2023 ACMG Annual Clinical Genetics Meeting Digital Edition: Poster Gallery-Improved classification framework demonstrates many population predicted loss of function (pLoF) variants in genomic sequencing do not result in LoF

Improved classification framework demonstrates many population predicted loss of function (pLoF) variants in genomic sequencing do not result in LoF

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The study developed a framework to improve the interpretation of loss-of-function (LoF) variants in genetic sequencing data. They curated a set of 1,113 LoF variants in 22 autosomal recessive disease genes from the Genome Aggregation Database (gnomAD) and classified them as LoF, likely LoF, uncertain LoF, likely not LoF, or not LoF. The study found that curation resulted in LoF escape and potential technical artifacts in 27.3% of variants, with reasons including last exon, homopolymer, low pext, and splice rescue. Additionally, the study downgraded PVS1 (a classification criterion) in 99.4% of LoF-evading variants, resulting in a change in variant pathogenicity from likely pathogenic to variants of uncertain significance (VUS) in 71.4% of cases.<br /><br />Furthermore, the study compared curated pLoF variants with ClinVar entries and found that variants curated as likely not LoF were more likely to be classified as benign/likely benign compared to those curated as LoF/likely LoF. The results show that the advanced variant classification framework significantly reduces the false positive rate of predicted LoF variants in population sequencing data.<br /><br />The study emphasizes the importance of carefully interpreting pLoF variants beyond standard annotation pipelines to avoid overinterpreting their pathogenicity. The developed framework provides a means to adjust the PVS1 criteria based on the results of LoF interpretation. Overall, the study highlights the need for improved interpretation of pLoF variants in both research and clinical settings.

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Presenting Author - Moriel Singer-Berk, MS; Co-Author - Sanna Gudmundsson, PhD; Co-Author - Samantha Baxter, MS, CGC; Co-Author - Eleanor G. Seaby, MD; Co-Author - Eleina England, MS; Co-Author - Jordan C. Wood, BS; Co-Author - Rachel G. Son, BS; Co-Author - Nicholas Watts, BS; Co-Author - Konrad Karczewski, PhD; Co-Author - Steven M. Harrison, PhD, FACMG; Co-Author - Daniel MacArthur, PhD; Co-Author - Heidi L. Rehm, PhD; Co-Author - Anne O'Donnell-Luria, MD, PhD;

Meta Tag

Databases

Exome sequencing

Genetic Testing

Genome sequencing

Methodology

NextGen Sequencing

Population Genetics

Sequencing

Variant Detection

Co-Author Sanna Gudmundsson, PhD

Co-Author Samantha Baxter, MS, CGC

Co-Author Eleanor G. Seaby, MD

Co-Author Eleina England, MS

Co-Author Jordan C. Wood, BS

Co-Author Rachel G. Son, BS

Co-Author Nicholas Watts, BS

Co-Author Konrad Karczewski, PhD

Co-Author Steven M. Harrison, PhD, FACMG

Co-Author Daniel MacArthur, PhD

Co-Author Heidi L. Rehm, PhD

Co-Author Anne O'Donnell-Luria, MD, PhD

Presenting Author Moriel Singer-Berk, MS

Keywords

interpretation

loss-of-function variants

genetic sequencing data

LoF variants

framework

curated

autosomal recessive disease genes

Genome Aggregation Database

LoF escape

technical artifacts