Evidence map›Paper›PMID 36732776›Full record

ArticleBioData mining2023

LoFTK: a framework for fully automated calculation of predicted Loss-of-Function variants and genes.

Abdulrahman Alasiri, Konrad J Karczewski, Brian Cole, Bao-Li Loza, Jason H Moore, Sander W van der Laan, Folkert W Asselbergs, Brendan J Keating, Jessica van Setten

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in BioData mining, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
3.1field-weighted citation impact, top 8% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed, 10 citations in OpenAlex.

  1. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors at 8 institutions in 5 countries.

Abdulrahman AlasiriDepartment of Cardiology, Division Heart and Lungs, University Medical Center Utrecht, University of Utrecht, Heidelberglaan 100, 3584 CX, Utrecht, Netherlands.
Konrad J KarczewskiProgram in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
Brian ColeBioinformatics Core, Harvard Medical School, Boston, MA, USA.
Bao-Li LozaPerelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
Jason H MooreDepartment of Computational Biomedicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
Sander W van der LaanCentral Diagnostic Laboratory, Division Laboratories, Pharmacy, and Biomedical Genetics, University Medical Center Utrecht, University of Utrecht, Utrecht, Netherlands.
Folkert W AsselbergsDepartment of Cardiology, Amsterdam University Medical Centers, University of Amsterdam, Amsterdam, Netherlands.
Brendan J KeatingPerelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
Jessica van SettenDepartment of Cardiology, Division Heart and Lungs, University Medical Center Utrecht, University of Utrecht, Heidelberglaan 100, 3584 CX, Utrecht, Netherlands. j.vansetten@umcutrecht.nl.
University of Pennsylvania · USBroad Institute · USCedars-Sinai Medical Center · USHarvard University · USKing Abdullah International Medical Research Center · SAUniversity College London · GBUniversity Hospital Heidelberg · DEUniversity Medical Center Utrecht · NL

Funding

Bioinformatics Strategies for Genome-Wide Association StudiesR01LM010098 · NLM · UNIVERSITY OF PENNSYLVANIA · PI MOORE, JASON H., WILLIAMS, SCOTT MATTHEW · 2009 to 2023
$5.1M
Dutch Heart Foundation 2017T003Innovative Medicines Initiative 116074King Abdullah International Medical Research Center 400016184NIH HHS LM010098NLM NIH HHS R01 LM010098the Netherlands CardioVascular Research Initiative CVON 2011/B019
6 · The paper itself

Abstract

backgroundLoss-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.

resultsWe 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.

conclusionsLoFTK 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 .

Indexed as

Compound heterozygotesHuman geneticKnockout genesLoss-of-Function variants

Identifiers

PMID36732776
PMCPMC9893534
OpenAlexW4319029703

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.