Background and aims While most of the confirmed SLE-risk loci are in or near genes with immune system function, a major unanswered question is how these loci influence diverse immune cell subsets.

Methods CD14++CD16- classical monocytes (CL) and CD14dimCD16+ non classical (NCL) monocytes from SLE patients were purified by magnetic separation. The Fluidigm C1 System was used for single cell capture and target gene pre-amplification and equal numbers of classical and non-classical monocytes were studied. 90 monocyte-related genes and 7 SLE-risk SNPs were included in eQTL analyses.

Results The SLE-associated SNPs demonstrated more eQTLs in NCLs as compared to CLs (p=2.5x10^-8). For a given SNP, the associated transcripts differed between cell types (p<0.001 for all 7 SNPs for discordance), suggesting that the same SNP resulted in different cellular events between the two monocyte subsets. Loci which shared a significant proportion of eQTL associations with each other in NCLs included TNFAIP3, IRF5, IRF7, PTPN22, and SPP1. In CLs, TNFAIP3 shared a large number of eQTLs with SPP1 and ITGAM, although SPP1 and ITGAM showed more limited overlap with each other. Thus, SLE-associated risk loci exert coordinated effects on gene expression within individual human monocytes, and the risk loci interact in different ways in different cell types.

Conclusions Our study revealed striking differences in the occurrence and interaction between of SLE risk associated eQTLs within different but closely related cell types. This suggests pleiotropic effects from each locus across various immune cell types, and a high degree of complexity when considering how these loci impact the immune system.