# Efficient identification of de novo mutations in family trios: a consensus-based informatic approach

## TL;DR

This paper introduces a reliable automated method for identifying new genetic mutations in family trios using a consensus-based approach, reducing the need for manual checks.

## Contribution

A novel consensus-based informatic workflow for high-precision de novo variant detection in genome sequencing trios.

## Key findings

- Consensus filtering achieved 98.0–99.4% precision in identifying de novo variants.
- The method reached 99.4% sensitivity when validated against manually confirmed variants.
- Validation on the Genome-in-a-Bottle trio showed 99.2% precision and 96.6% sensitivity.

## Abstract

Researchers developed a highly precise and accurate approach for identifying de novo genetic variants in probands from trio genome sequencing, making it suitable for automated large-scale analyses.

Accurate identification of de novo variants (DNVs) remains challenging despite advances in sequencing technologies, often requiring ad hoc filters and manual inspection. Here, we explored a purely informatic, consensus-based approach for identifying DNVs in proband–parent trios using short-read genome sequencing data. We evaluated variant calls generated by three sequence analysis pipelines—GATK HaplotypeCaller, DeepTrio, and Velsera GRAF—and examined the assumption that a requirement of consensus can serve as an effective filter for high-quality DNVs. Comparison with a highly accurate DNV set, validated previously by manual inspection and Sanger sequencing, demonstrated that consensus filtering, followed by a force-calling procedure, effectively removed false-positive calls, achieving 98.0–99.4% precision. At the same time, sensitivity of the workflow based on the previously established DNVs reached 99.4%. Validation in the HG002-3-4 Genome-in-a-Bottle trio confirmed its robustness, with precision reaching 99.2% and sensitivity up to 96.6%. We believe that this consensus approach can be widely implemented as an automated bioinformatics workflow suitable for large-scale analyses without the need for manual intervention, especially when very high precision is valued over sensitivity.