Characterization of genetic regulatory variants acting on the transcriptome of livestock is essential for interpreting the molecular mechanisms underlying traits of economic value and for increasing the rate of genetic gain through artificial selection. Here we build a cattle Genotype-Tissue Expression atlas (cattleGTEx) as part of the pilot phase of Farm animal GTEx (FarmGTEx) project for the research community based on publicly available 7,180 RNA-Seq samples. We describe the landscape of transcriptome across over 100 tissues and report hundreds of thousands of genetic associations with gene expression and alternative splicing for 24 major tissues. We evaluate the tissue-sharing patterns of these genetic regulatory effects, and functionally annotate them using multi-omics data. Finally, we link gene expression in different tissues to 43 economically important traits using both transcriptome-wide association and colocalization analyses to decipher the molecular regulatory mechanisms underpinning such agronomic traits in cattle.
There are mainly nine parts of analysis in our project.
Includes gene expression quantification from RNA-seq.
Includes the analysis of WGBS data.
Includes the detection of tissue specific expressed genes and spliced introns using the method illustrated in Finucane et al. (2018).
Includes the estimates of effect size (aFC) of requlatory variants using aggregated phASER haplotypic expression data using phASER and the correlation plot between top cis-eQTL slopes (from fastQTL).
Includes the SNP calling from RNA-seq and imputation using Beagle 5.
Includes the eQTL detection using fastQTL, effect size calculation using aFC and finemapping analysis using DAP-G
Includes the eQTL detection using fastQTL.
Includes the trans-eQTL detection by a mixed linear model using mlma from GCTA with and without cis-eQTL adjustments.
Includes the TWAS analysis using S-PrediXcan and MultiXcan from MetaXcan and Colocalization analysis using Coloc and fastENLOC.
Includes the HiC data processes using HiC-Pro(v2.11.4) and estimates of significant intra-chromosome contacts using FitHiC(v2.0.7).