Outcome of array CGH analysis for 255 subjects with intellectual disability and search for candidate genes using bioinformatics.
Array CGH enables the detection of pathogenic copy number variants (CNVs) in 5-15% of individuals with intellectual disability (ID), making it a promising tool for uncovering ID candidate genes. However, most CNVs encompass multiple genes, making it difficult to identify key disease gene(s) underlying ID etiology. Using array CGH we identified 47 previously unreported unique CNVs in 45/255 probands. We prioritized ID candidate genes using five bioinformatic gene prioritization web tools. Gene priority lists were created by comparing integral genes from each CNV from our ID cohort with sets of training genes specific either to ID or randomly selected. Our findings suggest that different training sets alter gene prioritization only moderately; however, only the ID gene training set resulted in significant enrichment of genes with nervous system function (19%) in prioritized versus non-prioritized genes from the same de novo CNVs (7%, p < 0.05). This enrichment further increased to 31% when the five web tools were used in concert and included genes within mitogen-activated protein kinase (MAPK) and neuroactive ligand-receptor interaction pathways. Gene prioritization web tools enrich for genes with relevant function in ID and more readily facilitate the selection of ID candidate genes for functional studies, particularly for large CNVs.
Qiao
Y
Y
Department of Pathology (Cytogenetics), Child and Family Research Institute, University of British Columbia (UBC), 950 West 28th, Room 3060, Vancouver, BC, V5Z 4H4, Canada.
Harvard
C
C
Tyson
C
C
Liu
X
X
Fawcett
C
C
Pavlidis
P
P
Holden
J J A
JJ
Lewis
M E S
ME
Rajcan-Separovic
E
E
D055028:Comparative Genomic Hybridization / Q000379:methods*; D019295:Computational Biology; D005796:Genes; D006801:Humans; D008607:Intellectual Disability / Q000235:genetics*