Outcome of array CGH analysis for 255 subjects with intellectual disability and search for candidate genes using bioinformatics

• Published in: Human genetics Vol. 128; no. 2; pp. 179 - 194
• Main Authors: Qiao, Y, Harvard, C, Tyson, C, Liu, X, Fawcett, C, Pavlidis, P, Holden, J. J. A, Lewis, M. E. S, Rajcan-Separovic, E
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Berlin/Heidelberg : Springer-Verlag 01.08.2010; Springer-Verlag; Springer; Springer Nature B.V
• Subjects: Adult and adolescent clinical studies; Bioinformatics; Biological and medical sciences; Biomedical and Life Sciences; Biomedicine; Classical genetics, quantitative genetics, hybrids; Comparative Genomic Hybridization - methods; Computational Biology; Cytogenetics; Datasets; Disease; Fundamental and applied biological sciences. Psychology; Gene Function; General aspects; Genes; Genetics; Genetics of eukaryotes. Biological and molecular evolution; Genomes; Genotype & phenotype; Human; Human Genetics; Humans; Intellectual deficiency; Intellectual disabilities; Intellectual Disability - genetics; Internet software; Kinases; Laboratories; Mathematics in biology. Statistical analysis. Models. Metrology. Data processing in biology (general aspects); Medical sciences; Metabolic Diseases; Molecular Medicine; Original Investigation; Proteins; Psychology. Psychoanalysis. Psychiatry; Psychopathology. Psychiatry; Canada; Mouse Genome Informatics; Intellectual Disability; Priority List; Gene Prioritization; Handicap; Intellectual deficiency; Genetics; Developmental disorder; Candidate gene; Mental retardation; Bioinformatics
• DOI: 10.1007/s00439-010-0837-0
• ISSN: 0340-6717; 1432-1203

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.