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2023 ACMG Annual Clinical Genetics Meeting Digital ...
Improved classification framework demonstrates man ...
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.
Asset Subtitle
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
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