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.

AutScore– An integrative scoring approach for prioritization of ultra-rare autism spectrum disorder candidate variants from whole exome sequencing data

“…182 ACMG/AMP [10] variant interpretation tool InterVar [14] and our in-house tool Psi-Variant 189 [28]. Although AutScore was initially designed to assess the ASD clinical relevance of rare 190 autosomal SNVs, it can be adapted for analyses of copy number variants (CNVs), 191 mitochondrial variants, and common heritable variants that are expected to enhance its applicability further.…”

“…3B). Comparing AutScore with AutoCasC Next, we compared the performance of AutScore (using the selected cut-off ≥ 12) vis-à-vis the existing NDD prioritization tool, AutoCasC, using its recommended cut-off of >6 [14], in detecting ASD candidate variants (i.e., likely, possibly) (Fig. 4).…”

“…We compared the performance of AutScore in detecting ASD candidate variants with the performance of AutoCasC [14], an existing variant prioritization tool for NDDs. The agreement between AutScore and AutoCasC scores, and between these scores and the clinical assessment ranking, were assessed using Pearson's correlation and Cohen's Kappa statistic, respectively.…”

“…Other factors usually considered are the segregation of the variant with the phenotype and existing evidence for the variant or gene association with the disease. To assist clinicians in this laborious process, several automated tools such as Exomiser [11], AMELIE [12], LIRICAL [13], AutoCasC [14], etc., have been devised to prioritize disease-specific variants (mainly single nucleotide variants [SNVs] and insertions/deletions [indels]) from WES/WGS data.…”

“…We applied the AutScore to WES data from 581 Israeli ASD-affected probands and their parents. We assessed its performance by comparing the obtained results to a manual and blinded evaluation of the variants by clinicians and to AutoCasC [14], an existing variant prioritization tool for neurodevelopmental disorders (NDDs).…”