markov model (HMM) implemented in PennCNV to derive the probabilities of different copy number states which we subsequently used in a logistic regression model, we developed a new genome-wide algorithm to detect CNV associations with diseases. We compared this new method with association test applied to the most probable copy number state for each individual that is provided by PennCNV after it performs an initial HMM analysis followed by application of the Viterbi algorithm, which removes information about copy number probabilities. In one of our simulation studies, we showed that for large CNVs (number of SNPs ? 10), the association tests based on PennCNV calls gave more significant results, but the new algorithm retained high power. For small CNVs (number of SNPs <10), the logistic algorithm provided smaller average p-values (e.g., p = 7.54e - 17 when relative risk RR = 3.0) in all the scenarios and could capture signals that PennCNV did not (e.g., p = 0.020 when RR = 3.0). From a second set of simulations, we showed that the new algorithm is more powerful in detecting disease associations with small CNVs (number of SNPs ranging from 3 to 5) under different penetrance models (e.g., when RR = 3.0, for relatively weak signals, power = 0.8030 comparing to 0.2879 obtained from the association tests based on PennCNV calls). The new method was implemented in software GWCNV. It is freely available at http://gwcnv.sourceforge.net, distributed under a GPL license.ConclusionsWe conclude that the new algorithm is more sensitive and can be more powerful in detecting CNV associations with diseases than the existing HMM algorithm, especially when the CNV association signal is weak and a limited number of SNPs are located in the CNV.

Dataset Information

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

Publications

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

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