Ethnic-Specific Genetic Analyses in Rheumatoid Arthritis: Incremental Gains but Valuable Contributions to the Big Picture

Rheumatoid arthritis (RA) is a common autoimmune disease characterized by chronic inflammation of the synovial membrane, which can lead to joint damage and a variety of other clinical manifestations (1). The number of genes reported to be associated with susceptibility to RA continues to grow, now totaling ~60 (2). Although RA affects individuals from diverse ethnic backgrounds, genome-wide association (GWA) studies of RA have been performed only in subjects of European or east Asian (particularly Japanese) ancestry (2). In addition, as is the case with most complex diseases, a substantial proportion of “missing heritability” remains to be identified in RA (3). The difference between RA heritability estimates from family-based studies and the variance explained by SNPs from GWA studies has not been investigated in non-European populations.

In this issue of Arthritis & Rheumatism, Negi et al. (4) report a novel association of the gene ARL15 (ADP-ribosylation factor-like 15) from the first GWA study of RA in North Indians. Although India's population of 1.2 billion has thousands of ethnic groups characterized by differences in language, customs, and religion, two ancient populations are ancestral to most present-day Indians: ancestral North Indians (ANI) and ancestral South Indians (5). Of the two ancestral groups, ANI are genetically closer to Middle Easterners, Central Asians, and Europeans. ANI ancestry ranges from 39-71% in most Indian groups and the degree of ANI ancestry is reportedly higher in traditionally upper caste and Indo-European speakers (5). There is a clear sub-structure in the ANI population in the study by Negi et al., with three clusters identified using multidimensional scaling (see Figure 1b), but single marker tests were adjusted for the resulting genomic inflation (Figure 2).

The association of ARL15 with RA reported by Negi et al. underscores the importance of the search for genetic underpinnings of RA and other immune-mediated diseases among different ethnicities. There are several reasons to study RA genetics among different ethnicities – to look for different, ethnic-specific risk factors (as was the focus of Negi et al.); to look for risk factors shared across multiple ethnicities; and to leverage divergent linkage disequilibrium patterns to refine the distance between the tag polymorphism and causal variant.

The presence of a significant number of ethnic-specific risk alleles for RA might lead to the development of ethnic-specific diagnostic and therapeutic tools. However, associations that are limited to one or more, but not all ethnicities, such as that of PTPN22 with RA in Europeans (6), but not in African-Americans (7), appear to be the exception rather than the rule. The paucity of ethnic-specific risk alleles points to the potential value of large-scale genetic association studies across RA patients of different ethnicities, namely enabling the conduct of trans-ethnic analyses. By analyzing large groups of RA patients of different ethnicities together, there is the capability of performing fine mapping of causal variants and increased statistical power to identify new genetic associations (8). Such an approach has been used to identify new loci associated with RA (9) as well as broader phenotypes such as serum protein levels (10).

The findings of Negi et al. underscore the importance of adipocytokine pathways in RA. As reviewed by Müller-Ladner et al. (11), adipocytokines such as adiponectin are produced by synovial fibroblasts; are present in substantial amounts in the serum and joints of patients with inflammatory joint diseases; and can up-regulate pro-inflammatory pathways and RANKL-dependent osteoclast activation. In addition, serum adiponectin levels are associated with radiographic damage in RA (12). While the association between RA and cardiovascular (CV) disease is known, there are conflicting data on whether circulating adiponectin levels are associated with CV disease. Negi et al. found that the allele (risk) C of the rs255758 influences adiponectin levels in RA patients. There is a link between RA and ARL15, and between ARL15 and adiponectin; it is interesting to speculate that ARL15 variants may contribute to the CV disease phenotype in RA through the adiponectin pathway.

In addition to conventional association analysis, the investigators used a machine learning approach, namely support vector machines (SVM), to identify novel susceptibility genetic loci for RA. The SVM approach is a well-developed machine-learning technique used in computer science for pattern recognition which utilizes a set of training data in order to learn how to classify objects (13). As applied to the problem of disease risk prediction, the SVM approach attempts to find an optimal set of genetic variants that can accurately classify a set of data between cases and controls. This is a different question than asking if individual markers explain variation in case-control status. Therefore it is not surprising that 6 additional loci, not including ARL15, were found to be most informative for RA risk prediction in ANI. SVMs have been used to predict genetic susceptibility based on genotype data in complex diseases such as type II diabetes (14). Another interesting application of the SVM methodology would be to preselect known RA risk variants from other or all ethnicities and test their predictive ability in the ANI sample set. This analysis would then help to answer the question of the extent to which ANI and other ethnicities share RA risk factors.

Use of different statistical analytic approaches may lead to additional novel genetic associations of RA in ANI. For example, in the replication phase of the current study, at least 20 SNPs flagging the SNP with lowest p-values could have been used rather than 1 SNP per region to provide more detailed data. In addition, the threshold used for significance for multiple testing (5 × 10^-8) could have been lowered if the spectral decomposition method had been used to find the effective number of SNPs for association. Finally, during the quality control analysis, using a p value of 1 × 10^-7 as a threshold to exclude SNPs not in Hardy-Weinberg equilibrium (HWE) may be too stringent; all SNPs with deviation from HWE could have been included in the analysis with subsequent use of zoom plots to investigate clustering of genotypes and ensure correct calling by the algorithm.

In summary, the finding of an association of ARL15 with RA in ancestral North Indians illustrates the utility of such genetic studies in populations of different ethnicity. By performing larger analyses, including trans-ethnic meta-analyses, the totality of the genetic contributions to this disease may finally start to become apparent. These studies have important implications on the full delineation of the multiple pathways involved in RA. Genetic profiles may allow identification of subsets of patients with different dominant pathologic pathways. This stratification may in turn lead to tailored approaches to early diagnosis or targeted therapy. For example, subjects with variants in CTLA4, which is associated with RA, may be more (or less) likely to respond to abatacept (CTLA4Ig). Through incremental gains, the big picture of RA becomes clearer and hopefully will lead improved outcomes for this chronic disease.