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

Area of Science:

• Genomics
• Bioinformatics
• Computational Biology

Background:

• Loss-of-Function (LoF) variants are crucial for understanding clinical phenotypes and identifying therapeutic targets.
• Existing methods struggle to pinpoint specific genes affected by LoF variants or detect compound heterozygous (CH) LoF variants.
• There's a need for tools that can identify genes with one or two lost copies due to LoF variants.

Purpose of the Study:

• To develop an automated tool for predicting LoF variants across various genome data types.
• To enable the identification of genes inactivated by one or two LoF variants, including CH LoF.
• To provide a reliable computational workflow for LoF variant analysis.

Main Methods:

• Development of the Loss-of-Function ToolKit (LoFTK), a command-line based software.
• Automated prediction of LoF variants from genotyped, imputed, and sequenced genomes.
• Analysis of parent-offspring data to validate predictions of CH LoF genes.

Main Results:

• LoFTK efficiently predicts LoF variants and identifies genes inactive in one or two copies.
• The tool successfully identifies compound heterozygous (CH) LoF variants, leading to two lost gene copies.
• Parent-offspring data confirmed that 96% of predicted CH LoF genes in offspring had alleles from both parents.

Conclusions:

• LoFTK offers a reliable computational workflow for predicting LoF variants and identifying affected genes.
• The tool is capable of detecting genes inactivated by one or two copies, including CH LoF.
• LoFTK is available as open-source software for non-commercial use.