| Title: | LoFTK: a framework for fully automated calculation of predicted Loss-of-Function variants and genes |
| Journal: | BioData Mining |
| Published: | 2 Feb 2023 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/36732776/ |
| DOI: | https://doi.org/10.1186/s13040-023-00321-5 |
| URL: | https://biodatamining.biomedcentral.com/counter/pdf/10.1186/s13040-023-00321-5 |
| Citations: | 1 (1 in last 2 years) as of 8 Aug 2024 |
Publication 7963
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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.9 Authors
- 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
1 Application
| Application ID | Title |
|---|---|
| 24711 | On the quest for tissue-specific contributions to disease |
Enabling scientific discoveries that improve human health