A novel reusable transcriptome-wide association study workflow used to map key genes linked to important cattle...

Area of Science:

• Genomics
• Animal Genetics
• Bioinformatics

Background:

• Transcriptome-wide association studies (TWAS) integrate GWAS and gene expression data to identify genes for complex traits.
• Previous cattle TWAS studies faced challenges with reproducibility and genotype calling accuracy from RNA sequencing data.

Purpose of the Study:

• To develop a streamlined, species-agnostic Nextflow workflow for TWAS in cattle.
• To improve genotype calling and gene expression imputation from transcriptomic data.
• To identify novel gene-trait associations in cattle for complex traits.

Main Methods:

• Developed a reusable Nextflow TWAS workflow integrating transcriptomic and GWAS summary statistics.
• Implemented strategies for accurate genotype calling from transcriptomic data, including imputation for low-pass sequencing.
• Generated gene expression prediction models and imputed gene expression into GWAS cohorts.
• Applied the workflow to novel and public cattle GWAS data, utilizing a new cattle genome transcriptome annotation.

Main Results:

• The workflow successfully generated accurate genotype calls and gene expression prediction models.
• Improved variant calling using imputation methods designed for low-pass sequencing data.
• Identified novel gene-trait associations for complex traits in cattle.
• Detected previously un-assayable associations using enhanced transcriptome annotation.

Conclusions:

• The developed TWAS workflow provides a valuable resource for cattle genetics research.
• The findings contribute to understanding the genetic basis of complex traits in cattle.
• This approach can inform future cattle breeding strategies by identifying key genes.