Assessment of copy number variation using the Illumina Infinium 1M SNP-array: a comparison of methodological approaches in the Spanish Bladder Cancer/EPICURO study

Abstract

High-throughput single nucleotide polymorphism (SNP)-array technologies allow to investigate copy number variants (CNVs) in genome-wide scans and specific calling algorithms have been developed to determine CNV location and copy number. We report the results of a reliability analysis comparing data from 96 pairs of samples processed with CNVpartition, PennCNV, and QuantiSNP for Infinium Illumina Human 1Million probe chip data. We also performed a validity assessment with multiplex ligation-dependent probe amplification (MLPA) as a reference standard. The number of CNVs per individual varied according to the calling algorithm. Higher numbers of CNVs were detected in saliva than in blood DNA samples regardless of the algorithm used. All algorithms presented low agreement with mean Kappa Index (KI) <66. PennCNV was the most reliable algorithm (KI(w=) 98.96) when assessing the number of copies. The agreement observed in detecting CNV was higher in blood than in saliva samples. When comparing to MLPA, all algorithms identified poorly known copy aberrations (sensitivity = 0.19-0.28). In contrast, specificity was very high (0.97-0.99). Once a CNV was detected, the number of copies was truly assessed (sensitivity >0.62). Our results indicate that the current calling algorithms should be improved for high performance CNV analysis in genome-wide scans. Further refinement is required to assess CNVs as risk factors in complex diseases.

Figure 1

Box plots of the distribution of kappa index estimates comparing duplicated pairs for: A: the SNP callings, B: the detection of CNVs according to the different algorithms, and C: the number of copies assigned by the different algorithms in the regions where a CNV was detected.

Figure 2

Box plots of the distribution of kappa indexes comparing the callings on duplicated samples by the different algorithms depending on the source of DNA.

Figure 3

Average Kappa Index for the agreement in detecting CNVs (first row) and median number of CNVs across the 92 individuals (second row) for each algorithm while filtering the called CNVs according the number of probes in the CNV (first column) and the length of the CNV (second column).

Figure 4

Sensitivity (SE) and Specificity (SP) estimates for the presence and for the type-specific CNV according to each algorithm.