Long-term outcome in patients with rheumatoid arthritis (RA) is highly dependent upon aggressive pharmacological control of inflammation early in the disease course. Despite the importance of selecting the optimal medication soon after disease onset, there is no clinical or biomarker predictor of drug treatment response. A genetic biomarker would be particularly useful for drugs that block the inflammatory cytokine TNF-alpha (TNF), as these drugs are first-line biological disease modifying anti-rheumatic drugs DMARDs, yet induce remission in only ~30% of patients. In this application, our central hypothesis is that common genetic variants of modest effect size predict response to anti-TNF therapy. To test this hypothesis, we propose to expand upon our established multi-center collaboration and available GWAS data to develop (i) new statistical methods for conducting GWAS (estimating variance explained by common single nucleotide polymorphisms, SNPs), (ii) new informatics methods for defining treatment response in the EMR (which will allow us to collect many more samples for GWAS), and (iii) a novel framework for testing mechanism directly in human immune cells.
Aim 1 : Analyze GWAS data on ~1,200 RA patients to search for common variants that predict response to anti-TNF therapy.
Aim 2 : Use electronic medical records (EMR) at Partners HealthCare, Vanderbilt and Northwestern to define treatment response, and conduct a GWAS on ~1,200 additional RA patients treated with anti-TNF therapy.
Aim 3 : Test mechanism of action of alleles that predict treatment response to anti-TNF therapy in human immune cells.

Public Health Relevance

A long-term goal of understanding the genetic basis treatment response in patients with rheumatoid arthritis (RA) is to improve care of patients with this common and debilitating disease. In theory, identifying specific pieces of DNA ("alleles") that predict treatment response should aid in targeting therapy to the right individuals early in the course of disease before bone destruction occurs.

National Institute of Health (NIH)
National Institute of General Medical Sciences (NIGMS)
Research Project--Cooperative Agreements (U01)
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Special Emphasis Panel (ZRG1-GGG-M (52))
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Long, Rochelle M
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Brigham and Women's Hospital
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Okada, Yukinori; Diogo, Dorothee; Greenberg, Jeffrey D et al. (2014) Integration of sequence data from a Consanguineous family with genetic data from an outbred population identifies PLB1 as a candidate rheumatoid arthritis risk gene. PLoS One 9:e87645
Cui, J; Taylor, K E; Lee, Y C et al. (2014) The influence of polygenic risk scores on heritability of anti-CCP level in RA. Genes Immun 15:107-14
Okada, Yukinori; Wu, Di; Trynka, Gosia et al. (2014) Genetics of rheumatoid arthritis contributes to biology and drug discovery. Nature 506:376-81
Diogo, Dorothee; Okada, Yukinori; Plenge, Robert M (2014) Genome-wide association studies to advance our understanding of critical cell types and pathways in rheumatoid arthritis: recent findings and challenges. Curr Opin Rheumatol 26:85-92
Okada, Yukinori; Han, Buhm; Tsoi, Lam C et al. (2014) Fine mapping major histocompatibility complex associations in psoriasis and its clinical subtypes. Am J Hum Genet 95:162-72
Han, Buhm; Diogo, Dorothée; Eyre, Steve et al. (2014) Fine mapping seronegative and seropositive rheumatoid arthritis to shared and distinct HLA alleles by adjusting for the effects of heterogeneity. Am J Hum Genet 94:522-32
Hutchinson, John N; Raj, Towfique; Fagerness, Jes et al. (2014) Allele-specific methylation occurs at genetic variants associated with complex disease. PLoS One 9:e98464
Sinnott, Jennifer A; Dai, Wei; Liao, Katherine P et al. (2014) Improving the power of genetic association tests with imperfect phenotype derived from electronic medical records. Hum Genet 133:1369-82
Liao, Katherine P; Diogo, Dorothee; Cui, Jing et al. (2014) Association between low density lipoprotein and rheumatoid arthritis genetic factors with low density lipoprotein levels in rheumatoid arthritis and non-rheumatoid arthritis controls. Ann Rheum Dis 73:1170-5
Li, Gang; Diogo, Dorothee; Wu, Di et al. (2013) Human genetics in rheumatoid arthritis guides a high-throughput drug screen of the CD40 signaling pathway. PLoS Genet 9:e1003487

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