title: Comorbid Analysis of Genes Associated with Autism Spectrum Disorders Reveals Differential Evolutionary Constraints.
abstract: The burden of comorbidity in Autism Spectrum Disorder (ASD) is substantial. The symptoms of autism overlap with many other human conditions, reflecting common molecular pathologies suggesting that cross-disorder analysis will help prioritize autism gene candidates. Genes in the intersection between autism and related conditions may represent nonspecific indicators of dysregulation while genes unique to autism may play a more causal role. Thorough literature review allowed us to extract 125 ICD-9 codes comorbid to ASD that we mapped to 30 specific human disorders. In the present work, we performed an automated extraction of genes associated with ASD and its comorbid disorders, and found 1031 genes involved in ASD, among which 262 are involved in ASD only, with the remaining 779 involved in ASD and at least one comorbid disorder. A pathway analysis revealed 13 pathways not involved in any other comorbid disorders and therefore unique to ASD, all associated with basal cellular functions. These pathways differ from the pathways associated with both ASD and its comorbid conditions, with the latter being more specific to neural function. To determine whether the sequence of these genes have been subjected to differential evolutionary constraints, we studied long term constraints by looking into Genomic Evolutionary Rate Profiling, and showed that genes involved in several comorbid disorders seem to have undergone more purifying selection than the genes involved in ASD only. This result was corroborated by a higher dN/dS ratio for genes unique to ASD as compare to those that are shared between ASD and its comorbid disorders. Short-term evolutionary constraints showed the same trend as the pN/pS ratio indicates that genes unique to ASD were under significantly less evolutionary constraint than the genes associated with all other disorders.
authors.affiliation: Department of Pediatrics, Division of Systems Medicine, Stanford University, Stanford, California, United States of America.
authors.forename: Maude M
authors.initials: MM
authors.lastname: David
authors.affiliation: Department of Biology, Stanford University, Stanford, California, United States of America.
authors.forename: David
authors.initials: D
authors.lastname: Enard
authors.affiliation: Department of Pediatrics, Division of Systems Medicine, Stanford University, Stanford, California, United States of America.
authors.forename: Alp
authors.initials: A
authors.lastname: Ozturk
authors.affiliation: Department of Pediatrics, Division of Systems Medicine, Stanford University, Stanford, California, United States of America.
authors.forename: Jena
authors.initials: J
authors.lastname: Daniels
authors.affiliation: Department of Pediatrics, Division of Systems Medicine, Stanford University, Stanford, California, United States of America.
authors.forename: Jae-Yoon
authors.initials: JY
authors.lastname: Jung
authors.affiliation: Department of Pediatrics, Division of Systems Medicine, Stanford University, Stanford, California, United States of America.
authors.forename: Leticia
authors.initials: L
authors.lastname: Diaz-Beltran
authors.affiliation: Department of Pediatrics, Division of Systems Medicine, Stanford University, Stanford, California, United States of America.
authors.forename: Dennis P
authors.initials: DP
authors.lastname: Wall
mesh_terms: D000067877:Autism Spectrum Disorder / Q000150:complications* / Q000235:genetics*; D016000:Cluster Analysis; D030541:Databases, Genetic; D006801:Humans; D038801:International Classification of Diseases