Assessment of algorithms for high throughput detection of genomic copy number variation in oligonucleotide microarray data

Baross, Ágnes; Delaney, Allen; Li, H. Irene; Nayar, Tarun; Flibotte, Stéphane; Qian, Hong; Chan, Susanna; Asano, Jennifer; Ally, Adrian; Cao, Manqiu; Birch, Patricia; Brown-John, Mabel; Fernandes, Nicole; Go, Anne; Kennedy, Giulia C.; Langlois, Sylvie; Eydoux, Patrice; Friedman, Jan M.; Marra, Marco A.

doi:10.1186/1471-2105-8-368

Cited by 53 publications

(61 citation statements)

References 27 publications

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“…Precise mapping of CNVs is important when determining overlap with CNV in parents and control cohorts, as well as for determining the functional content in these regions and performing detailed genotype-phenotype correlations. Our results and those of others [Baross et al, 2007;Friedman et al, 2006;Pinto et al, 2007] indicate that further optimization of automatic copy number detection algorithms is required for optimal CNV identification and characterization via genome wide microarray platforms. At present, we suggest performing CNV identification with two software packages and/or to include the gene content of the surrounding 100 kb for clinical/molecular interpretation.…”

Section: Discussionsupporting

confidence: 55%

Molecular karyotyping of patients with unexplained mental retardation by SNP arrays: A multicenter study

et al. 2009

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Genomic microarrays have been implemented in the diagnosis of patients with unexplained mental retardation. This method, although revolutionizing cytogenetics, is still limited to the detection of rare de novo copy number variants (CNVs). Genome-wide single nucleotide polymorphism (SNP) microarrays provide high-resolution genotype as well as CNV information in a single experiment. We hypothesize that the widespread use of these microarray platforms can be exploited to greatly improve our understanding of the genetic causes of mental retardation and many other common disorders, while already providing a robust platform for routine diagnostics. Here we report a detailed validation of Affymetrix 500k SNP microarrays for the detection of CNVs associated to mental retardation. After this validation we applied the same platform in a multicenter study to test a total of 120 patients with unexplained mental retardation and their parents. Rare de novo CNVs were identified in 15% of cases, showing the importance of this approach in daily clinical practice. In addition, much more genomic variation was observed in these patients as well as their parents. We provide all of these data for the scientific community to jointly enhance our understanding of these genomic variants and their potential role in this common disorder.

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Section: Discussionsupporting

confidence: 55%

Molecular karyotyping of patients with unexplained mental retardation by SNP arrays: A multicenter study

et al. 2009

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“…Twelve studies were identified since the publication of our previous meta-analysis. [21][22][23][24][25][26][27][28][29][30][31][32] Seven studies were conducted on patients based in the United States, [21][22][23]25,28,31,32 seven in Europe, 6,15,17,18,24,29,30 four using patients from multiple sources based in North America, South America, or Europe 19,20,26,27 and one in Japan. 16 All studies included sampling of control DNA as part of their protocol.…”

Section: Study Characteristicsmentioning

confidence: 99%

“…16 All studies included sampling of control DNA as part of their protocol. Seven studies used a 1 Mb array for investigating the whole genome, 6,15,18 -20,22,29 four used a targeted array, 16,26,27,31 three used an oligonucleotide array with 30 -35 k resolution, 21,25,28 two studies used a 100-k array, 23,30 one used a tiling BAC array, 17 one study used both a targeted array and a 1-Mb array, 32 and another used a 1-Mb array supplemented with an additional 3000 gene and region-specific BAC clones increasing the resolution to 0.5 Mb. 24 Control samples varied from 2 to 316 normal people, whereas Menten et al 15 used samples from other patients in the cohort as controls.…”

Section: Study Characteristicsmentioning

confidence: 99%

Array CGH in patients with learning disability (mental retardation) and congenital anomalies: updated systematic review and meta-analysis of 19 studies and 13,926 subjects

Sagoo

Butterworth

Sanderson

et al. 2009

Genetics in Medicine

194

133

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Array-based comparative genomic hybridization is being increasingly used in patients with learning disability (mental retardation) and congenital anomalies. In this article, we update our previous meta-analysis evaluating the diagnostic and false-positive yields of this technology. An updated systematic review and meta-analysis was conducted investigating patients with learning disability and congenital anomalies in whom conventional cytogenetic analyses have proven negative. Nineteen studies (13,926 patients) were included of which 12 studies (13,464 patients) were published since our previous analysis. The overall diagnostic yield of causal abnormalities was 10% (95% confidence interval: 8 -12%). The overall number needed to test to identify an extra causal abnormality was 10 (95% confidence interval: 8 -13). The overall false-positive yield of noncausal abnormalities was 7% (95% confidence interval: 5-10%). This updated meta-analysis provides new evidence to support the use of array-based comparative genomic hybridization in investigating patients with learning disability and congenital anomalies in whom conventional cytogenetic tests have proven negative. However, given that this technology also identifies false positives at a similar rate to causal variants, caution in clinical practice should be advised. Genet Med 2009:11(3):139 -146.

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“…While overlap between different algorithms applied to the same sample is usually low (Redon et al, 2006), it should be noted that CNVs called by multiple algorithms are very likely to be true positives (Pinto et al, 2007;Marshall et al, 2008). Unfortunately, little has been published on direct comparisons of different CNV calling algorithms/ analyses (Baross et al, 2007;Greshock et al, 2007). It is also evident that the size, frequency, and type of CNV (gains vs. losses) are all detectable with variable power by different methods.…”

Section: Variability In Cnv 'Control' Studiesmentioning

confidence: 99%

Germline copy number variation in control populations

Al-Sukhni

Gallinger²

2008

Cytogenet Genome Res

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Copy number variation (CNV) is an important source of genomic variation. Characterizing CNVs in phenotypically normal individuals is important for understanding the functional significance of these variants. Many studies have reported CNVs in control populations, but wide variability is observed in their design and outcome. Importantly, medical and phenotypic information for control populations must be carefully documented, and accurate genotyping will be necessary to determine the population genetics of CNVs. Despite existing challenges in studying this class of variants, it is evident that CNVs are ubiquitous in human genomes, with non-random distribution, and they affect thousands of coding regions, potentially contributing to human disease and phenotypic variability. Higher-resolution detection platforms and improved algorithms will further define our understanding of CNVs in control populations, leading to development of effective disease-association studies.

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Assessment of algorithms for high throughput detection of genomic copy number variation in oligonucleotide microarray data

Cited by 53 publications

References 27 publications

Molecular karyotyping of patients with unexplained mental retardation by SNP arrays: A multicenter study

Molecular karyotyping of patients with unexplained mental retardation by SNP arrays: A multicenter study

Array CGH in patients with learning disability (mental retardation) and congenital anomalies: updated systematic review and meta-analysis of 19 studies and 13,926 subjects

Germline copy number variation in control populations

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