AutoCaSc: Prioritizing candidate genes for neurodevelopmental disorders

Abstract

Routine exome sequencing (ES) in individuals with neurodevelopmental disorders (NDD) remains inconclusive in >50%. Research analysis of unsolved cases can identify novel candidate genes but is time consuming, subjective, and hard to compare between labs. The field therefore needs automated and standardized assessment methods to prioritize candidates for matchmaking. We developed AutoCaSc (https://autocasc.uni-leipzig.de) based on our candidate scoring scheme (CaSc). We validated our approach using synthetic trios and real in-house trio ES data. AutoCaSc consistently (94.5%) scored variants in valid novel NDD genes in the top three ranks. In 93 real trio exomes, AutoCaSc identified most (97.5%) previously manually scored variants while evaluating additional highly scoring variants missed in manual evaluation. It identified candidate variants in previously undescribed NDD candidate genes ( CNTN2, DLGAP1, SMURF1, NRXN3, PRICKLE1). AutoCaSc enables anybody to quickly screen a variant for its plausibility in NDD. After contributing >40 descriptions of NDD associated genes, we provide usage recommendations based on our extensive experience. Our implementation is capable of pipeline integration and therefore allows screening of large cohorts for candidate genes. AutoCaSc empowers even small labs to a standardized matchmaking collaboration and to contribute to the ongoing identification of novel NDD entities.