Assessment of copy number variation using the Illumina Infinium 1M SNP-array: a comparison of methodological approaches in the Spanish Bladder Cancer/EPICURO study
✍ Scribed by Gaëlle Marenne; Benjamín Rodríguez-Santiago; Montserrat García Closas; Luis Pérez-Jurado; Nathaniel Rothman; Daniel Rico; Guillermo Pita; David G. Pisano; Manolis Kogevinas; Debra T. Silverman; Alfonso Valencia; Francisco X. Real; Stephen J. Chanock; Emmanuelle Génin; Núria Malats
- Book ID
- 102259703
- Publisher
- John Wiley and Sons
- Year
- 2011
- Tongue
- English
- Weight
- 242 KB
- Volume
- 32
- Category
- Article
- ISSN
- 1059-7794
No coin nor oath required. For personal study only.
✦ Synopsis
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) o66. PennCNV was the most reliable algorithm (KI w 5 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 5 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 40.62). Our results indicate that the current calling algorithms should be improved for high performance CNV analysis in genomewide scans. Further refinement is required to assess CNVs as risk factors in complex diseases.