A genome-wide association study (GWAS) meta-analysis of over 100,000 individuals of European and Asian ancestry identified 42 novel risk loci for rheumatoid arthritis (RA), bringing the total number of RA risk loci to 101. These loci were analyzed using bioinformatics methods, including functional annotation, cis-acting expression quantitative trait loci (cis-eQTL), and pathway analysis, as well as novel methods based on genetic overlap with human primary immunodeficiency (PID), hematological cancer somatic mutations, and knock-out mouse phenotypes. The study identified 98 biological candidate genes at these 101 risk loci, many of which are targets of approved therapies for RA. The study also demonstrated that drugs approved for other indications may be repurposed for RA treatment. The study conducted a three-stage trans-ethnic meta-analysis, revealing significant correlations between risk allele frequencies (RAF) and odds ratios (OR) between Europeans and Asians. The study also found that approximately two-thirds of RA risk loci demonstrated pleiotropy with other human phenotypes, including immune-related diseases, inflammation-related or hematological biomarkers, and other complex traits. The study developed an in-silico bioinformatics pipeline to prioritize the most likely biological candidate genes. This pipeline integrated data across associated loci, evaluated biological datasets, and used various methods to test for enrichment of RA risk genes. The study identified 98 genes with a score ≥2, which were defined as "candidate biological RA risk genes." The study also evaluated the potential role of RA genetics in drug discovery. It found that 27 drug target genes of approved RA drugs demonstrated significant overlap with 98 biological RA risk genes and 2,332 genes from the expanded PPI network. Examples of approved RA therapies identified by this analysis include tocilizumab (anti-IL6R), tofacitinib (JAK3 inhibitor), and abatacept (CTLA4-Ig). The study also assessed how approved drugs for other diseases might be connected to biological RA risk genes. It highlighted CDK6 and CDK4, targets of three approved drugs for different types of cancer. One CDK6/CDK4 inhibitor, flavopiridol, has been shown to ameliorate disease activity in animal models of RA. The study concluded that the genetics of RA can provide important information for drug discovery. It provided empirical evidence that the genetics of RA can be integrated with other biological information to derive biological insights and drug discovery. The study also highlighted the role of genetics for drug discovery and provided a systematic approach by which human genetic data can be efficiently integrated with other biological information to derive biological insights and drug discovery.A genome-wide association study (GWAS) meta-analysis of over 100,000 individuals of European and Asian ancestry identified 42 novel risk loci for rheumatoid arthritis (RA), bringing the total number of RA risk loci to 101. These loci were analyzed using bioinformatics methods, including functional annotation, cis-acting expression quantitative trait loci (cis-eQTL), and pathway analysis, as well as novel methods based on genetic overlap with human primary immunodeficiency (PID), hematological cancer somatic mutations, and knock-out mouse phenotypes. The study identified 98 biological candidate genes at these 101 risk loci, many of which are targets of approved therapies for RA. The study also demonstrated that drugs approved for other indications may be repurposed for RA treatment. The study conducted a three-stage trans-ethnic meta-analysis, revealing significant correlations between risk allele frequencies (RAF) and odds ratios (OR) between Europeans and Asians. The study also found that approximately two-thirds of RA risk loci demonstrated pleiotropy with other human phenotypes, including immune-related diseases, inflammation-related or hematological biomarkers, and other complex traits. The study developed an in-silico bioinformatics pipeline to prioritize the most likely biological candidate genes. This pipeline integrated data across associated loci, evaluated biological datasets, and used various methods to test for enrichment of RA risk genes. The study identified 98 genes with a score ≥2, which were defined as "candidate biological RA risk genes." The study also evaluated the potential role of RA genetics in drug discovery. It found that 27 drug target genes of approved RA drugs demonstrated significant overlap with 98 biological RA risk genes and 2,332 genes from the expanded PPI network. Examples of approved RA therapies identified by this analysis include tocilizumab (anti-IL6R), tofacitinib (JAK3 inhibitor), and abatacept (CTLA4-Ig). The study also assessed how approved drugs for other diseases might be connected to biological RA risk genes. It highlighted CDK6 and CDK4, targets of three approved drugs for different types of cancer. One CDK6/CDK4 inhibitor, flavopiridol, has been shown to ameliorate disease activity in animal models of RA. The study concluded that the genetics of RA can provide important information for drug discovery. It provided empirical evidence that the genetics of RA can be integrated with other biological information to derive biological insights and drug discovery. The study also highlighted the role of genetics for drug discovery and provided a systematic approach by which human genetic data can be efficiently integrated with other biological information to derive biological insights and drug discovery.