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Advanced variant classification framework reduces the false positive rate of predicted loss-of-function variants in population sequencing data
Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA; Center for Genomic Medicine & Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA.
KTH, Centres, Science for Life Laboratory, SciLifeLab. KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Gene Technology. Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA; Center for Genomic Medicine & Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA; Division of Genetics and Genomics, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA.ORCID iD: 0000-0002-2332-074X
Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA; Center for Genomic Medicine & Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA.
Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA; Center for Genomic Medicine & Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA; Division of Genetics and Genomics, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA; Genomic Informatics Group, University Hospital Southampton, Southampton, UK.
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2023 (English)In: American Journal of Human Genetics, ISSN 0002-9297, E-ISSN 1537-6605, Vol. 110, no 9, p. 1496-1508Article in journal (Refereed) Published
Abstract [en]

Predicted loss of function (pLoF) variants are often highly deleterious and play an important role in disease biology, but many pLoF variants may not result in loss of function (LoF). Here we present a framework that advances interpretation of pLoF variants in research and clinical settings by considering three categories of LoF evasion: (1) predicted rescue by secondary sequence properties, (2) uncertain biological relevance, and (3) potential technical artifacts. We also provide recommendations on adjustments to ACMG/AMP guidelines’ PVS1 criterion. Applying this framework to all high-confidence pLoF variants in 22 genes associated with autosomal-recessive disease from the Genome Aggregation Database (gnomAD v.2.1.1) revealed predicted LoF evasion or potential artifacts in 27.3% (304/1,113) of variants. The major reasons were location in the last exon, in a homopolymer repeat, in a low proportion expressed across transcripts (pext) scored region, or the presence of cryptic in-frame splice rescues. Variants predicted to evade LoF or to be potential artifacts were enriched for ClinVar benign variants. PVS1 was downgraded in 99.4% (162/163) of pLoF variants predicted as likely not LoF/not LoF, with 17.2% (28/163) downgraded as a result of our framework, adding to previous guidelines. Variant pathogenicity was affected (mostly from likely pathogenic to VUS) in 20 (71.4%) of these 28 variants. This framework guides assessment of pLoF variants beyond standard annotation pipelines and substantially reduces false positive rates, which is key to ensure accurate LoF variant prediction in both a research and clinical setting.

Place, publisher, year, edition, pages
Elsevier BV , 2023. Vol. 110, no 9, p. 1496-1508
Keywords [en]
gnomAD, pLoF, population data, predicted loss-of-function variants, PVS1, variant classification, variant interpretation
National Category
Medical Genetics and Genomics
Identifiers
URN: urn:nbn:se:kth:diva-336291DOI: 10.1016/j.ajhg.2023.08.005ISI: 001075024300001PubMedID: 37633279Scopus ID: 2-s2.0-85169840058OAI: oai:DiVA.org:kth-336291DiVA, id: diva2:1796630
Note

QC 20230913

Available from: 2023-09-13 Created: 2023-09-13 Last updated: 2025-02-10Bibliographically approved

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Gudmundsson, Sanna

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