Farm animal Cell Atlas (FarmCA): exploiting regulatory variants and GWAS loci at single cell level

Genome-wide association studies (GWAS) have discovered thousands of non-coding genomic loci associated with complex traits of economic value and diseases in livestock. However, dissecting the functional significance of such variants is currently challenging because it is unclear how variation in the genotype is functionally linked to these traits. Through the pioneering work in the Farm animal Genotype-Tissue Expression (FarmGTEx) and Functional Annotation of Animal Genomes (FAANG) projects [1, 2, 3], we have made substantial strides toward linking traits, genes and tissues. However, this level of resolution is still insufficient to test hypotheses experientially. To do this, one would need to know both the target gene and the target cells within tissues. It is possible to statistically deconvolute the cell-specific signal of gene expression within tissues and use the deconvoluted signal to identify the cell-specific quantitative trait loci. To dissect the cell signals within tissues, it is crucial to build a tissue-specific cell atlas in major tissues in livestock species. Thus, it is of interest to integrate all the publicly available and newly generated single-nuclei/cell RNA-seq data to develop the Farm animal Cell Atlas (FarmCA) for systematically detecting and characterizing distinct cell types in major tissues in livestock species, particularly in cattle, pigs, and chickens. Then, we will integrate this resource with bulk-tissue data from FarmGTEx and FAANG and large-scale GWAS to dissect the genetics of molecular phenotypes (e.g., gene expression) and complex traits at single-cell resolution.

The overarching objectives of this PhD project are:

1) Developing Cell Atlas (FarmCA) for cattle, pig and chicken, and building the FarmCA web server for automatic annotation of new single-cell data.

2) Integrating FarmGTEx and FarmCA to study the regulatory circuitry underlying gene expression and complex traits.

3) Cross-species comparison of single-cell regulatory landscape for translating genetic findings between species