Streamlined computational pipeline for genetic background characterization of genetically engineered mice based on...

Area of Science:

• Genomics
• Bioinformatics
• Mouse Models

Background:

• Genetically engineered mice (GEM) are crucial for gene function and disease research.
• Traditional methods for characterizing GEM, like SNP genotyping, cannot detect novel or rare variants.
• Genetic introgression from 129-derived embryonic stem cells (ESCs) into C57BL/6 backgrounds can complicate experimental results.

Purpose of the Study:

• To develop and validate a computational pipeline for detecting genetic introgression in GEM.
• To identify variants and potential modifier genes associated with genetic introgression.
• To assess the impact of introgression on gene expression and potential phenotypic interference.

Main Methods:

• A computational pipeline was developed using the Galaxy platform and BASH/R scripts.
• The pipeline processes next-generation sequencing (NGS) data, including whole genome sequencing (WGS), whole exome sequencing (WES), and RNA-Seq.
• Analysis included variant visualization and identification of linked variants and modifier genes.

Main Results:

• The pipeline effectively identified genetic introgression in congenic knockout (KO) mouse lines.
• Impact of 129-derived ESC introgression on gene expression was revealed.
• Potential modifier genes and phenotypic interference in KO lines were identified.

Conclusions:

• The developed computational pipeline is an effective method for determining genetic introgression in GEM.
• Accurate characterization of GEM genetic makeup is essential for valid experimental conclusions.
• This approach aids in understanding the genetic landscape of GEM and its impact on research outcomes.