Characterization of genetic regulatory variants acting on livestock gene expression 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 the Farm animal GTEx (FarmGTEx) project for the research community based on 7,180 publicly available RNA-sequencing (RNA-seq) samples. We describe the transcriptomic landscape of more than 100 tissues/cell types and report hundreds of thousands of genetic associations with gene expression and alternative splicing for 23 distinct tissues. We evaluate the tissue-sharing patterns of these genetic regulatory effects, and functionally annotate them using multiomics 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.

Background: Cross-species comparison of transcriptomes is important for elucidating evolutionary molecular mechanisms underpinning phenotypic variation between and within species, yet to date it has been essentially limited to model organisms with relatively small sample sizes. Results: Here, we systematically analyze and compare 10,830 and 4866 publicly available RNA-seq samples in humans and cattle, respectively, representing 20 common tissues. Focusing on 17,315 orthologous genes, we demonstrate that mean/median gene expression, inter-individual variation of expression, expression quantitative trait loci, and gene co-expression networks are generally conserved between humans and cattle. By examining large-scale genome-wide association studies for 46 human traits (average n = 327,973) and 45 cattle traits (average n = 24,635), we reveal that the heritability of complex traits in both species is significantly more enriched in transcriptionally conserved than diverged genes across tissues. Conclusions: In summary, our study provides a comprehensive comparison of transcriptomes between humans and cattle, which might help decipher the genetic and evolutionary basis of complex traits in both species.

The mechanisms by which GnIH regulates the steroid synthesis pathway in duck granulosa cells remain poorly understood. In this study, we measured steroid hormone secretion by ELISA and reproduction-associated gene expression by quantitative real-time Polymerase Chain Reaction (qPCR) in duck granulosa cells treated with different concentrations of GnIH (0, 0.1, 1, 10, and 100 ng/mL) for 24 h. The genome-wide expression profiles of GnIH-treated cells (0 and 10 ng/mL) were evaluated by high-throughput RNA sequencing. Compared with untreated cells, the secretion of the steroid hormones E2, E1, P4, and T was downregulated, with that of E1 and P4 reaching statistical significance (P<0.05); in contrast, the secretion of ACV and INH was significantly upregulated (P<0.05) after treatment with 10 and 100 ng/mL GnIH. The expression of encoding steroidogenic proteins and enzymes genes (STAR, CYP11A1, CYP17A1, CYP19A1, and 3-β-HSD) and encoding gonadotropin receptors genes (FSHR, LHR) were significantly declined (P<0.05) in the 10 and 100 ng/mL GnIH treatments. Transcriptome sequencing identified 348 differentially expressed genes (DEGs), including 253 upregulated and 95 downregulated genes. The DEGs were mainly involved in cell growth and death, immune response, and steroid biosynthesis pathways. We identified four novel DEGs (MROH5, LOC113840576, SDR42E1, and LOC113841457) with key roles in the regulation of steroid hormone biosynthesis. Our study revealed changes in gonadal steroid hormone secretion and steroid biosynthesis pathway-related gene expression in duck granulosa cells under the inhibitory effect of GnIH. These data contribute to our understanding of the molecular and genetic mechanisms underlying reproduction in ducks.

With the development of SNP chips, SNP information provides an efficient approach to further disentangle different patterns of genomic variances and covariances across the genome for traits of interest. Due to the interaction between genotype and environment as well as possible differences in genetic background, it is reasonable to treat the performances of a biological trait in different populations as different but genetic correlated traits. In the present study, we performed an investigation on the patterns of region-specific genomic variances, covariances and correlations between Chinese and Nordic Holstein populations for three milk production traits. Variances and covariances between Chinese and Nordic Holstein populations were estimated for genomic regions at three different levels of genome region (all SNP as one region, each chromosome as one region and every 100 SNP as one region) using a novel multi-trait random regression model which uses latent variables to model heterogeneous variance and covariance. In the scenario of the whole genome as one region, the genomic variances, covariances and correlations obtained from the new multi-trait Bayesian method were comparable to those obtained from a multi-trait GBLUP for all the three milk production traits. In the scenario of each chromosome as one region, BTA 14 and BTA 5 accounted for very large genomic variance, covariance and correlation for milk yield and fat yield, whereas no specific chromosome showed very large genomic variance, covariance and correlation for protein yield. In the scenario of every 100 SNP as one region, most regions explained <0.50% of genomic variance and covariance for milk yield and fat yield, and explained <0.30% for protein yield, while some regions could present large variance and covariance. Although overall correlations between two populations for the three traits were positive and high, a few regions still showed weakly positive or highly negative genomic correlations for milk yield and fat yield. The new multi-trait Bayesian method using latent variables to model heterogeneous variance and covariance could work well for estimating the genomic variances and covariances for all genome regions simultaneously. Those estimated genomic parameters could be useful to improve the genomic prediction accuracy for Chinese and Nordic Holstein populations using a joint reference data in the future.