Abstract Background/Aims Systemic lupus erythematosus (SLE) genome-wide association studies (GWAS) face multiple challenges in order to identify reliable susceptibility genes. We sought to identify non-HLA overlapping loci in SLE across ethnicities using a cluster-based approach and verify the candidate genes with expression analysis. Methods Association clustering methods such as OASIS reduce the multiple-testing burden and are more powerful than single variant analysis for identifying modest genetic effects. Here, six SLE dbGAP GWAS datasets, 4 EU and 2 Chi involving 19,710 SLE cases and 30,876 controls were analysed using OASIS. Significant variants were tested as expression quantitative trait loci (eQTLs)/splicing quantitative trait loci (sQTLs) using genotype-tissue expression (GTEx). Composite list of genes and eQTLs were checked for differential expression in SLE The Genotype-Tissue Expression (GEO) datasets (GSE30153, GSE13887 and GSE10325) with the GEO2R tool. Pathway analysis was performed using STRING. Results Top genes, common in both ethnicities, were STAT4, SMG7, IRF5, BLK, and TNFAIP3. Overall, OASIS identified 19 highly significant and 16 modestly significant (P 10-8) non-HLA SLE genes common to EU and Chi ethnicities. Significant SNPs at these 35 loci were explored for eQTLs/sQTLs using GTEx. This identified 69 unique significant genes that, when matched with GEO2R results, identified 11 genes with altered expression (Table 1; bold crossed Bonferroni correction). Genes that are significant across multiple ethnicities and have the most significant variants as eQTLs/sQTLs, as well as demonstrate altered expression in multiple datasets, are believed to be reliable modulators of disease pathogenesis. Interaction of these 35 genes elucidated SLE pathways via NOD, TLR, JAK-STAT and RIG-1. Conclusion Several genes and loci were identified using this composite approach of cluster-based multi-ethnicity GWAS meta-analysis, followed by eQTL search for the most significant variants at these loci and expression analysis. This large meta-analysis has helped identify replicable pathogenic genes and pathways for SLE. Disclosure M. Khan: None.
Mohammad Saeed Khan (Wed,) studied this question.