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BMC Bioinformatics

, 13:330

Transcriptome analysis


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.

KeywordsTrios Oral cleft Copy number variants de novo High-throughput arrays Segmentation batch effects Genomic waves AbbreviationsSNPSingle nucleotide polymorphism

aCGHArray comparative genomic hybridization

HMMHidden Markov model

MCMCMarkov chain Monte Carlo

CNVCopy number variant

CBSCircular binary segmentation

FPFalse positive

FNFalse negative MAD: Median absolute deviation MAD

qPCRQuantitative polymerase chain reaction

CATConcordance at the top

GENEVAGene-Environment Association Studies consortium.

Electronic supplementary materialThe online version of this article doi:10.1186-1471-2105-13-330 contains supplementary material, which is available to authorized users.

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Autor: Robert B Scharpf - Terri H Beaty - Holger Schwender - Samuel G Younkin - Alan F Scott - Ingo Ruczinski


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