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

Abstract

AbstractBackgroundDiscerning clinically relevant ASD candidate variants from whole-exome sequencing (WES) data is complex, time-consuming, and labor-intensive. To this end, we developedAutScore, an integrative prioritization algorithm of ASD candidate variants from WES data, and assessed its performance to detect clinically relevant variants.MethodsWe studied WES data from 581 ASD probands, and their parents registered in the Azrieli National Center database for Autism and Neurodevelopment Research. We focused on rare allele frequency <1%), high-quality proband-specific variants affecting genes associated with ASD or other neurodevelopmental disorders (NDDs). We assigned a score (i.e.,AutScore) to each such variant based on their pathogenicity, clinical relevance, gene-disease association, and inheritance patterns. Finally, we compared theAutScoreperformance with the rating of clinical experts and the NDD variants prioritization algorithm,AutoCasC.ResultsOverall, 1161 ultra-rare variants distributed in 687 genes in 441 ASD probands were evaluated byAutScorewith scores ranging from -4 to 25, with a mean ± SD of 5.89 ± 4.18.AutScorecut-off of ≥ 12 outperformsAutoCasCin detecting clinically relevant ASD variants, with a detection accuracy rate of 72.3% and an overall diagnostic yield of 11.9%. Sixteen variants withAutScoreof ≥ 12 were distributed in fifteen novel ASD genes.ConclusionAutScoreis an effective automated ranking system for ASD candidate variants that could be implemented in ASD clinical genetics pipelines.