| Literature DB >> 31038196 |
Daniel Benjamin Callaghan1,2, Sanja Rogic1,2, Powell Patrick Cheng Tan1,2, Kristina Calli3, Ying Qiao3, Robert Baldwin4, Matthew Jacobson1,2, Manuel Belmadani1,2, Nathan Holmes1,2, Chang Yu5, Yanchen Li5, Yingrui Li5, Franz-Edward Kurtzke3, Boris Kuzeljevic6, An Yi Yu3, Melissa Hudson7,8, Amy J M Mcaughton7,8, Yuchen Xu4, Alexandre Dionne-Laporte9,10, Simon Girard11, Ping Liang4, Evica Rajcan Separovic12, Xudong Liu7,8, Guy Rouleau9,10, Paul Pavlidis1,2, M E Suzanne Lewis3.
Abstract
Autism spectrum disorder (ASD) is a highly heterogeneous genetic disorder with strong evidence of ASD-association currently available only for a small number of genes. This makes it challenging to identify the underlying genetic cause in many cases of ASD, and there is a continuing need for further discovery efforts. We sequenced whole genomes of 119 deeply phenotyped ASD probands in order to identify likely pathogenic variants. We prioritized variants found in each subject by predicted damage, population frequency, literature evidence, and phenotype concordance. We used Sanger sequencing to determine the inheritance status of high-priority variants where possible. We report five novel de novo damaging variants as well as several likely damaging variants of unknown inheritance; these include two novel de novo variants in the well-established ASD gene SCN2A. The availability of rich phenotypic information and its concordance with the literature allowed us to increase our confidence in pathogenicity of discovered variants, especially in probands without parental DNA. Our results contribute to the documentation of potential pathogenic variants and their associated phenotypes in individuals with ASD.Entities:
Keywords: zzm321990de novo variant; autism spectrum disorder (ASD); deep phenotyping; likely gene damaging (LGD) variant; phenotype clustering; single nucleotide variant (SNV); variant prioritization; whole genome sequencing
Year: 2019 PMID: 31038196 DOI: 10.1111/cge.13556
Source DB: PubMed Journal: Clin Genet ISSN: 0009-9163 Impact factor: 4.438