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

Transcriptome-wide association studies (TWAS) are a powerful approach for studying the genes underlying complex traits by directly integrating GWAS and gene expression datasets. In cattle they have been previously applied to identify genes driving fertility, milk production and health. However, these studies have also highlighted several challenges, from difficulties with reproducing these complex analyses, to limitations from poor genotype calls, especially when called directly from RNA sequencing data. To address these and other challenges, for the H2020 BovReg Project, we have developed a streamlined, species-agnostic and reusable, Nextflow TWAS analysis workflow to integrate transcriptomic and GWAS summary statistic datasets. The TWAS analysis workflow we present integrates available tools to both generate more accurate genotype calls and gene expression prediction models from transcriptomic datasets, to imputing gene expression levels into GWAS cohorts and associating genes with traits of interest. We explore optimal strategies for calling genetic variants directly from RNA-seq data and illustrate that using imputation approaches specifically designed for low-pass sequencing data can improve variant calling over previously adopted methods. We demonstrate the utility of this workflow by applying it to both novel BovReg, and publicly available published GWAS cohorts for cattle, and detect novel gene-trait associations for key traits. Using a new high resolution transcriptome annotation of the cattle genome, generated by BovReg Project partners we also illustrate how previously un-assayable associations can be detected. The results and the workflow we present, provide a new resource for the community and contribute to understanding the molecular drivers of complex traits in cattle populations with the goal of eventually leveraging this information in future breeding decisions.