In this paper, we present the first GWAS of anti–dsDNA autoantibody production in SLE. We have shown that SNPs in the MHC, STAT4, IRF5, and ITGAM regions are associated with anti–dsDNA + SLE. Only SNPs in the MHC and IRF5 met genome-wide significance threshold levels in the analysis of anti–dsDNA – SLE, with lower OR and larger p-values compared to their associations with anti–dsDNA + SLE. Furthermore, many of the previously identified SLE susceptibility loci showed differential associations between the 2 anti–dsDNA subgroups. Using a genetic risk score analysis, we found that SLE cases with a greater number of risk alleles were more likely to be anti–dsDNA +. These results suggest that genetic factors may have a greater influence in the development of anti–dsDNA + SLE as compared to anti–dsDNA – SLE.
The strongest association signals for both the anti–dsDNA + and anti–dsDNA – analyses were observed with MHC SNPs. Previous studies have shown that the strongest, most consistent genetic signals with SLE have been with the HLA-DR2 and HLA-DR3 MHC serotypes 
. While we confirm these findings, we also show that the HLA-DR3 association with SLE (as suggested by its tagSNP, rs2187668) is far stronger in anti–dsDNA + SLE as compared to anti–dsDNA – SLE or SLE itself. Thus, the HLA-DR3 allele may have a greater impact on the propensity to produce autoantibodies compared to SLE susceptibility generally. Although a similar finding was observed with HLA-DR2 (tagSNP rs9271366), the test of heterogeneity was not statistically significant, possibly due in part to decreased statistical power since the DR2 tagSNP is less common than the DR3 tagSNP (DR2 tagSNP minor allele frequency 0.182 in anti–dsDNA + SLE, 0.167 in anti–dsDNA – SLE, and 0.143 in healthy controls). Examination of other MHC SNPs in the case-only analysis indicates that other (non-HLA-DRB1
) loci may have associations with anti–dsDNA autoantibody production beyond the associations observed with SLE.
In addition to the HLA-DR3 tagSNP discussed above, the associations between the STAT4 and ITGAM SNPs and anti–dsDNA + SLE were stronger in magnitude than the associations with SLE per se in our datasets (). The smaller p-values seen in the associations for these loci with anti–dsDNA + SLE are especially striking given the substantially smaller sample size of this subgroup. Our results imply that STAT4, ITGAM, and HLA-DR3 may be more accurately considered “autoantibody propensity loci” rather than simply “SLE susceptibility loci” given their significant tests of heterogeneity (p<0.05). Using this criterion, three other previously identified SLE susceptibility loci may also be considered autoantibody propensity loci: KIAA1542, BANK1, and UBE2L3. In fact, these SNPs had no evidence of association with anti–dsDNA – SLE in this study (p>0.05). By characterizing these SNPs as autoantibody propensity loci, we identify a potential mechanistic role for these disease associations.
Are these autoantibody propensity loci associated with other autoantibodies? In rheumatoid arthritis (RA), other alleles of the HLA-DRB1
locus (collectively referred to as the “shared epitope”) are associated with anti-CCP autoantibody positivity 
. While a study of STAT4
(rs7574865) in an early RA inception cohort suggested an association with the anti-CCP autoantibody 
, others have not a shown strong association between this SNP and seropositivity in RA 
(rs2476601) has been shown to be more strongly associated with autoantibody positive RA 
. In our study, other SLE-related autoantibodies (anti-SSA, anti-SSB, anti-Sm, and anti-RNP) are more frequent in the anti–dsDNA + subgroup (), but correlations between anti–dsDNA and these other autoantibodies antibodies are modest, with Pearson correlation coefficients <0.2 (data not shown). Thus, additional studies are needed to further investigate whether these or other loci are associated with other autoantibodies.
Of note, not all of the previously identified SLE susceptibility SNPs showed differential associations between the anti–dsDNA subgroups. In fact, the OR for the SNPs in or near FCGR2A, OX40L, PXK, and UHRF1BP1 were strikingly similar between the anti–dsDNA + and anti–dsDNA – subgroups. These loci may represent more generalized SLE susceptibility loci, and their mode of conferring SLE disease risk is likely independent of anti–dsDNA autoantibody production. While PTPN22 (rs2476601), IRF5 (rs10488631), and PTTG1 (rs2431099) do not fulfill our criterion as autoantibody susceptibility loci, the results of the case-only analysis suggest that these loci may have a stronger effect in anti–dsDNA + SLE.
Interestingly, far fewer associations were seen in the anti–dsDNA – SLE analysis. Even in the joint analysis, which had the most statistical power, only 1 SNP outside of the MHC met our genome-wide significance threshold—rs10488631 in IRF5
. This finding may be explained by a number of different reasons. SNP associations for anti–dsDNA – subgroup may be weaker, and thus would require a larger sample of anti–dsDNA – SLE cases in order to be identified. Other types of genetic variation or non-genetic factors, such as environmental exposures 
, may have a stronger influence on susceptibility to anti–dsDNA – SLE. Lastly, the anti–dsDNA – subgroup may be more clinically heterogeneous or be comprised of individuals who develop SLE through different pathogenic (and genetic) mechanisms, thus decreasing our statistical power to identify genetic associations with this subgroup.
One limitation of this study is the potential misclassification of anti–dsDNA autoantibody status. This misclassification may have occurred because the anti–dsDNA autoantibody was assessed by different assays between the participating case collections, and a patient's anti–dsDNA status can vary over the disease course. However, this misclassification would bias our findings of differences between anti–dsDNA + and anti–dsDNA – SLE towards the null. Moreover, sensitivity analyses performed using the available longitudinal data showed consistent ORs, suggesting that the potential misclassification did not greatly influence our results. A second limitation is that all participants were of European descent. Limiting this study to those of European descent minimizes confounding due to genetic differences arising from differences in ethnicity. Future efforts should study non-European populations given their increased incidence of SLE 
In summary, this GWAS of anti–dsDNA autoantibody production in SLE shows that there are more, and stronger, genetic associations in anti–dsDNA + SLE compared to anti–dsDNA – SLE. Previously identified SLE susceptibility loci such as STAT4, ITGAM, KIAA1542, BANK1, and UBE2L3 are more strongly associated with anti–dsDNA + SLE and may confer disease risk through their role in autoantibody production. Weaker associations in anti–dsDNA – SLE may suggest that other types of genetic variation or non-genetic factors have a greater impact on disease risk. Lastly, focusing genetic studies on clinical disease characteristics decreases the heterogeneity that could cloud association results and may provide greater insight into pathogenic disease mechanisms.