Genome-wide association studies (GWAS) and related approaches have identified >60 rheumatoid arthritis (RA) risk loci. Despite this, many alleles remain to be discovered to account for heritability estimates observed in families. We have published data that most common RA risk alleles are shared across diverse ethnic groups.
In Aim 1, we will exploit this observation to discover new RA risk alleles - we will conduct a multi- ethnic GWAS of ~20,000 RA cases and 60,000 matched controls (Asian and European ancestry). We will also compare and contrast genetic findings across two ethnic groups, and develop new analytical methods. We have data (manuscript in press) that RA risk genes identified from GWAS harbor rare alleles that contribute to risk of RA. Together with data from other diseases, this leads to the hypothesis that causal genes at RA risk loci discovered by GWAS will contain independent, protein-coding mutations (most of which will be rare in the general population).
In Aim 2, we will test this hypothesis by sequencing all genes (~650 in total) identified by GWAS in 1,300 RA case and 1,300 non-RA controls of European ancestry. We will also sequence ~2,000 RA case-control samples of Asian ancestry in order to compare and contrast genetic findings across two ethnic groups. We will perform genetic burden tests to determine which genes harbor more rare risk alleles in cases compared to controls than would be expected by chance. However, finding causal variants and genes is only the first step in translating genetic discoveries to improved patient care, and drug discovery in particular. We hypothesize that genes with an allelic series (i.e., multiple alleles that influence disease risk) re excellent drug targets, especially genes that carry loss-of-function (LOF) alleles that protect from disease. We have unpublished data on one RA risk locus that demonstrates our ability to progress from a GWAS to a high-throughput screen (HTS) of ~2,000 small molecules.
In Aim 3 we will extend this approach to new RA risk genes: for at least one gene, we will understand the functional consequences of new RA risk alleles using cutting-edge molecular techniques (e.g., TALENs, RNAi), and use this information to conduct an HTS. Together, these three Aims will (1) discover new RA risk loci using GWAS in Europeans and Asians, (2) discover rare RA risk alleles at causal genes from GWAS loci (also in Europeans and Asians), and (3) translate genetic discoveries into new biology and an HTS of small molecule compounds. IMPACT: We will build upon our past accomplishments to perform the most comprehensive genetic study of RA to date (from common to rare alleles). We will develop innovative methods for integrating GWAS and sequence data, as well as innovative methods to use human genetics to guide drug discovery. Our team has all of the experience and resources to accomplish these Aims.

Public Health Relevance

A long-term goal of understanding the genetic basis of rheumatoid arthritis is to improve care of patients with this common and debilitating disease. In theory, identifying specific pieces of DNA (alleles) should aid in diagnosing a treatable condition either prior to onset of symptoms, or early in the course of disease before bone destruction occurs. In addition, genetics should provide insight into important steps of the disease pathway, allowing for the development of new therapies that target these pathways in at-risk individuals.

National Institute of Health (NIH)
Research Project (R01)
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Genetics of Health and Disease Study Section (GHD)
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Wang, Yan Z
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Brigham and Women's Hospital
United States
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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
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
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