Array-comparative genomic hybridization (array-CGH) is a widely used technique to detect Copy Number Variants (CNVs) associated with developmental delay/intellectual disability (DD/ID). We performed a comprehensive array-CGH investigation of 1,015 consecutive cases with DD/ID and combined literature mining, genetic evidence, evolutionary constraint scores, and functional information in order to assess the pathogenicity of the CNVs. We identified non-benign CNVs in 29% of patients. Amongst the pathogenic variants (11%), detected with a yield consistent with the literature, we found rare genomic disorders and CNVs spanning known disease genes. We further identified and discussed 51 cases with likely pathogenic CNVs spanning novel candidate genes, including genes encoding synaptic components and/or proteins involved in corticogenesis. Additionally, we identified two deletions spanning potential Topological Associated Domain (TAD) boundaries likely affecting the regulatory landscape. In conclusion, we show how phenotypic and genetic analyses of array-CGH data allow unraveling complex cases, identifying rare disease genes, and revealing unexpected position effects.

CNVs analysis in a cohort of isolated and syndromic DD/ID reveals novel genomic disorders, position effects and candidate disease genes

ALA, Ugo;GIORDANO, Mara;ZONTA, ANDREA;PROVERO, Paolo;GROSSO, Enrico;PASINI, BARBARA;
2017-01-01

Abstract

Array-comparative genomic hybridization (array-CGH) is a widely used technique to detect Copy Number Variants (CNVs) associated with developmental delay/intellectual disability (DD/ID). We performed a comprehensive array-CGH investigation of 1,015 consecutive cases with DD/ID and combined literature mining, genetic evidence, evolutionary constraint scores, and functional information in order to assess the pathogenicity of the CNVs. We identified non-benign CNVs in 29% of patients. Amongst the pathogenic variants (11%), detected with a yield consistent with the literature, we found rare genomic disorders and CNVs spanning known disease genes. We further identified and discussed 51 cases with likely pathogenic CNVs spanning novel candidate genes, including genes encoding synaptic components and/or proteins involved in corticogenesis. Additionally, we identified two deletions spanning potential Topological Associated Domain (TAD) boundaries likely affecting the regulatory landscape. In conclusion, we show how phenotypic and genetic analyses of array-CGH data allow unraveling complex cases, identifying rare disease genes, and revealing unexpected position effects.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11579/85214
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