Genetics of rheumatoid arthritis contributes to biology and drug discovery

Abstract

A major challenge in human genetics is to devise a systematic strategy to integrate disease-associated variants with diverse genomic and biological data sets to provide insight into disease pathogenesis and guide drug discovery for complex traits such as rheumatoid arthritis (RA). Here we performed a genome-wide association study meta-analysis in a total of >100,000 subjects of European and Asian ancestries (29,880 RA cases and 73,758 controls), by evaluating ∼10 million single-nucleotide polymorphisms. We discovered 42 novel RA risk loci at a genome-wide level of significance, bringing the total to 101 (refs 2 - 4). We devised an in silico pipeline using established bioinformatics methods based on functional annotation, cis-acting expression quantitative trait loci and pathway analyses--as well as novel methods based on genetic overlap with human primary immunodeficiency, haematological cancer somatic mutations and knockout mouse phenotypes--to identify 98 biological candidate genes at these 101 risk loci. We demonstrate that these genes are the targets of approved therapies for RA, and further suggest that drugs approved for other indications may be repurposed for the treatment of RA. Together, this comprehensive genetic study sheds light on fundamental genes, pathways and cell types that contribute to RA pathogenesis, and provides empirical evidence that the genetics of RA can provide important information for drug discovery.

Figure 1. Overlap of RA risk loci with PID, hematological cancer somatic mutation, and molecular pathways

a, Overlap of RA risk genes with PID genes, subset by PID categories (I-VIII). b, Examples of overlap of hematological cancer somatic mutation genes with RA risk genes. c, Comparisons of molecular pathway analysis results between the current trans-ethnic meta-analysis (y-axis) and the previous meta-analysis for rheumatoid arthritis (x-axis). Each dot represents a molecular pathway. Dotted line represents FDR-q = 0.05 or y = x.

Figure 2. Prioritized biological RA risk genes

Representative biological RA risk genes. We list the summary gene score derived from individual criterion (filled red box indicates criterion satisfied; 98 genes with score ≥2 out of 377 genes included in the RA risk loci were defined as “biological candidate genes”; see details in Extended Data Fig. 6). Filled blue box indicates the nearest gene to the RA risk SNP. Filled green boxes indicate overlap with H3K4me3 peaks in immune-related cells. Filled purple boxes indicate overlap with drug target genes. Full results are in Supplementary Table 5.

Figure 3. Connection of biological RA risk genes to drug targets

a, PPI network of biological RA risk genes and drug target genes. b, Overlap and relative enrichment of 98 biological RA risk genes with targets of approved RA drugs and with all drug target genes. Enrichment was more apparent than that from all 377 RA risk genes (Extended Data Fig. 7c). c, Connections between RA risk SNPs (blue), biological genes (purple), genes from PPI (green), and approved RA drugs (orange). Full results are in Extended Data Fig. 8. d, Connections between RA genes and drugs indicated for other diseases.