The human immune system defends the body against microbes and other threats. However, if this process goes wrong the immune system can attack the body’s own healthy cells, which can lead to serious autoimmune diseases.

Systemic lupus erythematosus (SLE) is an autoimmune disease in which immune cells often attack internal organs – including the kidneys, nervous system and heart. Over the past decade, multiple genes have been linked with an increased risk of SLE. However, it is largely unknown how the sequences of these genes differ between individuals with SLE and healthy individuals, and the precise changes that lead to an increased risk of SLE are also not clear.

Now, Raj, Rai et al. have determined the genetic sequences of over 700 people with SLE and over 500 healthy individuals and looked for differences that influence susceptibility to the disease. The vast majority of differences were discovered in stretches of DNA that regulate the expression of nearby genes, rather than in DNA that encodes the structures of proteins. Notably, extensive differences were found in a region of the human genome that regulates the production of proteins called Human Leukocyte Antigen class II molecules; which are known to play a critical role in activating the immune system. Raj, Rai et al. found that slight changes to the regulatory DNA sequences resulted in an overabundance of these proteins, which led to a hyperactive immune system that is strongly associated with SLE.

Future studies could now ask if the changes to the regulatory DNA sequences highlighted by Raj, Rai et al. increase susceptibility to other autoimmune disorders as well. It may also be possible to use the increased understanding of how the immune system is regulated to develop new ways to minimize the rejection of organ transplants.

Systemic Lupus Erythematosus (SLE) is a complex autoimmune disease resulting from a profound loss of immune tolerance to self-antigens (Olsen and Karp, 2014; Theofilopoulos, 1995a; 1995b; Fairhurst et al., 2006). The disease initiates with the production of autoantibodies against a spectrum of self-antigens (typically >10 in SLE patients), focused on nucleic acids and nucleic-acid-associated proteins. Disease pathology begins with the deposition of immune complexes in various target tissues, leading to the activation of inflammatory effector mechanisms that damage critical organ systems. Patients with SLE can present with combinations of symptoms, including skin rashes, oral ulcers, glomerulonephritis, neurologic disorders, severe vasculitis, and a distinct form of arthritis (Tsokos, 2011). This extensive heterogeneity in clinical presentation presumably reflects variations in the sites of immune complex deposition and induced inflammation among patients, but also suggests that SLE may be a collection of related diseases, rather than a single pathogenic process. A generalized loss in immune tolerance by the humoral immune system and the aberrant activation of inflammatory effector mechanisms at the sites of immune complex deposition, however, are consistent features of SLE.

Susceptibility to SLE is caused by a combination of genetic and environmental factors (Fairhurst et al., 2006; Harley et al., 2009; Deng and Tsao, 2010; Rai and Wakeland, 2011). Current thought postulates that a collection of common risk alleles mediates the development of an autoimmune-prone immune system which, when coupled with poorly-defined environmental triggers, becomes dysregulated, leading to the development of autoantibodies and the initiation of disease pathologies. Genome-wide association analyses (GWAS) have identified more than 50 SLE risk loci to date, indicating that susceptibility is quite polygenic (Harley et al., 2009; Nath et al., 2008; Harley et al., 2008; Kim et al., 2012; Graham et al., 2006; 2008; 2009; Hom et al., 2008; Gateva et al., 2009; Relle et al., 2015). A variety of candidate genes have been identified within these risk loci, including: HLA-DR and HLA-DQ class II alleles, IRF5, ITGAM (CD11b), STAT4/STAT1, TNFAIP3, and BLK. The functional effects or 'endophenotypes' that these disease genes contribute to the disease process have not been clearly delineated.

GWAS utilize a dense array of single nucleotide polymorphisms (SNP) to map the positions of risk loci within the human genome to relatively small segments, termed linkage disequilibrium (LD) blocks (typically < 200 Kb in length). Within these LD blocks, recombination is infrequent and polymorphisms form stable combinations or 'haplotypes' that persist within populations for extended periods (Balding, 2006; de Bakker et al., 2005; Frazer et al., 2007). Disease associated 'tagging' SNPs are postulated to be imbedded in specific haplotypes that contain the functional variations that impact disease susceptibility. The characteristics of these functional variations and the endophenotypes that they contribute to disease processes are a poorly described aspect of common disease genetics.

Population sequencing studies have identified extensive variations in both the coding and non-coding regions of the human genome (Abecasis et al., 2010; 2012; Barreiro and Quintana-Murci, 2010; Laval et al., 2010). The ENCODE consortium has investigated the functional characteristics of non-coding regions in the human genome in detail and have defined a plethora of regulatory elements impacting transcription levels and cell lineage differentiation, including histone associated regions, transcription factor (TF) binding sites, and DNase hypersensitivity clusters (Gerstein et al., 2012). A parallel series of investigations by several research groups have used expression quantitative trait locus (eQTL) analysis to identify common polymorphisms that quantitatively impact gene transcription (Sheffield et al., 2013; Vernot et al., 2012; Dunham et al., 2012; Bernstein et al., 2010; Cookson et al., 2009; Fairfax et al., 2012; 2014; Gilad et al., 2008). These findings, coupled with data indicating that many disease-tagging SNPs are localized to non-coding regulatory regions (Maurano et al., 2012), suggest that the causal variants for common disease risk alleles may impact regulatory processes, rather than protein structure.

Here we describe the targeted, deep sequencing of 28 risk loci for SLE in a population of SLE patients and controls. Our sequencing study identified 124,552 high quality sequence variants contained in these risk loci among 1349 Caucasian cases (773) and controls (576). Detailed analysis of sixteen of these SLE risk loci demonstrate that haplotypes of functional variations in tight LD with SLE- tagging SNPs often impact the expression of multiple genes, resulting in the association of several transcriptional variations with SLE risk haplotypes. Notably, multiple SLE risk haplotypes within the HLA-D region were found to coordinately upregulate HLA-DR, -DQ and a variety of other genes within the antigen processing and presentation pathways for HLA class I and class II molecules. These results reveal a new functional diversification mediated by HLA-D polymorphisms and provide important insights into the molecular mechanisms by which HLA-D and other SLE risk loci potentiate disease.

These analyses provide a comprehensive assessment of the genomic variations associated with SLE disease alleles. We identified 345 regulatory variations impacting gene transcription within these loci that exhibited stronger disease-associations than previously identified GWAS tagging SNPs (Figure 3B). Detailed analyses of the allelic architecture at these loci revealed that SLE disease alleles are haplotypes composed of multiple functional variations and that these variations often modulate several endophenotypes. This architecture is modeled in Figure 14A, which depicts the manner in which several functional variants in tight LD with GWAS tagging SNPs mediate transcriptional variations at multiple adjacent genes. As shown, the functional haplotypes identified in our analyses capture all of these causal variants within the LD block, which leads to the identification of a peak risk haplotype with increased disease association. Our analyses identified variants impacting multiple transcriptional changes at nine of the sixteen SLE risk loci, indicating that this level of complexity is prevalent among SLE risk loci. In this regard, we could only utilize our own MDM eQTL database and a few public eQTL databases for immune cell lineages in this analysis (datasets for monocytes, PBMC, and LBL are currently accessible, (Fairfax et al., 2014; Raj et al., 2014; Westra et al., 2013) and it is quite likely that these haplotypes will be found to have additional effects as more datasets become available.

Figure 14

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Figure 14B presents the odds ratios for disease obtained with GWAS-defined tagging SNPs (Harley et al., 2008; Graham et al., 2008; Adriantoet al., 2011; Taylor et al., 2011) and peak risk haplotypes for the sixteen SLE loci analyzed. As shown, the odds ratio for disease obtained for the peak risk haplotype at each locus was consistently higher than that of the tagging SNP, leading to an average increase of 17% in odds of disease overall (Table 2 and illustrated in Figure 14B). These results support the presence of most or all of the causal variations for disease susceptibility within the identified peak risk haplotypes for each locus. Interestingly, we identified both protective and risk haplotypes with nominal statistical significance (p<0.05) at fourteen of the sixteen risk loci analyzed, suggesting that both types of disease alleles are prevalent and contribute to population risk. MJ analyses consistently found peak risk and protective haplotypes at opposite ends of the network, which suggests that the combined effects of multiple regulatory variations may additively impact disease associations. Consistent with this, HLA-D, STAT4, IRF5, and CCL2-CX3Cl1 all have multiple haplotypes with different disease associations, suggesting that a spectrum of disease alleles with different impacts on susceptibility may occur at highly variable risk loci. However, some caution is appropriate when interpreting the significance of multiple intermediate risk haplotypes within a network, in that sample numbers for many haplotypes were often small. Consequently, a larger sample of SLE patients will be required for the detailed assessment of the disease risk attributable to all of the prevalent haplotypes at SLE risk loci.

These haplotypes represent stable polymorphisms within the EA population, with six or fewer haplotypes accounting for about 90% of the LD block regulatory sequences segregating in the EA population at individual risk loci (Table 2 and Figures 10–13). We assessed this in more detail by determining the frequencies of the major risk haplotypes for HLA-D, STAT4, IRF5, ITGAM, and UBE2L3 in 2504 individuals derived from 26 global ethnic populations sequenced in the 1000 Genomes project (Abecasis et al., 2010; 2012). As shown in Figure 14C, these five risk haplotypes are present at variable frequencies within all of the European, South American, and South Asian populations. However, they are much less frequent in the African populations sampled and, with the exception of UBE2L3, absent from the East Asian populations. This suggests that additional haplotypes will be identified during the analysis of specific ethnic groups as previously shown (Kim-Howard et al., 2014). In addition, although these five SLE risk haplotypes are predominantly found in European populations and populations with significant European admixture, they are also detectable with low frequencies in some African populations. Based on this distribution, it is likely that these haplotypes arose prior to human global colonization and that they were present with divergent frequencies in the ancestral founding populations of modern ethnic groups.

This dataset can also be used to assess the percentage of population disease risk that is attributable to the combined effects of all of these risk loci As tabulated in Table 2, the cumulative risk associated with the GWAS tagging SNPs for all sixteen loci sums to 6.04 fold, while the same value for all peak risk haplotypes is 8.8, indicating that improved resolution of disease alleles increases the disease risk and proportion of 'heritability' that is associated with these common disease alleles. Assuming that the contribution of all risk loci for SLE sum to 29 (Alarcon-Segovia et al., 2005; Deapen et al., 1992), then the sum of these sixteen loci would account for about one third of the genetic heritability for SLE. In this regard, a contentious debate concerning the contribution of 'common' (MAF > 0.05) versus 'rare' (MAF << 0.05) disease risk alleles to the overall heritability of common diseases has persisted among investigators in complex phenotype genetics for several years (Raychaudhuri, 2011; Cirulli and Goldstein, 2010; Manolio et al., 2009; Pritchard and Cox, 2002). Although it is clear that rare alleles contribute to disease susceptibility in small subsets of patients (Hunt et al., 2013; Lee-Kirsch et al., 2007; Tang et al., 2014; Mitchell et al., 2002), recent analytical studies have firmly established that common disease alleles are responsible for the bulk of the heritability for autoimmune diseases (Yang et al., 2010; Visscher et al., 2010; Stahl et al., 2012). It is likely that 'missing' heritability predominantly reflects an extensive genetic heterogeneity that underlies many common diseases.

An alternative method to measure the cumulative risk attributable to a specific collection of risk loci is via the calculation of population attributable risk (PAR) (Zheng et al., 2008; Bruzzi et al., 1985; Rockhill et al., 1998a; 1998b; Mezzetti et al., 1998; Natarajan et al., 2007; Claus et al., 1996; Kraft et al., 2009; Pepe et al., 2004). This calculation utilizes the odds ratio for disease and the risk allele population frequency to calculate a weighted risk value for each locus and then combines them to assess their contribution to genetic risk for the population as a whole. As shown in Figure 14B and tabulated in Supplementary file 3B, the peak risk haplotypes at these sixteen risk loci account for 66% of the population attributable risk for SLE within this EA cohort (Supplementary file 3A and 3B). PAR and estimates of 'heritability' differ in that PAR calculations do not assume a specific level of population genetic risk (i.e. 29), but instead simply calculates the proportion of risk that cannot be accounted for by the variables assayed within the population studied, and thus determining the proportion of risk that is attributable to tested factors. The calculated PAR in this analysis indicates that these sixteen loci contribute a significant proportion of disease risk within our population. However, a larger cohort and broader list of risk loci will be essential to estimate genetic risk in all populations and account for a larger proportion of SLE heritability.

Our results provide as system using sequence analyses that can efficiently and accurately identify disease risk alleles within large populations. Further, we define a path forward for the development of useful genetic tools for assessing disease risk. The next phase of genetic analyses of autoimmune disease will involve assessing the functional properties of these disease alleles, sorting out their interactions during disease development, and developing analytical tools for the accurate quantitation of genetic risk for disease in individual genomes (Ray and Hacohen, 2015; Ghodke-Puranik and Niewold, 2015; Lewi et al., 2015; Wang et al., 2015; Mohan and Putterman, 2015).

HLA-D polymorphisms, antigen presentation pathways, and autoimmune disease

The most intriguing result of our sequence analyses is the discovery of a strong association between SLE susceptibility and HLA-D polymorphisms that regulate HLA class II gene expression. The HLA-D region is consistently a potent susceptibility locus in autoimmunity and significant effort has focused on defining the molecular mechanisms that mediate autoimmunity in the context of specific HLA-D class II alleles (Morris et al., 2012; 2014; Armstrong et al., 2014; Kim et al., 2014; Niu et al., 2015; Graham et al., 2007; Cruz-Tapias et al., 2012; Todd et al., 1987). Multiple genetic studies have identified coding variations in the peptide binding sites of MHC class II molecules as key genetic components of the disease associations, strongly supporting the hypothesis that allelic variations in the antigen presentation process underlie autoimmune disease (Kim et al., 2014; Morris et al., 2014; Fernando et al., 2012; Raychaudhuri et al., 2012). The dominant paradigm has been that the peptide binding regions of disease-associated HLA class II alleles have unique peptide binding properties that present a novel spectrum of self-peptides or modified self-peptides in a manner capable of eliciting autoimmunity. Solid data supporting this mechanism have been developed by decades of experiments, notably for insulin peptides in autoimmune diabetes (Unanue, 2014) However, many studies have found that multiple self-antigens are recognized by T cells clones isolated from the earliest stages of disease development, suggesting that HLA-D associated autoimmunity is initiated against multiple self-antigens by a heterogeneous T cell response. Notably, SLE patients have a profound breach in immune tolerance and typically produce autoantibodies binding more than ten different self-antigens, with the diversity of autoantigens recognized increasing as individuals approach disease diagnosis (Olsen and Karp, 2014; Arbuckle et al., 2003; Li et al., 2005). Further, multiple HLA class II DR and DQ alleles are associated with SLE susceptibility, which indicates that HLA class II alleles with highly divergent peptide binding properties are capable of promoting disease development. In this regard, classic studies of the association of DR2 and DR3 with susceptibility to SLE have shown that DR2/DR3 heterozygotes are more strongly associated with disease susceptibility than the individual haplotypes (Graham et al., 2007). Taken together, these data suggest that SLE is associated with an extensive array of divergent HLA class II alleles that would be predicted to present a diverse array of self-peptides.

Our data indicate that all of the HLA-DR and -DQ alleles that are strongly associated with susceptibility to SLE are in strong LD with XL9 regulatory haplotypes that increase HLA class II gene transcription. Boss and co-workers have shown that XL9 contains CTCF elements that interact with cohesion molecules and other chromatin factors to assemble a transcriptional complex that brings multiple HLA class II promoters into close proximity with an array of transcription factor binding sites (Majumder et al., 2006; 2008). The XL9 model in Figure 15A.i, which is adapted from a model presented by Majumder et al. (2008), illustrates the key role of chromatin configuration in the coordinated transcription of HLA class II genes. Consistent with the chromatin structure effects of XL9, our data indicate that transcriptional variations are chromosome specific in HLA-D heterozygotes, with polymorphisms in the XL9 regulatory haplotype modulating transcription of DR and DQ genes in a cis-specific fashion. This indicates that the level of transcription dictated by XL9 will be specific for the adjacent HLA-DR or DQ allele, thus making expression levels an additional allele-specific facet of HLA-D class II molecules. Classic studies have demonstrated allele-specific variations in the expression levels of MHC class II molecules in murine MHC heterozygotes and shown that these levels strongly impacted the stimulation of antigen-specific T cells in autoimmune disease models (Ridgway et al., 1998). Our data suggest that these early studies revealed an important facet of MHC diversity that strongly impacts the development of autoimmunity.

Figure 15

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XL9 contains the peak association signal with SLE in our sequencing dataset, indicating that these regulatory haplotypes play an important role in the development of SLE autoimmunity. We suspect that the failure to detect XL9 associations in previous GWAS reflects the low frequency of many of the associated variants and the paucity of SNPs in this region on the commonly utilized SNP typing arrays (see Figure 5.iii & iv). Our data demonstrate that individuals homozygous for XL9 HAP3 (risk) variations have more than two-fold higher surface expression for HLA-DR and DQ molecules at baseline which increases to 4-fold after stimulation with TLR-ligand on monocyte-derived dendritic cells than HAP1 (protective) homozygotes (Figure 7). This increase is maintained during the maturation of these dendritic cells via TLR7/8 stimulation, thus supporting the functional significance of these transcriptional variations to immune mechanisms known to impact immune response activation. Potent polymorphisms in the binding motif of the IRF4 transcription factor are in the final MJ branch to the SLE-associated XL9 HAP2 and HAP3 haplotypes and it is likely that these two polymorphisms are causal and contribute significantly to this expression change. Recent studies by Vander Lugt et al (Vander Lugt et al., 2014) have demonstrated that IRF4 is a key component of the transcriptional regulation of HLA class II molecules in dendritic cells and that upregulation of MHC class II molecules strongly promotes susceptibility to autoimmunity in an animal model. Based on these data, we hypothesize that the XL9-mediated increase in surface expression of HLA-DR and DQ in dendritic cells is predominantly responsible for the association of XL9 regulatory haplotypes with susceptibility to SLE.

The extensive diversification of HLA-D regulatory elements has implications well beyond the association of these polymorphisms with SLE. HLA class II molecules are expressed on a variety of immune cell lineages, including monocytes, macrophages, dendritic cells, B cells, activated T cells and thymic epithelial cells. Expression levels are tightly controlled by a variety of transcription factors unique to these cell lineages and their expression impacts a variety of functional processes in the immune system (Cresswell, 1994; Krawczyk et al., 2004; Steimle et al., 1994; Reith et al., 2005). For example, increased surface expression of class II molecules is an essential event in the maturation of dendritic cells, in that higher surface expression is crucial to the increased capacity of mature dendritic cells to effectively present antigens to naïve T cells (Cella et al., 1997a; 1997b; Banchereau and Steinman, 1998; Pierre, 1997). As tabulated in Figure 15A.ii, twenty-three transcription factor binding sites in XL9 and fourteen in the DQB1 5’ segment are strongly modified by the extensive variations present within these genomic segments. Overall, we identified a total of 1651 functional variants in XL9 and 1912 functional variants in the DQB1 5’ segment, indicating that the regulatory characteristics of these transcriptional complexes will be highly diversified among HLA-D haplotypes. This level of polymorphism is readily comparable to that observed in the codons for the peptide binding regions of the HLA class II molecules. Interestingly, as shown in Figure 15B, both the protective and risk haplotypes that we identified for the entire HLA-D region are present with varying frequencies throughout the global population. These findings are consistent with the characteristics of other highly polymorphic regions of HLA, including the HLA-D class II coding alleles, and indicate that these regulatory haplotypes have evolved together with the HLA class II coding regions over long periods. Given all of these characteristics, we propose that these regulatory variations represent an essential and highly selected characteristic of the diversification of HLA-D that strongly impacts a variety of immune functions.

These regulatory HLA-D haplotypes probably have divergent effects on the expression levels of HLA class II molecules in different cell lineages. That is, XL9 HAP3 clearly upregulates HLA class II expression in the myeloid lineage, however, that may not be true for HAP3 B cells, T cells, or thymic epithelial cells. In addition, XL9 variants impact the function of a variety of transcription factors whose activity is modified by innate system activation signals in specific cell lineages, indicating that modulations of HLA class II expression levels by activation signals may also be differentially associated with individual haplotypes. Taken together, these features indicate that the regulatory polymorphisms identified in this study will have multifaceted effects on the adaptive immune response and probably result in a significant diversification in the functioning of HLA class II antigen presentation among HLA haplotypes.

Finally, a variety of eQTL studies have identified the HLA complex as a 'master' regulatory complex that impacts the expression of genes throughout the genome (Fehrmann et al., 2011; 2012). An analysis of the available eQTL databases identified a total of 72 genes whose transcription levels are reported to be modulated by variations in the HLA-D region. Many of these eQTL targets are encoded in other segments of the HLA complex, as well as 35 that are located on other chromosomes. A variety of immune system genes are included in this list and Figure 16 illustrates the pattern of up and down expression that distinguishes the HAP3 risk haplotype from the HAP1 and HAP6 protective haplotypes. As shown, essentially all of the HLA class II molecules and a variety of gene products involved in antigen processing, peptide loading, and surface expression are up-regulated by the HAP3 risk haplotype. This result illustrates the extensive functional variations that are associated with a single HLA-D risk haplotype within the EA population. Whether these eQTLs reflect the formation of remarkably intricate transcriptional complexes, or (more likely) very strong LD throughout the HLA complex remains to be determined. However, these results indicate that regulatory polymorphisms in HLA-D affect a plethora of immune system genes that are involved in various pathways of the adaptive immune system. It is likely that these regulatory variations are an integral element of the functional diversification of HLA and that they will ultimately be found to modulate functions throughout the immune system.

Figure 16

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