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OP0027 Identification of Molecular Disease Drivers Using eQTLS Derived from a Cohort of Rheumatoid Arthritis Patients
  1. A. Walsh,
  2. J.W. Whitaker,
  3. C.C. Huang,
  4. Y. Cherkas,
  5. S. Lamberth,
  6. C. Brodmerkel,
  7. M. Curran,
  8. R. Dobrin
  1. Janssen R&D, Spring House, United States

Abstract

Background Genome wide association studies (GWAS) have yielded over 100 genetic loci associated with rheumatoid arthritis (RA). However, the utility of GWAS studies for drug discovery is limited because most GWAS hits do not reside in coding regions and have no known function. Elucidating potential transcriptional regulatory roles of GWAS hits will allow functional annotation of disease-related GWAS loci and lead to better understanding of RA etiology.

Objectives To identify potential molecular drivers of RA by mapping expression quantitative trait loci (eQTLs) from gene expression and genotype data from a cohort of RA patients.

Methods eQTLs were mapped using whole blood transcriptome data (Affymetrix microarray) from 377 RA patients with inadequate response to methotrexate enrolled in a clinical trial at baseline. Genotypes were generated from whole genome sequencing of DNA from the same subjects. eQTL mapping was performed by linear regression on adjusted data and FDR was estimated with a permutation method separately for local and distant associations.

Results We report over 6,000 unique genes with significant eQTLs that represent potential molecular drivers. These eQTLs are enriched for genetic variants associated with RA. Additionally, the genes associated with these RA GWAS loci represent several disease-relevant biological pathways, including antigen presentation and B cell signaling. While there are multiple published eQTL datasets from non-RA cohorts, our analysis suggests that there is utility in detecting eQTLs from disease-relevant cohorts. Indeed, we identified several eQTLs overlapping with RA GWAS loci that have not been reported previously. We also demonstrate that integration with publicly available epigenomics datasets enables elucidation of cell-type-specific relationships. In particular, genes with expression driven by active enhancers in specific immune cell types (e.g., B cells and T helper cells) were identified, generating hypotheses that can be validated experimentally.

Conclusions As one of the first studies to detect eQTLs from RA patients, this work provides a valuable resource to better understand the genetic basis for RA and aid in translating genetics research into therapeutic interventions. Overall, this analysis highlights the value of studying peripheral blood given the challenge of obtaining large numbers of synovial tissue biopsies from RA patients.

Disclosure of Interest A. Walsh Employee of: Johnson & Johnson, J. Whitaker Employee of: Johnson & Johnson, C. Huang Employee of: Johnson & Johnson, Y. Cherkas Employee of: Johnson & Johnson, S. Lamberth Employee of: Johnson & Johnson, C. Brodmerkel Employee of: Johnson & Johnson, M. Curran Employee of: Johnson & Johnson, R. Dobrin Shareholder of: Johnson & Johnson, Employee of: Johnson & Johnson

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