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Genome-wide array comparative genomic hybridization (aCGH) profiling of copy number variations (CNVs) in various dog breeds in comparison to a reference sample

ABSTRACT: This study used the NimbleGen dog whole genome CGH 2.1M tiling array to assay copy number variants in the dog genome in multiple breeds and wolf.

ORGANISM(S): Canis lupus familiaris Canis lupus

PROVIDER: GSE40210 | GEO | 2012/08/21

SECONDARY ACCESSION(S): PRJNA173205

REPOSITORIES: GEO

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Genome-wide array comparative genomic hybridization (aCGH) profiling of copy number variations (CNVs) in various dog breeds in comparison to a reference sample

Project description:This study used the NimbleGen dog whole genome CGH 2.1M tiling array to assay copy number variants in the dog genome in multiple breeds and wolf. 53 samples of genomic DNA were hybridized to a reference sample. The dataset comprises 2 samples from each of 15 dog breeds, 10 samples from each of 2 dog breeds and 3 samples from gray wolf.

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Genome-wide array comparative genomic hybridization (aCGH) profiling of copy number variations (CNVs) in various dog breeds in comparison to a reference sample

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Project description:BackgroundDNA copy number variations (CNV) constitute an important source of genetic variability. The standard method used for CNV detection is array comparative genomic hybridization (aCGH).ResultsWe propose a novel multiple sample aCGH analysis methodology aiming in rare CNVs detection. In contrast to the majority of previous approaches, which deal with cancer datasets, we focus on constitutional genomic abnormalities identified in a diverse spectrum of diseases in human. Our method is tested on exon targeted aCGH array of 366 patients affected with developmental delay/intellectual disability, epilepsy, or autism. The proposed algorithms can be applied as a post-processing filtering to any given segmentation method.ConclusionsThanks to the additional information obtained from multiple samples, we could efficiently detect significant segments corresponding to rare CNVs responsible for pathogenic changes. The robust statistical framework applied in our method enables to eliminate the influence of widespread technical artifact termed 'waves'.

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