Dataset Information

Detection of de novo copy number deletions from targeted sequencing of trios.

ABSTRACT:

Motivation

De novo copy number deletions have been implicated in many diseases, but there is no formal method to date that identifies de novo deletions in parent-offspring trios from capture-based sequencing platforms.

Results

We developed Minimum Distance for Targeted Sequencing (MDTS) to fill this void. MDTS has similar sensitivity (recall), but a much lower false positive rate compared to less specific CNV callers, resulting in a much higher positive predictive value (precision). MDTS also exhibited much better scalability.

Availability and implementation

MDTS is freely available as open source software from the Bioconductor repository.

Supplementary information

Supplementary data are available at Bioinformatics online.

SUBMITTER: Fu JM

PROVIDER: S-EPMC6378941 | biostudies-literature | 2019 Feb

REPOSITORIES: biostudies-literature

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Publications

Detection of de novo copy number deletions from targeted sequencing of trios.

Fu Jack M JM Leslie Elizabeth J EJ Scott Alan F AF Murray Jeffrey C JC Marazita Mary L ML Beaty Terri H TH Scharpf Robert B RB Ruczinski Ingo I

Bioinformatics (Oxford, England) 20190201 4

<h4>Motivation</h4>De novo copy number deletions have been implicated in many diseases, but there is no formal method to date that identifies de novo deletions in parent-offspring trios from capture-based sequencing platforms.<h4>Results</h4>We developed Minimum Distance for Targeted Sequencing (MDTS) to fill this void. MDTS has similar sensitivity (recall), but a much lower false positive rate compared to less specific CNV callers, resulting in a much higher positive predictive value (precision ...[more]

PMID: 30084993

Similar Datasets

Project description:BackgroundIn studies of case-parent trios, we define copy number variants (CNVs) in the offspring that differ from the parental copy numbers as de novo and of interest for their potential functional role in disease. Among the leading array-based methods for discovery of de novo CNVs in case-parent trios is the joint hidden Markov model (HMM) implemented in the PennCNV software. However, the computational demands of the joint HMM are substantial and the extent to which false positive identifications occur in case-parent trios has not been well described. We evaluate these issues in a study of oral cleft case-parent trios.ResultsOur analysis of the oral cleft trios reveals that genomic waves represent a substantial source of false positive identifications in the joint HMM, despite a wave-correction implementation in PennCNV. In addition, the noise of low-level summaries of relative copy number (log R ratios) is strongly associated with batch and correlated with the frequency of de novo CNV calls. Exploiting the trio design, we propose a univariate statistic for relative copy number referred to as the minimum distance that can reduce technical variation from probe effects and genomic waves. We use circular binary segmentation to segment the minimum distance and maximum a posteriori estimation to infer de novo CNVs from the segmented genome. Compared to PennCNV on simulated data, MinimumDistance identifies fewer false positives on average and is comparable to PennCNV with respect to false negatives. Genomic waves contribute to discordance of PennCNV and MinimumDistance for high coverage de novo calls, while highly concordant calls on chromosome 22 were validated by quantitative PCR. Computationally, MinimumDistance provides a nearly 8-fold increase in speed relative to the joint HMM in a study of oral cleft trios.ConclusionsOur results indicate that batch effects and genomic waves are important considerations for case-parent studies of de novo CNV, and that the minimum distance is an effective statistic for reducing technical variation contributing to false de novo discoveries. Coupled with segmentation and maximum a posteriori estimation, our algorithm compares favorably to the joint HMM with MinimumDistance being much faster.

Dataset Information

Detection of de novo copy number deletions from targeted sequencing of trios.

Motivation

Results

Availability and implementation

Supplementary information

Publications

Detection of de novo copy number deletions from targeted sequencing of trios.

Similar Datasets

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