# Assessment of In‐Frame Indel Variants in an Unsolved Cohort of Inherited Retinal Diseases Using Machine Learning

## TL;DR

This study evaluates machine learning tools to predict the impact of in-frame indel mutations in inherited retinal diseases, finding that one tool can reliably identify harmful variants.

## Contribution

The study introduces the use of MetaRNN-indel for reliable pathogenicity prediction of in-frame indels in unsolved inherited retinal disease cases.

## Key findings

- MetaRNN-indel outperformed other ML tools in predicting pathogenic in-frame indels.
- Two likely pathogenic variants were identified in unrelated inherited retinal disease patients.
- Proper evaluation and tuning of ML tools can reliably assess in-frame indel pathogenicity.

## Abstract

The standard for in silico pathogenicity prediction of in‐frame insertions and deletions (indels) is less established compared to other types of variations. We aimed to systematically assess the performance of in silico machine learning (ML) tools on a patient cohort with inherited retinal diseases (IRDs). The performance of four ML tools (CADD, FATHMM‐indel, VEST4, and MetaRNN‐indel) was compared. Among them, MetaRNN‐indel showed the best overall results. MetaRNN‐indel was then applied to 1013 unsolved IRD patients, identifying two likely pathogenic causal variants in two unrelated IRD patients by confirming clinical phenotypes. Hence, our findings indicate that reliable prediction of the pathogenicity of in‐frame indels can be achieved using existing ML tools with proper evaluation and tuning.