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Abstract

  1. Top of page
  2. Abstract
  3. PATIENTS AND METHODS
  4. RESULTS
  5. DISCUSSION
  6. AUTHOR CONTRIBUTIONS
  7. Acknowledgements
  8. REFERENCES

Objective

To determine whether genetic substructure in European-derived populations is associated with specific manifestations of systemic lupus erythematosus (SLE), including mucocutaneous phenotypes, autoantibody production, and renal disease.

Methods

SLE patients of European descent (n = 1,754) from 8 case collections were genotyped for >1,400 ancestry informative markers that define a north–south gradient of European substructure. Using the Structure program, each SLE patient was characterized in terms of percent Northern (versus percent Southern) European ancestry based on these genetic markers. Nonparametric methods, including tests for trend, were used to identify associations between Northern European ancestry and specific SLE manifestations.

Results

In multivariate analyses, increasing levels of Northern European ancestry were significantly associated with photosensitivity (Ptrend = 0.0021, odds ratio for highest quartile of Northern European ancestry versus lowest quartile [ORhigh–low] 1.64, 95% confidence interval [95% CI] 1.13–2.35) and discoid rash (Ptrend = 0.014, ORhigh–low 1.93, 95% CI 0.98–3.83). In contrast, increasing levels of Northern European ancestry had a protective effect against the production of anticardiolipin autoantibodies (Ptrend = 1.6 × 10−4, ORhigh–low 0.46, 95% CI 0.30–0.69) and anti–double-stranded DNA autoantibodies (Ptrend = 0.017, ORhigh–low 0.67, 95% CI 0.46–0.96).

Conclusion

This study demonstrates that specific SLE manifestations vary according to Northern versus Southern European ancestry. Thus, genetic ancestry may contribute to the clinical heterogeneity and variation in disease outcomes among SLE patients of European descent. Moreover, these results suggest that genetic studies of SLE subphenotypes will need to carefully address issues of population substructure based on genetic ancestry.

Systemic lupus erythematosus (SLE) is the prototypic systemic autoimmune disease and can affect virtually any organ system. The overall prevalence of SLE is ∼1 in 2,000 individuals, with a marked predominance in women (female:male ratio of 6–10:1). The peak incidence occurs between ages 15 years and 40 years (1). Studies have shown that the prevalence of SLE manifestations varies between ethnic groups, with higher rates of severe disease manifestations in non-European populations. For example, higher rates of renal disease have been noted in Asians (2, 3), African Americans (4–6), and Hispanics (6). In contrast, higher rates of photosensitivity have been observed in SLE subjects of European descent (7). These differences in SLE manifestation rates are presumably due, in part, to differences in genetic factors between these continental groups.

Genetic population structure arises from the genetic differences between the major continental ethnic groups (e.g., European, African, Amerindian, East Asian, and South Asian) and can lead to confounding in genetic association studies if cases and controls differ in ethnic background. In this situation, biased associations with genetic polymorphisms that are not related to disease can be observed when the polymorphisms have different frequencies in the continental ethnic groups comprising the cases and controls (8). An example of this type of confounding has been observed between a human IgG haplotype and diabetes mellitus among Pima Indians. Initially, the Gm3;5,13,14 haplotype was found to be protective against diabetes mellitus in this group. However, this haplotype was determined to be a marker for European ancestry, and Europeans have a lower prevalence of diabetes mellitus compared with the Pima Indians. Thus, the association between this haplotype and diabetes disappeared when only Pima Indians without any European ancestry were studied (9).

Recent advances in human population genetics have led to the identification of the genetic polymorphisms whose frequencies differ between the continental ethnic groups. These markers, termed ancestry informative markers (AIMs), can be used to identify major continental contributions to an individual's ancestry. AIMs have also been used to study admixture between 2 or more major continental populations. More recently, genetic differences within the same major continental group (called population substructure) have also been identified. Studies of European-derived populations have shown clear evidence of substructure, with the largest genetic distinction occurring along a north–south (or northwest–southeast) gradient (10–13). As defined in these studies, Scandinavian, Western European, Eastern European (Poland and Ukraine), and Central European (German) are considered Northern European, whereas Spanish, Portuguese, Italian, Greek, and Ashkenazi Jewish are considered Southern European. (Of note, for Ashkenazi Jewish, the country of origin has been shown to be irrelevant [10,12].) Admixed individuals (e.g., 2 grandparents of Italian origin and 2 grandparents of Irish origin) appear in the intermediate region of this gradient. These studies have also identified Eurostructure AIMs (ESAIMs), which can be used to identify European population substructure in genetic studies and to assess the contribution of Northern and Southern European ancestry for a given individual (10, 12).

Differences in SLE manifestations among SLE patients from different continental groups are likely due, in part, to the genetic differences between these groups (population structure). Therefore, we hypothesized that differences in SLE manifestations among SLE patients from the same major continental group may be due to differences in genetic ancestry within that group (population substructure). To examine this hypothesis, we conducted this study to determine whether population substructure among SLE patients of European descent, specifically Northern versus Southern European ancestry, is associated with particular subphenotypes of SLE.

PATIENTS AND METHODS

  1. Top of page
  2. Abstract
  3. PATIENTS AND METHODS
  4. RESULTS
  5. DISCUSSION
  6. AUTHOR CONTRIBUTIONS
  7. Acknowledgements
  8. REFERENCES

Subjects and clinical data.

SLE patients (n = 1,891) were obtained from the following independent case collections: the University of California, San Francisco (UCSF) Lupus Genetics Project (n = 579) (14), the Autoimmune Biomarkers Collaborative Network (ABCoN) (n = 312) (15), the Pittsburgh Lupus Registry (n = 297) (16), the University of Minnesota (UMN) SLE cohort (n = 251) (17), the Lupus Family Registry and Repository at the Oklahoma Medical Research Foundation (OMRF) (n = 231) (18), the Multiple Autoimmune Disease Genetics Consortium (MADGC) (n = 103) (19), the University of California, Los Angeles (UCLA) (n = 81) (20), and the collection of European SLE patients based at Uppsala University (n = 37) (21–23). All subjects were of self-described European descent and confirmed as having SLE by fulfilling ≥4 of the American College of Rheumatology (ACR) classification criteria for SLE (24) as determined by medical record review. The Institutional Review Board of all investigative institutions approved these studies, and all participants provided written informed consent.

Phenotypes of interest for this study were the 11 SLE manifestations that comprise the ACR classification criteria (24): malar rash, discoid rash, photosensitivity, oral ulcers, serositis, arthritis, renal disorder, neurologic disorder, hematologic disorder, immunologic disorder, and positive findings of antinuclear antibodies (ANAs). In addition, we studied 6 autoantibodies associated with SLE: anti–double-stranded DNA (anti-dsDNA), anti-SSA/Ro, anti-SSB/La, anti-Sm, anti-RNP, and anticardiolipin autoantibodies. Clinical data on the SLE manifestations in all subjects were obtained from medical record review performed at the individual institutions. Autoantibody status was determined by medical record review and/or serologic testing of banked serum. A subject was considered positive for an autoantibody if he/she had a positive test result for that autoantibody documented at least once. Additional information on sex and disease duration was collected, if available.

Genotyping and estimation of European ancestry.

The primary genotyping data for this study were obtained from 2 genome-wide association scans of SLE patients (25, 26). The UCSF, ABCoN, Pittsburgh, and MADGC collections were genotyped using the Illumina HumanHap500 BeadChip, as described previously (26). The UMN, OMRF, UCLA, and Uppsala collections were genotyped on the Illumina HumanHap300 BeadChip, as described previously (25).

Genotyping data for a minimum of 1,400 AIMs were obtained for all subjects. These AIMs were informative for both continental ancestry and for Northern or Southern European substructure (12, 27). Subjects and AIMs were removed from the analysis when >10% of genotypes were missing or when they did not meet the Hardy-Weinberg equilibrium criterion (P < 1 × 10−5), both being common quality control criteria for genotyping data. A set of 128 AIMs was used to estimate the percent European ancestry, using the model- based nonhierarchical clustering approach applied in the Structure program (version 2.1; http://pritch.bsd.uchicago.edu/structure.html), as previously described (27). For those subjects with >90% European ancestry (n = 1,754), another set of 1,250 north–south ESAIMs was used to estimate the percent Northern European versus percent Southern European ancestry (details on the final sample sizes of each case collection are available on the Web site http://pages.medicine.ucsf.edu/lupus). This additional marker set was derived from a panel of 1,440 north–south ESAIMs (12) that were common to all genotype sets and met quality filters.

For continental ancestry, population structure was examined using the Structure program, version 2.1 (28, 29). Each Structure analysis was performed without any prior population assignment, included the same parameters as those in previously described analyses (12), and used 100,000 replicates and 100,000 burn-in cycles. The analyses included 80 subjects from each of the following continental or subcontinental groups: European, Amerindian, East Asian, African, and South Asian (27). Four independent runs demonstrated nearly identical results under these parameters.

For the European substructure analysis, the same parameters as those used for the population structure analysis were utilized in Structure runs, with the exception that the numbers of replicates and burn-in cycles were reduced to 50,000 each. Runs included 150 Southern European subjects and 150 Northern European subjects, as determined from previous studies (12). Four independent runs demonstrated nearly identical results under these parameters.

Statistical analysis.

Associations between the SLE phenotypes of interest and the primary predictor, percent Northern European ancestry, were assessed initially by Spearman's rank correlations, since the primary predictor was not normally distributed (see Figure 1). P values for the Spearman's rank correlation coefficients were determined using Monte Carlo permutation testing. Each analysis consisted of 10,000 repetitions and permuted the SLE phenotype of interest.

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Figure 1. Histogram showing the distribution of percent Northern European ancestry in all study participants (n = 1,754).

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Phenotypes whose correlations with percent Northern European ancestry were considered significant, at a P value of less than 0.05 by permutation testing, were further analyzed using multivariate, nonparametric techniques. Since the distribution of percent Northern European ancestry was highly skewed, we transformed this variable into a 4-level ordinal variable based on quartiles of percent Northern European ancestry. For each phenotype of interest, multivariate logistic regression analyses, adjusted for sex, were performed for each quartile and each case collection. Of note, disease duration was not available for the UMN case collection (n = 241) or most of the Uppsala case collection (n = 30). Therefore, disease duration (dichotomized at the median number of years of disease) was also included in the multivariate models when it was a statistically significant term (P < 0.05). When disease duration was not a statistically significant term, it was not included in the multivariate models, in order to maximize the available sample size.

Odds ratios (ORs) for a particular quartile were then combined across the case collections using the Cochran-Mantel-Haenszel method. Score tests were used to assess evidence of trend across the quartiles. We used these nonparametric methods, since they are more conservative and do not rely on the linearity assumptions used in regression models.

Of note, the phenotypes investigated in this study are not independent. For example, the immunologic disorder criterion is based on the subject testing positive for 1 of the following 3 autoantibodies included in this study: anti-dsDNA, anti-Sm, or the anticardiolipin autoantibody. In addition, subjects who produce anti-SSA/Ro autoantibodies are more likely to produce anti-SSB/La autoantibodies (Pearson's r = 0.56, P < 0.00005), and subjects who produce the anti-dsDNA autoantibody are more likely to have renal disease (Pearson's r = 0.24, P < 0.00005). Therefore, principal components analysis was performed for the 11 ACR criteria as well as the 6 autoantibodies to determine whether Northern European ancestry was associated with an unmeasured factor underlying the correlated phenotypes.

Since 17 phenotypes (11 ACR criteria and 6 autoantibodies) were analyzed, the issue of multiple testing must be considered. However, as described above, these phenotypes are not independent. Given the lack of independence among phenotypes, a simple Bonferroni correction, calculated by dividing the alpha level of 0.05 by 17 to yield an alpha value of 0.0029, is clearly overly conservative; however, the unadjusted alpha level of 0.05 is clearly liberal. We therefore chose to present unadjusted P values, so that they may be directly interpreted by the reader.

All statistical analyses were conducted using Stata/SE version 9.0 (StataCorp, College Station, TX).

RESULTS

  1. Top of page
  2. Abstract
  3. PATIENTS AND METHODS
  4. RESULTS
  5. DISCUSSION
  6. AUTHOR CONTRIBUTIONS
  7. Acknowledgements
  8. REFERENCES

The clinical characteristics of the 1,754 SLE patients analyzed in this study are provided in Table 1, including the overall prevalence of the 11 ACR criteria and the autoantibody frequencies (comprising the SLE phenotypes under study). As expected, >90% of the subjects were women. The median age at the onset of SLE was 33 years, and the median disease duration in the patients was ∼7 years. (The frequency of the 11 ACR criteria and autoantibodies in each case collection separately is available on the Web site http://pages.medicine.ucsf.edu/lupus).

Table 1. Demographic and clinical disease characteristics of the study population (n = 1,754)*
  • *

    IQR = interquartile range; ACR = American College of Rheumatology; SLE = systemic lupus erythematosus; anti-dsDNA = anti–double-stranded DNA.

  • Since autoantibody data were not available on all subjects, the total number of subjects for whom data were available is provided.

Female, no. (%)1,652 (94)
Age at diagnosis, median (IQR) years33 (24–43)
Disease duration, median (IQR) years7.3 (3–14)
ACR classification criteria for SLE, no. (%) 
 Malar rash965 (55)
 Discoid rash176 (10)
 Photosensitivity1,225 (70)
 Oral ulcers772 (44)
 Arthritis1,447 (83)
 Serositis767 (44)
 Neurologic disorder211 (12)
 Hematologic disorder1,077 (61)
 Immunologic disorder1,209 (69)
 Renal disorder518 (30)
 Antinuclear antibodies1,682 (96)
Autoantibodies, no. positive/total no.  (% positive) 
 Anti-dsDNA769/1,565 (49)
 Anti-SSA/Ro449/1,597 (28)
 Anti-SSB/La199/1,598 (12)
 Anti-Sm184/1,575 (12)
 Anti-RNP294/1,589 (19)
 Anticardiolipin483/1,415 (34)
% Northern European ancestry, median (IQR)94 (83–98)
>90% Northern European ancestry, no. (%)1,071 (61)

We first estimated the continental ancestry for each subject in the study. All subjects with <90% European ancestry were removed from the analysis (n = 137) (complete data are available on the Web site http://pages.medicine.ucsf.edu/lupus). European substructure analysis was then performed for the remaining subjects (n = 1,754). The median percent Northern European ancestry in the entire sample was 94% (Table 1). In almost all of the case collections, the majority of subjects had >90% Northern European ancestry (data on all case collections are available on the Web site http://pages.medicine.ucsf.edu/lupus). Individuals of different European ancestry were generally dispersed among the different collection sites across the United States. The distribution of percent Northern European ancestry was substantially skewed in all study participants, as shown in Figure 1.

We next examined the association between percent Northern European ancestry and the phenotypes defined by the ACR classification criteria for SLE, using Spearman's rank correlations in univariate analyses. These analyses showed that increasing percent Northern European ancestry was associated with discoid rash (P = 0.0009 by permutation testing), photosensitivity (P < 0.0001 by permutation testing), and the immunologic disorder criterion for SLE (P < 0.0001 by permutation testing) (Table 2). The associations remained statistically significant in multivariate models with tests for trend (Tables 3 and 4).

Table 2. Associations between percent Northern European ancestry and manifestations of SLE*
SLE phenotypeSpearman's rank correlationP
  • *

    See Table 1 for definitions.

  • P values were determined using Monte Carlo permutation testing based on 10,000 replications. See Patients and Methods for further details.

ACR classification criteria for SLE  
 Malar rash0.0250.31
 Discoid rash0.0780.0009
 Photosensitivity0.10<0.0001
 Oral ulcers−0.0440.065
 Arthritis−0.0750.0022
 Serositis−0.0470.051
 Neurologic disorder0.00020.99
 Hematologic disorder−0.00390.87
 Immunologic disorder−0.12<0.0001
 Renal disorder−0.0570.015
 Antinuclear antibodies−0.0430.073
Autoantibodies  
 Anti-dsDNA−0.0500.046
 Anti-Ro/SSA0.0190.47
 Anti-La/SSB0.0270.28
 Anti-Sm−0.0150.56
 Anti-RNP−0.0290.25
 Anticardiolipin−0.100.0001
Table 3. Tests for trend for the systemic lupus erythematosus manifestations of discoid rash, photosensitivity, arthritis, and renal disorder in association with percent Northern European ancestry*
Quartile of percent Northern European ancestryDiscoid rashPhotosensitivityArthritisRenal disorder
OR (95% CI)POR (95% CI)POR (95% CI)POR (95% CI)P
  • *

    Values are the combined odds ratio (OR) with 95% confidence interval (95% CI), as determined using the Cochran-Mantel-Haenszel method from multivariate analyses for each quartile. Quartiles of percent Northern European ancestry were as follows: quartile 1 ≤82.6%, quartile 2 >82.6% and ≤94.2%, quartile 3 >94.2% and ≤98.0%, and quartile 4 >98.0%. P values were determined using tests for trend.

  • Adjusted for sex, case collection, and disease duration (see Patients and Methods).

  • Adjusted for sex and case collection (see Patients and Methods).

1 (n = 439)ReferentReferentReferentReferent
2 (n = 445)1.68 (0.88–3.18)0.111.25 (0.92–1.71)0.150.78 (0.51–1.19)0.240.98 (0.70–1.37)0.90
3 (n = 445)2.36 (1.19–4.68)0.0111.90 (1.32–2.73)0.00050.62 (0.40–0.97)0.0340.98 (0.64–1.47)0.91
4 (n = 425)1.93 (0.98–3.83)0.0541.64 (1.13–2.35)0.00840.68 (0.44–1.06)0.0860.88 (0.59–1.33)0.55
Ptrend across quartiles0.0140.00210.120.19
Table 4. Tests for trend for lupus-related autoantibody phenotypes in association with percent Northern European ancestry*
Quartile of percent Northern European ancestryImmunologic disorder criterionAnti-dsDNA antibodyAnticardiolipin antibody
OR (95% CI)POR (95% CI)POR (95% CI)P
  • *

    Values are the combined OR with 95% CI, as determined using the Cochran-Mantel-Haenszel method from multivariate analyses for each quartile. Quartiles of percent Northern European ancestry were as follows: quartile 1 ≤82.6%, quartile 2 >82.6% and ≤94.2%, quartile 3 >94.2% and ≤98.0%, and quartile 4 >98.0%. P values were determined using tests for trend. See Tables 1 and 3 for definitions.

  • Adjusted for sex, case collection, and disease duration (see Patients and Methods).

  • Adjusted for sex and case collection (see Patients and Methods).

1 (n = 439)ReferentReferentReferent
2 (n = 445)0.92 (0.63–1.33)0.660.98 (0.72–1.36)0.920.80 (0.57–1.11)0.19
3 (n = 445)0.65 (0.43–0.99)0.0420.87 (0.60–1.25)0.450.56 (0.38–0.83)0.0034
4 (n = 425)0.50 (0.33–0.76)0.0010.67 (0.46–0.96)0.0290.46 (0.30–0.69)0.0001
Ptrend across quartiles0.00030.0171.6 × 10−4

After using the Cochran-Mantel-Haenszel method to account for the effects of sex, case collection, and disease duration (if appropriate), increasing percent Northern European ancestry was significantly associated with photosensitivity (Ptrend = 0.0021, OR for highest quartile of Northern European ancestry compared with lowest quartile [ORhigh–low] 1.64, 95% confidence interval [95% CI] 1.13–2.35) and discoid rash (Ptrend = 0.014, ORhigh–low 1.93, 95% CI 0.98–3.83). Conversely, a higher level of Northern European ancestry was protective against the immunologic disorder criterion for SLE (Ptrend = 0.0003, ORhigh–low 0.50, 95% CI 0.33–0.76) (Table 4). Although univariate tests suggested associations between Northern European ancestry and arthritis and renal disorder, no associations were observed with these phenotypes in the multivariate models with tests for trend (Table 3). The analyses were also conducted using multivariate logistic regression adjusting for sex, case collection, and disease duration (if appropriate), and similar results were observed (data not shown).

Since the immunologic disorder criterion for SLE is based on autoantibody production, we also examined the association between Northern European ancestry and lupus-related autoantibodies. Associations between the percent Northern European ancestry and the presence of anti-dsDNA and anticardiolipin autoantibodies were observed in univariate analyses (Table 2). No significant associations between the percent Northern European ancestry and antibodies to the nuclear antigens Ro/SSA, La/SSB, Sm, and RNP were observed. In multivariate analyses using the Cochran-Mantel-Haenszel method to account for the effects of sex, case collection, and disease duration (if appropriate), increasing Northern European ancestry remained significantly protective against the production of anti-dsDNA antibodies (Ptrend = 0.017, ORhigh–low 0.67, 95% CI 0.46–0.96), as shown in Table 4. In multivariate analyses, the inverse association with increasing Northern European ancestry was even stronger with the production of anticardiolipin antibodies (Ptrend = 1.6 × 10−4, ORhigh–low 0.46, 95% CI 0.30–0.69). These analyses were also conducted using multivariate logistic regression, and similar results were observed (data not shown).

Given the correlation between phenotypes, we conducted a principal components analysis to determine whether Northern European ancestry was more strongly associated with an unmeasured factor underlying the 11 ACR classification criteria than with the individual ACR criteria. Four principal components of the ACR criteria were identified, but the associations with Northern European ancestry were not stronger than the associations with the individual criteria (data not shown). A similar analysis was performed using the 6 autoantibody phenotypes. Three principal components were identified, but once again, the associations with Northern European ancestry were not stronger than the association with the individual autoantibodies (data not shown). Therefore, these findings suggest that it is not likely that Northern European ancestry is more strongly associated with an unmeasured factor underlying these phenotypes.

DISCUSSION

  1. Top of page
  2. Abstract
  3. PATIENTS AND METHODS
  4. RESULTS
  5. DISCUSSION
  6. AUTHOR CONTRIBUTIONS
  7. Acknowledgements
  8. REFERENCES

This study shows that genetic substructure within European-derived populations is associated with specific manifestations of SLE. Increased Northern European ancestry is associated with an increased risk of photosensitivity and discoid rash (mucocutaneous manifestations) and a decreased risk of autoantibody production. These results support the hypothesis that differences in genetic background between subjects within the same major continental ethnic group (as reflected by Northern versus Southern European ancestry in this study) can influence the development of specific SLE phenotypes. Of note, ancestry associations with autoantibody production are, in some instances, stronger than the associations with mucocutaneous manifestations. This finding also supports the theory that genetic factors may be more relevant to the production of autoantibodies (which are implicated in disease pathogenesis) than to the development of other SLE manifestations such as arthritis or serositis.

The associations of photosensitivity and discoid rash with increasing Northern European ancestry are particularly intriguing, since exposure to sunlight and ultraviolet radiation has been shown to precipitate various SLE manifestations, including cutaneous reactions (30–32). One can hypothesize an evolutionary mechanism for this finding. In general, populations in Northern Europe are exposed to less sunlight than are populations in Southern Europe. Over time, Northern European populations may have developed an increased capacity for sunlight absorption compared with their Southern counterparts. However, this increased absorption may become detrimental if the person moves to a more sun-exposed region. The resulting additional sunlight absorption may lead to sun-induced damage (such as discoid rash) and photosensitivity reactions. In addition, previous studies have shown that skin damage and inflammation from ultraviolet light exposure have been associated with skin and hair color (33, 34), suggesting that the association between increasing Northern European ancestry and the mucocutaneous subphenotypes of photosensitivity and discoid rash may be related to these traits.

The mechanism for increased autoantibody production in those with a lower percent Northern European ancestry (i.e., more Southern European ancestry) is not known. This association is likely attributable, at least in part, to genetic differences between Northern and Southern Europeans. It is interesting to speculate that natural selection may play a role in explaining this result. Differential exposure to infectious agents in Southern European populations compared with Northern European populations may have resulted in selection of genetic variants with consequent differences in immune responses.

Genes previously associated with the risk of developing SLE display evidence of geographic variation. One example is the R620W polymorphism of PTPN22. This polymorphism has been associated with multiple autoimmune diseases that are characterized by autoantibody production, including SLE (19, 35). The allele frequency of R620W in Europeans decreases substantially from Northern Europe to Southern Europe (36). However, the geographic variation seen in this polymorphism is not likely to explain the association between autoantibody production and European substructure as observed in the present study. The PTPN22 R620W polymorphism is more common in Northern Europe, and we found that increasing Northern European ancestry was protective against autoantibody production. In addition, no associations between the PTPN22 R620W polymorphism and SLE-related autoantibodies have been reported.

Evidence of geographic variation has also been observed in the HLA region on chromosome 6p21. HLA alleles (specifically HLA–DRB1*0301 and HLA–DRB1*1501) were the first genetic susceptibility risk factors for SLE identified (37). In the UK, allele frequencies for genes in this region have been found to vary on a northwest–southeast cline (38). HLA class II alleles have also been associated with both anti-dsDNA autoantibody production (39) and anticardiolipin autoantibody production (40, 41). Further studies are needed to determine the role of the HLA region in the associations between autoantibody production and European population substructure as seen in this study.

The strengths of this study include its large sample size of subjects with well-characterized clinical features who were recruited from Europe and from multiple sites across the US. Analyses were also adjusted for potential confounding factors such as SLE patient recruitment site, sex, and disease duration (when appropriate). The use of continental ancestry markers ensured that each subject in our study was truly of European ancestry. In addition, the detailed assessment of population substructure in Europe-derived SLE cases has not been previously applied to genetic studies of SLE manifestations.

This study does have limitations. The first limitation is the skewed distribution of the primary predictor, percent Northern European ancestry. This skewing may reflect the overall predominance of Northern European ancestry among many North Americans of European descent. Ideally, the associations identified in this study should be further investigated in a sample of SLE patients with more Southern European ancestry. Second, while the north–south gradient in Europe is the largest source of population substructure in European Americans (10), more subtle stratification due to ethnic or regional differences may influence specific phenotypes.

In addition, since these SLE cases are not part of a longitudinal cohort, misclassification of outcomes may occur. SLE patients may develop additional manifestations as their disease progresses. Since these subphenotypes were not present at study enrollment, the patient would be misclassified as being negative for this outcome. However, this misclassification error results in biasing our findings toward the null and thus should not cause false-positive results. Furthermore, since most SLE patients in this study had well-established disease at study entry, with a median disease duration of ∼7 years, the rate of misclassification of outcomes should be relatively low.

Finally, this study had limited statistical power to detect associations with certain SLE phenotypes (e.g., the neurologic disorder criterion) due to the low frequency of these phenotypes in SLE patients. To fully identify genetic predictors of these rare outcomes, one would need to enrich the case group for these manifestations to achieve a sample size adequate to study these outcomes.

In summary, this study emphasizes the concept that SLE patients who are descended from the same major continental ethnic group (e.g., European) have measurable genetic differences related to their geographic ancestry (e.g., Northern Europe versus Southern Europe) that influence their risk of developing specific SLE manifestations. As an example, we have shown in this study that increased Northern European ancestry is associated with photosensitivity and discoid rash and is protective against autoantibody production. These findings also indicate that geographic ancestry, likely reflecting genetic differences between individuals of Northern European ancestry and those of Southern European ancestry, may contribute to the clinical heterogeneity seen in SLE patients of European descent. Further detailed investigation of the genetic differences among SLE patients with more Northern versus Southern European ancestry may provide insight into the genetic mechanisms underlying the development of photosensitivity, discoid rash, and autoantibody production. Given the association between SLE-related autoantibodies and potentially severe disease manifestations (e.g., anticardiolipin autoantibodies in association with arterial and venous thrombosis), the results of this study also suggest that genetic ancestry can influence life-threatening disease outcomes. Finally, the overall findings of this study have substantial implications for genetic case–control studies of SLE. Future genetic studies of the subphenotypes of SLE, even if they involve only a single continental ethnic group, should include assessment for population substructure to avoid confounding by differences in genetic ancestry.

AUTHOR CONTRIBUTIONS

  1. Top of page
  2. Abstract
  3. PATIENTS AND METHODS
  4. RESULTS
  5. DISCUSSION
  6. AUTHOR CONTRIBUTIONS
  7. Acknowledgements
  8. REFERENCES

All authors were involved in drafting the article or revising it critically for important intellectual content, and all authors approved the final version to be published. Dr. Chung had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

Study conception and design. Chung, Seldin, Criswell.

Acquisition of data. Chung, Lee, Ortmann, Hom, Graham, Nititham, Kelly, Morrisey, Wu, Yin, Alarcón-Riquelme, Tsao, Harley, Gaffney, Moser, Manzi, Petri, Gregersen, Langefeld, Seldin, Criswell, Behrens.

Analysis and interpretation of data. Chung, Tian, Taylor, Seldin, Criswell.

Acknowledgements

  1. Top of page
  2. Abstract
  3. PATIENTS AND METHODS
  4. RESULTS
  5. DISCUSSION
  6. AUTHOR CONTRIBUTIONS
  7. Acknowledgements
  8. REFERENCES

We would like to thank all of the SLE patients and referring physicians for their contributions to our research.

REFERENCES

  1. Top of page
  2. Abstract
  3. PATIENTS AND METHODS
  4. RESULTS
  5. DISCUSSION
  6. AUTHOR CONTRIBUTIONS
  7. Acknowledgements
  8. REFERENCES