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LoFTK: a framework for fully automated calculation of predicted Loss-of-Function variants and genes.
Alasiri, Abdulrahman; Karczewski, Konrad J; Cole, Brian; Loza, Bao-Li; Moore, Jason H; van der Laan, Sander W; Asselbergs, Folkert W; Keating, Brendan J; van Setten, Jessica.
Afiliação
  • Alasiri A; Department of Cardiology, Division Heart and Lungs, University Medical Center Utrecht, University of Utrecht, Heidelberglaan 100, 3584 CX, Utrecht, Netherlands.
  • Karczewski KJ; Medical Genomics Research Department, King Abdullah International Medical Research Center, King Saud Bin Abdulaziz University for Health Sciences, Ministry of National Guard Health Affairs, Riyadh, Saudi Arabia.
  • Cole B; Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
  • Loza BL; Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA.
  • Moore JH; Bioinformatics Core, Harvard Medical School, Boston, MA, USA.
  • van der Laan SW; Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
  • Asselbergs FW; Department of Computational Biomedicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
  • Keating BJ; Central Diagnostic Laboratory, Division Laboratories, Pharmacy, and Biomedical Genetics, University Medical Center Utrecht, University of Utrecht, Utrecht, Netherlands.
  • van Setten J; Department of Cardiology, Amsterdam University Medical Centers, University of Amsterdam, Amsterdam, Netherlands.
BioData Min ; 16(1): 3, 2023 Feb 02.
Article em En | MEDLINE | ID: mdl-36732776
ABSTRACT

BACKGROUND:

Loss-of-Function (LoF) variants in human genes are important due to their impact on clinical phenotypes and frequent occurrence in the genomes of healthy individuals. The association of LoF variants with complex diseases and traits may lead to the discovery and validation of novel therapeutic targets. Current approaches predict high-confidence LoF variants without identifying the specific genes or the number of copies they affect. Moreover, there is a lack of methods for detecting knockout genes caused by compound heterozygous (CH) LoF variants.

RESULTS:

We have developed the Loss-of-Function ToolKit (LoFTK), which allows efficient and automated prediction of LoF variants from genotyped, imputed and sequenced genomes. LoFTK enables the identification of genes that are inactive in one or two copies and provides summary statistics for downstream analyses. LoFTK can identify CH LoF variants, which result in LoF genes with two copies lost. Using data from parents and offspring we show that 96% of CH LoF genes predicted by LoFTK in the offspring have the respective alleles donated by each parent.

CONCLUSIONS:

LoFTK is a command-line based tool that provides a reliable computational workflow for predicting LoF variants from genotyped and sequenced genomes, identifying genes that are inactive in 1 or 2 copies. LoFTK is an open software and is freely available to non-commercial users at https//github.com/CirculatoryHealth/LoFTK .
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Texto completo: 1 Base de dados: MEDLINE Tipo de estudo: Prognostic_studies / Risk_factors_studies Idioma: En Ano de publicação: 2023 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Tipo de estudo: Prognostic_studies / Risk_factors_studies Idioma: En Ano de publicação: 2023 Tipo de documento: Article