Comparison of three bioinformatics tools in the detection of ASD candidate variants from whole exome sequencing data - Apurba Shil; Liron Levin; Hava Golan; Gal Meiri; Analya Michaelovski; Yair Sadaka; Adi Aran; Ilan Dinstein; Idan Menashe (Scientific Reports, 2023)
Abstract Autism spectrum disorder (ASD) is a heterogenous multifactorial neurodevelopmental condition with a significant genetic susceptibility component. Thus, identifying genetic variations associated with ASD is a complex task. Whole-exome sequencing (WES) is an effective approach for detecting extremely rare protein-coding single-nucleotide variants (SNVs) and short insertions/deletions (INDELs). However, interpreting these variants' functional and clinical consequences requires integrating multifaceted genomic information. We compared the concordance and effectiveness of three bioinformatics tools in detecting ASD candidate variants (SNVs and short INDELs) from WES data of 220 ASD family trios registered in the National Autism Database of Israel. We studied only rare (< 1% population frequency) proband-specific variants. According to the American College of Medical Genetics (ACMG) guidelines, the pathogenicity of variants was evaluated by the InterVar and TAPES tools. In addition, likely gene-disrupting (LGD) variants were detected based on an in-house bioinformatics tool, Psi-Variant, that integrates results from seven in-silico prediction tools. Overall, 372 variants in 311 genes distributed in 168 probands were detected by these tools. The overlap between the tools was 64.1, 22.9, and 23.1% for InterVar–TAPES, InterVar–Psi-Variant, and TAPES–Psi-Variant, respectively. The intersection between InterVar and Psi-Variant (I ∩ P) was the most effective approach in detecting variants in known ASD genes (PPV = 0.274; OR = 7.09, 95% CI = 3.92–12.22), while the union of InterVar and Psi Variant (I U P) achieved the highest diagnostic yield (20.5%).Our results suggest that integrating different variant interpretation approaches in detecting ASD candidate variants from WES data is superior to each approach alone. The inclusion of additional criteria could further improve the detection of ASD candidate variants.