Review
Genetic basis of rheumatoid arthritis: A current review

https://doi.org/10.1016/j.bbrc.2014.07.085Get rights and content

Highlights

  • •

    GWASs have clarified the genetic architecture of rheumatoid arthritis (RA).

  • •

    Functional analysis of each RA risk gene reveals pathologic mechanism of disease.

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    Genetic data can be used in clinical practice in RA treatment.

Abstract

Rheumatoid arthritis (RA) is one of the most common autoimmune diseases. As with other complex traits, genome-wide association studies (GWASs) have tremendously enhanced our understanding of the complex etiology of RA. In this review, we describe the genetic architecture of RA as determined through GWASs and meta-analyses. In addition, we discuss the pathologic mechanism of the disease by examining the combined findings of genetic and functional studies of individual RA-associated genes, including HLA-DRB1, PADI4, PTPN22, TNFAIP3, STAT4, and CCR6. Moreover, we briefly examine the potential use of genetic data in clinical practice in RA treatment, which represents a challenge in medical genetics in the post-GWAS era.

Section snippets

Genetic aspects of rheumatoid arthritis

Rheumatoid arthritis (RA) is one of the most common forms of autoimmune arthritis, affecting approximately 0.5–1.0% of the world’s population. The serum of most RA patients contains autoantibodies, such as rheumatoid factor (RF) or anti-citrullinated protein antibodies (ACPAs), the presence of which constitutes one of the new classification criteria for RA revised in 2010 [1]. Although RF is also present in other autoimmune diseases and immunological conditions, such as chronic infection and

HLA-DRB1 gene

Since the first evidence suggesting the involvement of human leukocyte antigens (HLAs) in RA was reported in 1969 [10], polymorphisms in the HLA region have been at the center of genetic studies of RA. That study demonstrated reduced lymphocyte responses in autologous mixed cultures of cells from RA patients, suggesting that polymorphisms in HLA genes (which encode the major histocompatibility complex [MHC] molecules that present antigens to T cells) are shared among patients [10].

Insights from GWASs

In a GWAS, ∼1 million single-nucleotide polymorphisms (SNPs) are simultaneously genotyped for affected patients (cases) and non-affected individuals (controls). The null hypothesis of a GWAS is that there is no association between a given SNP and disease susceptibility and is tested by comparing the allele frequency or genotype frequency between cases and controls. If the null hypothesis is rejected with a genome-wide significance level, which is usually set at α = 5 × 10−8, the genetic marker

RA risk genes and pathogenesis

As mentioned above, GWASs have identified more than 100 RA risk loci. Although the effect of each individual locus is moderate (e.g., the odds ratio for most individual alleles ranges between 1.1 and 1.3), detailed analyses of individual loci to identify disease-causing variants and to determine the effect of the identified variants on responsible genes (e.g., gain-of-function or loss-of-function) would enhance our understanding of the disease. Examples of RA risk genes and their role in the

Missing heritability

The GWAS approach has proven to be a powerful means of identifying risk loci that control complex traits under the common disease–common variant hypothesis, which assumes that common variants of modest effect are responsible for common diseases [73]. However, it is becoming apparent that common variants can explain only a small proportion of the heritability of these diseases. In RA, the 100 risk loci identified outside the HLA region explain only 5.5% and 4.7% of the total risk of developing

Clinical use of genetic data in RA

In the final section of this review, we discuss the use of genetic data in clinical practice as it pertains to treating RA. The use of genetic data represents a challenge in the post-GWAS era because RA is a very heterogeneous disease with an outcome that is difficult to predict. The heterogeneity of RA can be partially explained by genetic factors; that is, the specific combination of genetic factors in an individual can determine the outcome of the disease. In this context, GWAS data can be

Acknowledgments

This work was supported by grants from the Ministry of Education, Culture, Sports, Sciences and Technology and the Ministry of Health, Labor and Welfare of the Japanese government.

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