Although autoimmune disorders (AIDs) include a wide array of different diseases and symptoms, they all share a common component: the fading of immune tolerance towards ‘self’ antigens. Susceptibility to AIDs collectively appears to be influenced by genetic factors. Even though the concordance rate of AIDs in monozygotic twins is relatively low, there is a marked increase in heritability in families, decreasing steadily with the degree of relatedness Citation[1–3]. In parallel with the revolutionary advantages of genetic research, the investigation of genetic influences of AIDs has intensified in recent years. Currently, reports describing genome-wide association studies (GWASs) in different AIDs are published very frequently; these aim to illuminate the genetic background of AIDs in a nonhypothetical manner. Hundreds of thousands to one million genetic markers are used to cover most of the common genetic variants in both cases and controls. Owing to the large number of markers and hence, tests, there is a stringent correction for multiple testing. As a result, only highly associated variants are considered truly associated. Intriguingly, when reviewing the NIH GWAS catalog, it was noticed that a substantial amount of susceptibility loci identified by these GWASs represent a common genetic background for AIDs. As expected, GWASs performed in systemic lupus erythematosus (SLE), rheumatoid arthritis (RA), celiac disease (CeD), multiple sclerosis, Type 1 diabetes (T1D), psoriasis (Ps) and systemic sclerosis (SSc) uniformally point towards regions in the MHC Citation[4,101]. More unexpected, however, was the finding that many non-HLA gene regions identified from GWASs overlap with AIDs. A recent review compared all the genes identified by GWASs on RA, T1D, CeD, multiple sclerosis, SLE and Ps with a significance level of at least p < 10-4, which was chosen to allow a comparison with moderately associated variants as well Citation[5]. This review identified a striking 71 non-MHC genes shared by at least two diseases, seven by three diseases and two by four diseases. The most promising genes are PTPN22, a tyrosine phosphatase strongly associated with T1D, RA, Graves’ thyroiditis, Addison’s disease and Crohn’s disease (CD) Citation[101]. The associated allele of this nonsynonymous single nucleotide polymorphism interrupts a proline motif that is necessary to interrelate with cytoplasmic tyrosine kinase. This might potentially interfere with the protein’s function as a negative regulator of T-cell activation Citation[6]. Recently, PTPN22 was also associated with SSc, in a candidate study approach Citation[7]. Furthermore TNFAIP3, a TNF-α-induced gene able to terminate NF-κB responses, IL-23 receptor (IL23R) and KIAA1109 were associated. IL23R has an important role in expansion and differentiation of proinflammatory TH17 cells. KIAA1109 is located within a region of high linkage disequilibrium in chromosome 4, so a precise interpretation of this finding remains obscure. Recent GWASs in SSc added several genes to this list, such as STAT4, which is involved in the interferon signaling pathway. This gene has also been found to be associated with SLE, CD and juvenile idiopathic arthritis Citation[101]. The other gene to be identified from this GWAS in SSc was CD247, part of the T-cell receptor ζ chain Citation[8]. This gene has previously been associated with SLE and was just below the significance threshold in two GWASs performed in RA and CeD (10-6 and 10-7, respectively) Citation[101].
Notwithstanding the importance of the identification of numerous genetic susceptibility loci for multiple diseases, identification of the real causative mutations and respective aberrant function is hampered by the combination of several factors. For instance, it appears counterintuitive to focus only on the highest associated variants from GWASs when investigating a multifactorial disease. Ideally, one would try to identify combinations of variants that place subjects at a higher risk for developing a certain AID. Unfortunately, the data gained from GWASs is of such quantity that current statistical analyses taking into account the additive effects of all variants are too immense to be calculated with the current state-of-the-art computer hardware. As an alternative, methods to study susceptibility pathways have been developed; in simple terms, one looks at which pathway contains the most variants with a certain significance level Citation[9]. The pathway with the most hits or the one with the best mean overall significance is the pathway of interest. This seems elegant, but it requires the investigator to have defined pathways in advance (e.g., using the Kyoto Encyclopedia of Genes and Genomes [KEGG] database). As our knowledge of pathways is not absolute, important observations could be missed, or wrong assumptions made. As a consequence of this, the biologic relevance of these findings is often unclear, and hence functional translation remains the stumbling block for GWAS results to find their connection with clinical practice. Fortunately, more effort is being put into directly identifying variants with functional effects by combining GWAS databases with genome-wide expression databases. However, especially in the shifting processes of immunology, certain polymorphisms may only play a role under specific immunologic stimulations in a certain cell subtype. For this reason, simply merging genome-wide genetic data with genome-wide expression data is not likely to solve AIDs; instead, this could be achieved by in vitro research addressing the impact of associated GWASs gene variants at multiple conditions in a variety of immune cell subsets.
Taken together, there appears to be a common genetic background for AIDs. Of course, some genes are found that seem to be exclusively associated with one disease (e.g., NOS2 in Ps and NOD2 in CD) Citation[101]. However, it is undeniable that most AIDs, despite their sometimes very different clinical presentations, share genetic features. This might imply that similar pathways are involved in these diseases. Unfortunately, the exact impact of genetic variants is not well understood for the majority of diseases and functional validation is generally lacking. Functional studies addressing these associations need to take into account the small effect sizes of most variants and the fact that they may only have an effect in a small selection of cell types, which hinders straightforward experiment set up and analyses. This leaves us with the question of what makes AIDs differ from each other? An explanation frequently addressed in the literature is the possibility that evolutionary younger and, consequently, rare variants have strong effects on driving susceptibility for AIDs. These variants would typically be missed with GWASs, since these are based on the common variation in the population. This hypothesis will be put to the test in the near future with next-generation sequencing becoming mainstream, replacing GWASs. A second explanation might be that environmental factors have a stronger effect than expected. These are able to impact on epigenetic regulation and might therefore be capable of differentiating AIDs on a genetically similar background. The rapidly expanding field of epigenetic research will hopefully help to answer these questions.
In conclusion, as a result of the effort that has been put into GWASs in AIDs, we are now aware that they are genetically quite similar and that fully differentiating genetic variants remain generally unidentified. To determine differentiating and causative factors, it is necessary to systematically investigate genomics, epigenetics and their functional impact in a combined endeavor.
Financial & competing interests disclosure
The authors have no relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript. This includes employment, consultancies, honoraria, stock ownership or options, expert testimony, grants or patents received or pending, or royalties.
No writing assistance was utilized in the production of this manuscript.
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Website
- A catalog of published genome-wide association studies www.genome.gov/26525384