The effect of algorithms on copy number variant detection

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...

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Bibliographic Details
Published in:PloS one 2010-12, Vol.5 (12), p.e14456-e14456
Main Authors: Tsuang, Debby W, Millard, Steven P, Ely, Benjamin, Chi, Peter, Wang, Kenneth, Raskind, Wendy H, Kim, Sulgi, Brkanac, Zoran, Yu, Chang-En
Format: Article
Language:English
Subjects:
Adult
Algorithms
Alzheimer's disease
Alzheimers disease
Analysis
Autism
Behavioral sciences
Case-Control Studies
Collaboration
Computational Biology/Genomics
Computational Biology/Molecular Genetics
Consortia
Copy number
Education
False Negative Reactions
False Positive Reactions
Female
Gene Dosage
Gene frequency
Genetic disorders
Genetics
Genetics and Genomics
Genetics and Genomics/Bioinformatics
Genetics and Genomics/Comparative Genomics
Genetics and Genomics/Complex Traits
Genetics and Genomics/Disease Models
Genetics and Genomics/Functional Genomics
Genetics and Genomics/Gene Discovery
Genetics and Genomics/Genetics of Disease
Genetics and Genomics/Genomics
Genomes
Genomics
Genotype
Genotyping
Humans
Hybridization
Male
Medical research
Medicine
Mental disorders
Middle Aged
Minority & ethnic groups
Mutation
Polymorphism, Single Nucleotide
Psychiatry
Reproducibility of Results
Schizophrenia
Schizophrenia - genetics
Sequence Analysis, DNA
Single-nucleotide polymorphism
Studies
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Summary: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. 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. 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.
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0014456