Session Type: ACR Poster Session A
Session Time: 9:00AM-11:00AM
Background/Purpose: Rheumatoid arthritis (RA) is driven by immune-system dysfunction with contribution from genetic risk factors. Emerging data from genomewide association studies (GWAS) of single nucleotide polymorphisms (SNPs) has revealed 100 risk SNPs in RA outside the HLA region. Beyond GWAS, novel approaches for interpretation of big data are needed to unravel the complex genetics of RA. Our aim is to identify novel risk genes to improve the power of GWAS. We performed a gene-based association analysis using extended Simes procedure (GATES) in RA.
Methods: Our dataset consisted of 14,361 RA cases and 43,923 controls from GWAS meta-analysis in Europeans, from publicly available summary statistics1. All RA cases fulfilled the 1987 ACR criteria or were diagnosed by a rheumatologist. A powerful Knowledge-based mining system for Genome-wide Genetic studies (KGG) was used to run GATES for gene-based association testing with 8,694,488 SNPs (excluding extended MHC, chromosome 6, 25-33 Mb)2. Genes were defined as ± 5kb. Genomic control was calculated by median of Chi-square statistic. We accounted for linkage disequilibrium (LD) between SNPs from 1000 Genomes Project for Europeans. Bonferroni correction was used for multiple testing. LocusZoom was used for visual interpretation of risk loci and genes.
Results: Our genome analysis build used ~8.7 million SNPs assigned to 25,539 genes; 51% of SNPs were located inside genes. GATES revealed a total of 115 genes significantly associated with RA compared with controls (p<1.96E-6, Bonferroni corrected). The majority of GATES top gene hits were located on chromosomes 6, 1, 2 and 19 (22.6%, 20%, 7.8% and 7.8% respectively). From these 115 genes, we identified 43 RA risk loci: 23 risk loci contained a single top risk gene, while 20 risk loci contained two or more risk genes by GATES. Compared to the meta-GWAS results by Okada et al.1, our GATES top genes were replicated for 26 loci; however 17 risk loci had a different top gene by GATES. Our analysis revealed 1 potentially new RA risk locus, located on chromosome 11 (start position 118528941 bp) containing TREH-PHLDB1-MIR6716. Additionally, GATES identified 6 new top gene hits for each of the following 6 risk loci: RPP14 (for DNASE1L3-ABHD6-PXK), PXT1 (for ETV1), MIR5708 (for TPD52), DDX6 (for CXCR5), SUOX (for CDK2), and PCAT29 (for LOC145837). 3 of these loci (ETV1, TPD52 & CDK2) are novel RA risk loci identified by Okada et al.1 and require further analysis to determine top risk genes in the region given our GATES results.
Conclusion: Gene-based association analysis with GATES of big data from meta-analysis of GWAS confirmed prior risk loci and identified potential novel candidate gene hits in RA. Our results will be used subsequently to identify gene-gene interactions and perform genomewide pathway analysis to improve our understanding of the complex genetics in RA. References: 1. Okada Y, Wu D, Trynka G, Raj T, Terao C, et al. Genetics of rheumatoid arthritis contributes to biology and drug discovery. Nature. 2014 Feb 20;506(7488):376-81. 2. Li MX, Gui HS, Kwan JS, Sham PC. GATES: a rapid and powerful gene-based association test using extended Simes procedure. Am J Hum Genet. 2011 Mar 11;88(3):283-93.
To cite this abstract in AMA style:Lenert A, Fardo D. Detecting Novel Candidate Risk Genes in Rheumatoid Arthritis with Gene-Based Association Testing [abstract]. Arthritis Rheumatol. 2016; 68 (suppl 10). https://acrabstracts.org/abstract/detecting-novel-candidate-risk-genes-in-rheumatoid-arthritis-with-gene-based-association-testing/. Accessed September 25, 2020.
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