2023 ACMG Annual Clinical Genetics Meeting Digital Edition: Poster Gallery-Minimizing False Negatives in NGS-based Diagnostic Testing by Optimizing Variant Filtering Strategy

Pdf Summary

The study focuses on minimizing false negatives in next-generation sequencing (NGS) diagnostic testing. False negatives can occur due to variant filtering strategies, specifically the use of variant allele frequencies (AF) and maximum subpopulation AF (popmax AF) to filter out common variants. The study found that allele frequencies for certain variants vary significantly among different public population databases, potentially causing false negative results. <br /><br />The authors examined the gnomAD exome and genome datasets in disease genes and identified variants with differential AFs. They found that some pathogenic variants, which are relatively common in the population at risk, may be filtered out in the automatic filtering process. They suggested that relaxing AF cutoffs could help to address this issue. The study also evaluated the effectiveness of popular annotation tools ANNOVAR and VEP in considering database filter status and found that they do not take this into account.<br /><br />The authors proposed considering filter status during the annotation and filtering steps to reduce false-negative findings in NGS diagnostics. They also demonstrated that Sanger confirmation of homopolymers can be achieved without cloning and plasmid preparation.<br /><br />The study presented an example of a pathogenic variant mis-classified as benign in a patient with rod-cone dystrophy, developmental delay, overweight, bilateral clinodactyly, and brachydactyly. The authors highlighted the importance of considering the gnomAD variant flag status during the analysis.<br /><br />In conclusion, the study suggests that optimizing variant filtering strategies and considering filter status during annotation and filtering can help minimize false negatives in NGS-based diagnostic testing. Relaxing AF cutoffs and incorporating database filter status in annotation tools can improve the accuracy of variant calling.

Asset Subtitle

Presenting Author - Bin Guan, PhD, FACMG; Co-Author - Amelia Naik, BS; Co-Author - Ehsan Ullah, PhD; Co-Author - David McGaughey, PhD; Co-Author - Aime Agather, CGC; Co-Author - Brian P. Brooks, MD PhD; Co-Author - Robert B. Hufnagel, MD, PhD;

Meta Tag

Visual System

Variant Detection

Sequencing

Population Genetics

NextGen Sequencing

Methodology

Genomic Methodologies

Genome sequencing

Genetic Testing

Eye disorders

Exome sequencing

Databases

Bioinformatics

Co-Author Robert B. Hufnagel, MD, PhD

Co-Author Brian P. Brooks, MD PhD

Co-Author Aime Agather, CGC

Co-Author David McGaughey, PhD

Co-Author Ehsan Ullah, PhD

Co-Author Amelia Naik, BS

Presenting Author Bin Guan, PhD, FACMG

Keywords

Next-generation sequencing

NGS diagnostic testing

False negatives

Variant filtering strategies

Variant allele frequencies

Maximum subpopulation AF

Public population databases

gnomAD exome

gnomAD genome

Annotation tools