bioRxivGeneticsPreprint
Phenotypic analysis of 11,125 trio exomes in neurodevelopmental disorders
Shiva GanesaniD, Sarah McKeown RuggieroiD, Shridhar ParthasarathyiD, Peter D. GaleriD, David Lewis‐SmithiD, Ian McSalleyiD, Stacey R. CoheniD, Laina G. LuskiD, Anna Jenne PrenticeiD, Jillian L. McKeeiD, Manuela PendziwiatiD, Lacey A. SmithiD, Yvonne G. WeberiD, Heather C. MeffordiD, Annapurna PoduriiD, Ingo HelbigiD
Abstract
Genomic sequencing is widely used to identify causative genetic changes in neurodevelopmental disorders, such as autism, intellectual disability, and epilepsy. Most neurodevelopmental disorders also present with diverse clinical features, and delineating the interaction between causative genetic changes and phenotypic features is a key prerequisite for developing personalized therapies. However, assessing clinical features at a scale that parallels genomic sequencing remains challenging. Here, we standardize phenotypic information across 11,125 patient-parent trios with exome sequencing data using biomedical ontologies, analyzing 674,767 phenotypic terms. We find that individuals with de novo variants in 69 out of 261 neurodevelopmental genes exhibit statistically significant clinical similarities with distinct phenotypic fingerprints. We also observe that phenotypic relatedness follows a gradient, spanning from highly similar to dissimilar phenotypes, with intra-gene similarities suggesting clinically distinct subgroups for seven neurodevelopmental genes. For most genetic etiologies, only a small subset of highly phenotypically similar individuals carried de novo variants in the same gene, highlighting the heterogeneous and complex clinical landscape of neurodevelopmental disorders. Our study provides a large-scale overview of the dynamic relationship between genotypes and phenotypes in neurodevelopmental disorders, underscoring how the inherent complexity of these conditions can be deciphered through approaches that integrate genomic and phenotypic data.
- organism: human
- assay: whole-exome sequencing
- research focus: differential expression
- research focus: gene regulatory network inference
- approach: statistical model
- Citations
- 2
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