title: The effect of algorithms on copy number variant detection.
abstract: BACKGROUND
  The detection of copy number variants (CNVs) and the results of CNV-disease association studies rely on how CNVs are defined, and because array-based technologies can only infer CNVs, CNV-calling algorithms can produce vastly different findings. Several authors have noted the large-scale variability between CNV-detection methods, as well as the substantial false positive and false negative rates associated with those methods. In this study, we use variations of four common algorithms for CNV detection (PennCNV, QuantiSNP, HMMSeg, and cnvPartition) and two definitions of overlap (any overlap and an overlap of at least 40% of the smaller CNV) to illustrate the effects of varying algorithms and definitions of overlap on CNV discovery.
  
  
  RESULTS
  We used a 56 K Illumina genotyping array enriched for CNV regions to generate hybridization intensities and allele frequencies for 48 Caucasian schizophrenia cases and 48 age-, ethnicity-, and gender-matched control subjects. No algorithm found a difference in CNV burden between the two groups. However, the total number of CNVs called ranged from 102 to 3,765 across algorithms. The mean CNV size ranged from 46 kb to 787 kb, and the average number of CNVs per subject ranged from 1 to 39. The number of novel CNVs not previously reported in normal subjects ranged from 0 to 212.
  
  
  CONCLUSIONS
  Motivated by the availability of multiple publicly available genome-wide SNP arrays, investigators are conducting numerous analyses to identify putative additional CNVs in complex genetic disorders. However, the number of CNVs identified in array-based studies, and whether these CNVs are novel or valid, will depend on the algorithm(s) used. Thus, given the variety of methods used, there will be many false positives and false negatives. Both guidelines for the identification of CNVs inferred from high-density arrays and the establishment of a gold standard for validation of CNVs are needed.
authors.affiliation: Department of Psychiatry and Behavioral Sciences, University of Washington, Seattle, Washington, United States of America. dwt1@uw.edu
authors.forename: Debby W
authors.initials: DW
authors.lastname: Tsuang
authors.forename: Steven P
authors.initials: SP
authors.lastname: Millard
authors.forename: Benjamin
authors.initials: B
authors.lastname: Ely
authors.forename: Peter
authors.initials: P
authors.lastname: Chi
authors.forename: Kenneth
authors.initials: K
authors.lastname: Wang
authors.forename: Wendy H
authors.initials: WH
authors.lastname: Raskind
authors.forename: Sulgi
authors.initials: S
authors.lastname: Kim
authors.forename: Zoran
authors.initials: Z
authors.lastname: Brkanac
authors.forename: Chang-En
authors.initials: CE
authors.lastname: Yu
mesh_terms: D000328:Adult; D000465:Algorithms; D016022:Case-Control Studies; D005188:False Negative Reactions; D005189:False Positive Reactions; D005260:Female; D018628:Gene Dosage; D005838:Genotype; D006801:Humans; D008297:Male; D008875:Middle Aged; D020641:Polymorphism, Single Nucleotide; D015203:Reproducibility of Results; D012559:Schizophrenia / Q000235:genetics*; D017422:Sequence Analysis, DNA