Dataset Information

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.

SUBMITTER: Marenne G

PROVIDER: S-EPMC3230937 | biostudies-literature | 2011 Feb

REPOSITORIES: biostudies-literature

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

Marenne Gaëlle G Rodríguez-Santiago Benjamín B Closas Montserrat García MG Pérez-Jurado Luis L Rothman Nathaniel N Rico Daniel D Pita Guillermo G Pisano David G DG Kogevinas Manolis M Silverman Debra T DT Valencia Alfonso A Real Francisco X FX Chanock Stephen J SJ Génin Emmanuelle E Malats Núria N

Human mutation 20110125 2

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-depen ...[more]

PMID: 21089066

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Project description:BackgroundCopy Number Variations (CNVs) are usually inferred from Single Nucleotide Polymorphism (SNP) arrays by use of some software packages based on given algorithms. However, there is no clear understanding of the performance of these software packages; it is therefore difficult to select one or several software packages for CNV detection based on the SNP array platform.We selected four publicly available software packages designed for CNV calling from an Affymetrix SNP array, including Birdsuite, dChip, Genotyping Console (GTC) and PennCNV. The publicly available dataset generated by Array-based Comparative Genomic Hybridization (CGH), with a resolution of 24 million probes per sample, was considered to be the "gold standard". Compared with the CGH-based dataset, the success rate, average stability rate, sensitivity, consistence and reproducibility of these four software packages were assessed compared with the "gold standard". Specially, we also compared the efficiency of detecting CNVs simultaneously by two, three and all of the software packages with that by a single software package.ResultsSimply from the quantity of the detected CNVs, Birdsuite detected the most while GTC detected the least. We found that Birdsuite and dChip had obvious detecting bias. And GTC seemed to be inferior because of the least amount of CNVs it detected. Thereafter we investigated the detection consistency produced by one certain software package and the rest three software suits. We found that the consistency of dChip was the lowest while GTC was the highest. Compared with the CNVs detecting result of CGH, in the matching group, GTC called the most matching CNVs, PennCNV-Affy ranked second. In the non-overlapping group, GTC called the least CNVs. With regards to the reproducibility of CNV calling, larger CNVs were usually replicated better. PennCNV-Affy shows the best consistency while Birdsuite shows the poorest.ConclusionWe found that PennCNV outperformed the other three packages in the sensitivity and specificity of CNV calling. Obviously, each calling method had its own limitations and advantages for different data analysis. Therefore, the optimized calling methods might be identified using multiple algorithms to evaluate the concordance and discordance of SNP array-based CNV calling.

Project description:BACKGROUND: In recent years, genome-wide association studies have successfully uncovered single-nucleotide polymorphisms (SNPs) associated with complex traits such as diseases and quantitative phenotypes. These variations account for a small proportion of heritability. With the development of high throughput techniques, abundant submicroscopic structural variations have been found in organisms, of which the main variations are copy number variations (CNVs). Therefore, CNVs are increasingly recognized as an important and abundant source of genetic variation and phenotypic diversity. RESULTS: Analyses of CNVs in the genomes of three sheep breeds were performed using the Ovine SNP50 BeadChip array. A total of 238 CNV regions (CNVRs) were identified, including 219 losses, 13 gains, and six with both events (losses and gains), which cover 60.35 Mb of the sheep genomic sequence and correspond to 2.27% of the autosomal genome sequence. The length of the CNVRs on autosomes range from 13.66 kb to 1.30 Mb with a mean size of 253.57 kb, and 75 CNVRs events had a frequency > 3%. Among these CNVRs, 47 CNVRs identified by the PennCNV overlapped with the CNVpartition. Functional analysis indicated that most genes in the CNVRs were significantly enriched for involvement in the environmental response. Furthermore, 10 CNVRs were selected for validation and 6 CNVRs were further experimentally confirmed by qPCR. In addition, there were 57 CNVRs overlapped in our new dataset and other published ruminant CNV studies. CONCLUSIONS: In this study, we firstly constructed a sheep CNV map based on the Ovine SNP50 array. Our results demonstrated the differences of two detection tools and integration of multiple algorithms can enhance the detection of sheep genomic structure variations. Furthermore, our findings would be of help for understanding the sheep genome and provide preliminary foundation for carrying out the CNVs association studies with economically important phenotypes of sheep in the future.

Dataset Information

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

Publications

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

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