title: Fast detection of de novo copy number variants from SNP arrays for case-parent trios.
abstract: BACKGROUND
  In 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.
  
  
  RESULTS
  Our 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.
  
  
  CONCLUSIONS
  Our 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.
authors.affiliation: Department of Oncology, Johns Hopkins University, Baltimore, MD, USA. rscharp1@jhmi.edu
authors.forename: Robert B
authors.initials: RB
authors.lastname: Scharpf
authors.forename: Terri H
authors.initials: TH
authors.lastname: Beaty
authors.forename: Holger
authors.initials: H
authors.lastname: Schwender
authors.forename: Samuel G
authors.initials: SG
authors.lastname: Younkin
authors.forename: Alan F
authors.initials: AF
authors.lastname: Scott
authors.forename: Ingo
authors.initials: I
authors.lastname: Ruczinski
mesh_terms: D000465:Algorithms; D002648:Child; D002972:Cleft Palate / Q000235:genetics; D056915:DNA Copy Number Variations; D015894:Genome, Human; D006801:Humans; D020411:Oligonucleotide Array Sequence Analysis / Q000706:statistics & numerical data*; D010290:Parents; D020641:Polymorphism, Single Nucleotide; D012044:Regression Analysis; D012984:Software