DICE: Transcriptomic Profiling and Analysis of Impact of Genetic Variation on Rare Immune Cell Populations

Genomic studies of individuals from various human populations have revealed significant genetic heterogeneity, with over 80 million autosomal single nucleotide polymorphisms (SNPs), including about 8 million common variants. Disease-risk associations for an enormous number of these SNPs have been identified by genome-wide association studies (GWAS). However, the overwhelming majority of GWAS hits are located in non-coding regions of the genome, and thus, very little is known about exactly which genes they affect, in which cell types they act, and what their functional relevance is to disease development and progression. Results from large-scale epigenomic initiatives indicate that these disease-risk variants are likely to affect gene expression in a specific subset of cell types and in a context-dependent manner. Defining the specific cell types in which disease-risk variants modulate gene expression will aid mechanistic and functional studies that investigate the genetic basis of specific diseases. To address this knowledge gap as it relates to cells of the immune system, as well as to identify which immune cell populations are most susceptible to effects of disease-risk variants, we established the DICE (Database of Immune Cell Expression, Expression quantitative trait loci (eQTLs) and Epigenomics) project. Here, we simultaneously study multiple and diverse immune cell populations from a well-defined cohort of donors, which is essential to uncover all potential cell-specific effects of genetic variants. Utilizing high-throughput single-cell RNA-seq techniques, we discovered novel transcriptomic features and genetic effects on gene expression in rare immune cell populations such as innate lymphoid cells (ILCs) and natural killer (NK) cells in our DICE cohort. We identified functionally distinct cell populations, and detected effects of cis-eQTLs for >1,000 genes in these immune cell populations. Interestingly, the vast majority of these genes show strong cis-associations with genotype only in a single immune cell subset, and a substantial fraction of these genes is also associated with GWAS, linking these genes and the respective immune cell types directly to disease pathogenesis. In addition, we found that biological sex is associated with major differences in gene expression in these immune cells in a highly cell-specific manner. In conclusion, our results are highlighting the value of studying homogeneous cell populations to identify novel immune cell subsets and genes that are most susceptible to the effects of particular genetic variants. With these new datasets from the DICE project, we are largely expanding the understanding of the function of rare immune cell populations. This helps to reveal the effects of genetic variants associated with risk for human disease on specific genes and immune cell populations, and provides novel insights into the underlying molecular mechanisms of the pathogenesis of human disease (http://dice-database.org).