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Abstract

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

Objective

The A allele of the PD1.3 single-nucleotide polymorphism (SNP) on the programmed cell death gene PDCD1 was markedly more frequent in patients with systemic lupus erythematosus (SLE) than in unaffected controls in a recent study involving large sets of Swedish, European American, and Mexican families. This study sought to determine the role of PDCD1 in susceptibility to SLE in the Spanish population.

Methods

Seven PDCD1 SNPs were studied in 518 SLE patients and 800 healthy control subjects who had been recruited in 5 distant towns spanning continental Spain. Patients and controls were of Spanish ancestry. The diagnosis of SLE was in accordance with the American College of Rheumatology updated classification criteria.

Results

The A allele of the PD1.3 polymorphism was significantly less frequent in Spanish female patients with SLE than in Spanish female controls (9.0% versus 13.0%, odds ratio 0.67, 95% confidence interval 0.50–0.89). This difference was consistent across the 5 sets of samples grouped by town of recruitment. The other PDCD1 SNPs were not associated with SLE susceptibility. The haplotype structure of PDCD1 in the Spanish controls was different from that reported in other healthy control populations.

Conclusion

Our results confirm the association of PDCD1 with susceptibility to SLE, but the findings show a lack of involvement of the PD1.3 SNP, which is contrary to the role of the PD1.3 A allele observed previously. These contradictory results probably reflect population differences in the haplotype structure of the PDCD1 locus. More research focusing on new polymorphisms and identifying associations in other populations will be needed to clarify the role of PDCD1 in SLE susceptibility.

The role of genetics in the etiology of systemic lupus erythematosus (SLE) is firmly established (1, 2). However, it has been difficult to identify the causal genetic factors. Many genes that are important for immune function have been studied as candidates. These studies have found clear evidence of an association with SLE for only a few factors: the histocompatibility antigens, some members of the complement cascade, and the low-affinity IgG receptors.

A different approach in the investigation of the genetics of SLE that has been followed by 4 research groups is to perform genome-wide linkage analyses in families of patients with SLE (1, 2). The results of these studies define a complex picture; none of the genetic factors have major effects, but it seems likely that many loci with small effects are involved (more than 50 loci have shown some degree of linkage). Significant linkage has already been found with 7 loci, and some of these have been replicated in different studies, yielding further support to their role in SLE. However, it has proved difficult, as in other complex diseases, to progress from linkage results to identification of the causative polymorphisms. A recent report by Prokunina et al (3) indicated that, although difficult, this progress will be possible. The authors had initially identified a locus on chromosome 2q37 that was strongly linked to SLE susceptibility (4, 5) in a genome-wide linkage study involving multicase families of Northern European descent. On the basis of this linkage result, Prokunina et al started to search for candidate genes in the 2q37 region. The authors found a very good candidate in the programmed cell death gene PDCD1.

PDCD1 is a member of the CD28 family of receptors, which are type I membrane proteins with an important function in modulating lymphocyte activation (6, 7). The most distinctive feature of PDCD1 is an immunoinhibitory domain in its intracellular tail, that is, an immunoreceptor tyrosine-based inhibition motif (ITIM), which is shared with a diverse group of inhibitory receptors. Upon tyrosine phosphorylation at the ITIMs, these molecules recruit SH2 domain–containing phosphatases, such as SH2-containing tyrosine phosphatase 1, and negatively regulate cell activity. Specifically, upon activation, PDCD1 is expressed by T and B lymphocytes, and when it binds to its ligands, PD-L1 and PD-L2, which are expressed in many tissues, it attenuates T and B cell responses.

This function is critical for the prevention of autoimmunity, as has been shown in 2 mouse strains made deficient in PDCD1 by homologous recombination. In the first strain, BALB/c-PDCD1−/−, the mice developed an autoimmune myocardiopathy mediated by antibodies against cardiac troponin I (8). The second strain, C57BL/6-PDCD1−/−, had a more relevant autoimmune phenotype for SLE, because this strain showed a late-onset, chronic, progressive, lupus-like glomerulonephritis with IgG3 and C3 deposition accompanied by other autoimmune features, including arthritis and splenomegaly (9). These experimental mouse strains have shown that PDCD1 is a necessary negative regulator of self-reactivity and is a good candidate gene in the predisposition to SLE.

Prokunina et al searched for polymorphisms in the PDCD1 gene that were overrepresented in SLE families. A single-nucleotide polymorphism (SNP) in intron 4, called PD1.3, was found to segregate with the disease. An association analysis of familial and sporadic cases of SLE found a consistent overrepresentation of the A allele among Swedish, European American, and Mexican patients with SLE. The functional consequence of the PD1.3 SNP would be to interfere with binding of a transcription factor, RUNX1, that would modify PDCD1 expression.

The identification of the role of the A allele of PD1.3 seems very significant because of the sparse data on the genetics of SLE, and because it can be expected that this finding will trigger a series of exciting discoveries about the pathogenesis of SLE and will generate new opportunities to manage the disease. This association has already been used to support the hypothesis of a special role of RUNX1 in regulating autoimmunity (10–12) and to propose a role for PDCD1 in SLE nephritis (13). However, in the present study, we found that the described relationship between the PD1.3 SNP and SLE susceptibility was not evident in Spanish patients. Therefore, new investigations will be needed to understand the true nature of the role of PDCD1 in SLE predisposition.

PATIENTS AND METHODS

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

Patients.

We studied 518 unrelated patients with SLE and 800 healthy control subjects who were independently recruited in 5 distant towns spanning continental Spain: Santiago de Compostela, Madrid, Barcelona, Seville, and Granada. Patients with SLE were classified according to the American College of Rheumatology 1997 revised criteria for SLE (14). Healthy controls were selected in different ways at each town: in Santiago, 141 were recruited from the general population and 37 from laboratory and hospital personnel; in Madrid, all were blood bank donors; in Seville, all were bone marrow donors (unrelated to the patients); in Granada, 64 were laboratory and hospital personnel and 42 were bone marrow donors (unrelated to the patients); in Barcelona, 91 were blood bank donors and 21 were healthy women recruited for clinical research. All participants were of Spanish ancestry and gave their written informed consent. Approval was obtained from the respective ethics committees: Comite Etico de Investigacion Clinica de Galicia, Comite Etico de Investigacion Clinica and Comision de Investigacion del Hospital Universitari Vall d′Hebron, Comite Etico de Investigacion Clinica del Hospital Universitario Virgen del Rocio, Comite Etico de Investigacion Clinica del Hospital 12 de Octubre, and Comite Etico de Investigacion Clinica del Hospital Virgen de las Nieves.

Genotyping.

The 7 PDCD1 SNPs have been previously described by Prokunina et al (3), and we have used the nomenclature of that report (Table 1). Primers and fluorescence-labeled probes were synthesized by TIB MOLBIOL (Berlin, Germany) (Table 1). Genotyping of PD1.3 was done by analysis of the melting curve after hybridization with FRET probes on a LightCycler (Roche, Barcelona, Spain). Melting temperature was 58°C for the G allele and 66°C for the A allele. Polymerase chain reaction (PCR) was carried out in a total volume of 15 μl in glass capillaries containing 100–150 ng of genomic DNA, 0.5 μM of each primer, 0.2 μM of each probe, 3.5 mM of MgCl2, and 1.5 μl of the DNA-Master Hybridization Probes reagent (Roche). PCR conditions were denaturation at 95°C for 2 minutes, followed by 45 cycles of denaturation at 95°C for 0 seconds, annealing at 67°C for 10 seconds, and extension at 72°C for 5 seconds. After amplification, the melting analysis was performed by denaturation at 95°C for 5 seconds, annealing at 45°C for 10 seconds, and increasing temperature to 85°C at a rate of 0.5°C/second.

Table 1. PDCD1 polymorphisms and genotyping methods*
 SNPLocationMethodRestriction enzymePCR oligonucleotides
  • *

    SNP = single-nucleotide polymorphism; PCR = polymerase chain reaction; PCR-RFLP = polymerase chain reaction–restriction fragment length polymorphism; UTR = untranslated region.

  • Positions relative to the transcription start site on locus NT_077995.

  • Modifications of the oligonucleotides used for analysis of the melting curve: anchor labeled at the 3′ end with fluorescein and sensor labeled at the 5′ end with LightCycler Red640 and modified at the 3′ end by phosphorylation.

PD1.1−538G/APromoterPCR-RFLPMsp IForward TTCTAGCCTCGCTTCGGTTA; reverse CTCAACCCCACTCCCATTCT
PD1.26438G/AIntron 2PCR-RFLPMsp IForward AGCGGCACCTACCTCTGTGG; reverse GTGGGCTGTGGGCACTTCTG
PD1.37146G/AIntron 4Melting curve Forward AGCCCCCAGGCAGCAA; reverse ACCGCAGGCAGGCACATAT; anchor CCTAAAGCCATGATCTGGGGCCCC; sensor GCCCACCTGCAGTCTCC
PD1.47499G/AIntron 4Sequence Forward GCAGCAACCTCAATCCCTAA; reverse AGGGTCTGCAGAACACTGGT
PD1.57785C/TExon 5PCR-RFLPAlu IForward AGACGGAGTATGCCACCATT; reverse CACTGTGGGCATTGAGACAT
PD1.97625C/TExon 5PCR-RFLPBpu10 IForward GGACAGCTCAGGGTAAGCAG; reverse AGGGTCTGCAGAACACTGGT
PD1.68737G/A3′-UTRPCR-RFLPNla IIIForward TCAGAAGAGCTCCTGGCTGT; reverse GGGGAACGCCTGTACCTT

The remaining SNPs, except PD1.4, were genotyped by PCR–restriction fragment length polymorphism analysis. Primers were designed to include 2 potential restriction sites in the amplified fragment: the site with the SNP and an invariant site that was used as an internal control of the enzymatic digestion. The PCRs were performed in a total volume of 10 μl containing 50 ng of genomic DNA, 200 μM of dNTPs, 0.1 μM of each primer, 1 unit of Taq DNA polymerase (Roche), and 1× PCR buffer. In addition, the PCR mixture was supplemented with MgCl2: 1 mM for PD1.1, 0.5 mM for PD1.2, and 2 mM for PD1.5. Touchdown PCR was used to genotype PD1.1, with initial denaturation at 95°C for 3 minutes, followed by 14 cycles of denaturation at 95°C for 15 seconds, annealing for 15 seconds at temperatures decreasing from 66°C to 59°C at a rate of 0.5°C/cycle, and extension at 72°C for 15 seconds, followed by 30 cycles in which the annealing temperature was kept at 59°C. PCR conditions for PD1.2, PD1.5, PD1.6, and PD1.9 were initial denaturation at 95°C for 3 minutes, followed by 35 cycles of denaturation at 95°C for 15 seconds, annealing for 15 seconds at different temperatures (64°C for PD1.2 and PD1.5, 56°C for PD1.6, and 54°C for PD1.9), and extension at 72°C for 15 seconds. The PCR products were digested with the restriction enzymes indicated in Table 1, and the products were examined after electrophoresis in agarose gels.

The PD1.4 SNP was genotyped by sequencing 30 samples, which included 10 with each of the 3 PD1.5 genotypes. The PCR was carried out in 10 μl containing 50 ng of genomic DNA, 0.1 μM of each primer, 0.25 units of FailSafe PCR Enzyme Mix (Epicentre, Madison, WI), and 5 μl of FailSafe PreMix E. PCR conditions were 95°C for 3 minutes, followed by 35 cycles of 95°C for 15 seconds, 56°C for 15 seconds, and 72°C for 15 seconds.

Sequencing.

Sequencing was used to genotype PD1.4 and to test the accuracy of the genotypes. The system used for sequencing was the BigDye Ready Reaction Kit (Applied Biosystems, Madrid, Spain) in an ABI Prism 3100-Avant Genetic Analyzer (Applied Biosystems). Cycling conditions were initial denaturation at 96°C for 4 minutes, followed by 30 cycles of denaturation at 96°C for 15 seconds, annealing at 50°C for 10 seconds, and extension at 60°C for 3 minutes. Final elongation was done at 60°C for 10 minutes.

Statistical analysis.

Allele frequencies as well as odds ratios and their 95% confidence intervals (95% CIs) were calculated. Comparisons of allele frequencies were done using a chi-square test with a 2 × 2 contingency table. The post hoc power of the study was determined with Gpower software (15). Evidence of a gene-dose effect was evaluated with logistic regression. Concordance with Hardy-Weinberg equilibrium was determined with a chi-square goodness-of-fit test. Haplotype frequencies were estimated with PL-EM software, which uses an implementation of the expectation-maximization algorithm (16). Comparison of haplotype frequencies was done with a nonparametric homogeneity test (17) and with a chi-square test, after collapsing columns with low frequency, as implemented in the T2 option of the Clump software (5,000 permutations used to determine the P value) (18). P values less than or equal to 0.05 were considered significant.

RESULTS

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

In an attempt to replicate the association study performed by Prokunina et al, we explored the possible association between the PD1.3 A allele and SLE susceptibility. We found that in contrast to the previously reported results, the PD1.3 A allele was significantly less frequent in Spanish patients with SLE than in Spanish controls (P = 0.006) (Table 2). Moreover, the frequency of the A allele in the Spanish controls (12.9%) was notably larger than that observed in any of the populations previously studied (in which it was 5% or lower).

Table 2. Frequency of the A allele of the PD1.3 single-nucleotide polymorphism in Spanish controls and Spanish patients with systemic lupus erythematosus (SLE)*
 FemalesMalesTotal
  • *

    Except where indicated otherwise, values are the number of alleles/number of studied chromosomes (%).

  • Sex not recorded for 3 samples.

Controls140/1,076 (13.0)66/522 (12.6)206/1,600 (12.9)
SLE patients84/930 (9.0)12/102 (11.8)97/1,036 (9.4)
Odds ratio (95% confidence interval)0.67 (0.50–0.89)0.9 (0.5–1.8)0.70 (0.54–0.90)

Therefore, we considered it necessary to check the accuracy of our results. We used 3 criteria to check accuracy. We sequenced every sample that had yielded ambiguous melting curves. We sent 72 samples to be cross-checked by authors of the original report on PD1.3 (L. Prokunina and M. Alarcón-Riquelme, Uppsala University, Uppsala, Sweden). Finally, we checked the genotype frequencies for concordance with Hardy-Weinberg equilibrium. When we applied all 3 criteria, our results were found to be accurate and we confirmed that the technology used, analysis of the melting curve with FRET probes, was reliable.

The decreased frequency of the A allele in Spanish patients with SLE was restricted to the female patients (9.0% versus 13.0% in the female controls; P = 0.005), whereas the male patients with SLE had a frequency of A alleles similar to that in the male controls (11.8% versus 12.6%) (Table 2). Nevertheless, a small difference between the male patients and male controls could not be excluded completely, due to the small number of male patients with SLE (only 51 among the 518 SLE patients). It was not possible to compare this sex effect with previous data, because only data on female subjects were available.

When we focused on the results in the female population, the decreased frequency of the A allele of PD1.3 in SLE patients, although moderate (as reflected by a 0.67 odds ratio), was consistently observed across the 5 sets of SLE cases and controls grouped by town of recruitment (Table 3). Town-specific odds ratios showed only moderate and expected fluctuations, ranging from 0.5 to 0.8. These results provided evidence against spurious differences due to unappreciated population stratification, because such an artifact would be very unlikely across 5 independent sample sets. There was no geographic trend in the frequency of the A allele or in the odds ratios along the North-South or the East-West axes of Spain.

Table 3. Frequency of the A allele of the PD1.3 single-nucleotide polymorphism in Spanish female subjects, grouped by town of recruitment*
 SantiagoMadridSevilleGranadaBarcelona
  • *

    Except where indicated otherwise, values are the number of alleles/number of studied chromosomes (%). SLE = systemic lupus erythematosus.

Controls24/212 (11.3)52/406 (12.8)23/142 (16.2)18/174 (10.3)23/142 (16.2)
SLE patients12/166 (7.2)14/202 (6.9)26/246 (10.6)9/102 (8.8)23/214 (10.7)
Odds ratio (95% confidence interval)0.6 (0.3–1.3)0.5 (0.3–0.9)0.6 (0.3–1.1)0.8 (0.4–1.9)0.6 (0.3–1.2)

There was some evidence for a gene-dose effect. Susceptibility to SLE among the female subjects with the AA genotype seemed much lower than that among the female subjects with the AG genotype; the odds ratio for the AA genotype was 0.3 (95% CI 0.1–1.0), whereas the odds ratio was 0.7 for the AG genotype (95% CI 0.5–1.0). However, this difference in SLE susceptibility between the AA and AG genotypes was not statistically significant.

We also explored the other 6 PDCD1 SNPs that were studied by Prokunina et al (3). The frequencies of the PD1.5 C allele and of the PD1.6 A allele were similar between the Spanish patients with SLE and the Spanish controls (56.9% versus 55.3%, odds ratio 1.1, 95% CI 0.9–1.3 and 11.6% versus 11.5%, odds ratio 1.0, 95% CI 0.8–1.3, respectively). There were no significant differences between male and female subjects for these SNPs, and they showed genotype distributions in agreement with Hardy-Weinberg equilibrium. The PD1.4 SNP was in complete linkage disequilibrium with PD1.5, and only PD1.5 was studied in all samples. The remaining 3 SNPs, PD1.1, PD1.2, and PD1.9, were in complete linkage disequilibrium. The allele frequency of their minor alleles was 1.1% in SLE patients (95% CI 0.3–3.9%; a total of 186 chromosomes studied) and 0% in controls (95% CI 0.0–1.9%; a total of 186 chromosomes studied). Due to their rarity, these latter 3 SNPs were not studied further.

We compared the allele frequencies of the PD1.3, PD1.5, and PD1.6 SNPs in Spanish female controls and in the female control populations studied by Prokunina et al (Table 4). There were significant differences in 2 SNPs: PD1.3 and PD1.6. As already mentioned, the PD1.3 A allele was significantly more frequent in Spanish controls than in any of the other control populations; its frequency was double that reported in the Swedish and European American sets, and was 6-fold higher than the frequency in Mexican controls. Regarding the PD1.6 SNP, the frequency in Spanish controls was more than half that reported in European Americans and much lower than that reported in Mexicans. The PD1.6 A allele seemed, however, marginally more frequent in the Spanish control subjects than in the Swedish control population, with the difference approaching statistical significance (P = 0.06).

Table 4. Differences in allele frequencies between the Spanish female control population and female controls from the study by Prokunina et al (3)*
PopulationPD1.3 APD1.5 CPD1.6 A
n/N (%)POR (95% CI)n/N (%)POR (95% CI)n/N (%)POR (95% CI)
  • *

    Data on the Swedish, European American, and Mexican populations are from Table 3 in reference3. Swedish and Mexican data include family controls and population controls, whereas the European American data are only from family controls. Results were similar when family and population controls were considered separately. n/N = number of alleles/number of studied chromosomes; OR = odds ratio; 95% CI = 95% confidence interval.

  • All comparisons versus Spanish female controls.

Spanish140/1,076 (13.0)  540/984 (54.9)  103/956 (10.8)  
Swedish41/602 (6.8)0.9 × 10−42.0 (1.4–2.9)220/404 (54.4)0.91.0 (0.8–1.3)47/598 (7.9)0.061.4 (1.0–2.0)
European American8/160 (5.0)0.00352.8 (1.4–5.9)84/160 (52.5)0.61.1 (0.8–1.5)30/160 (18.75)0.0040.5 (0.3–0.8)
Mexican12/538 (2.2)0.3 × 10−116.6 (3.6–11.9)215/360 (59.7)0.10.8 (0.6–1.0)168/348 (48.3)<1 × 10−160.13 (0.1–0.2)

To complete this analysis, we estimated haplotype frequencies of PDCD1 among the female subjects, using the data on PD1.3, PD1.5, and PD1.6. We found subjects bearing 7 of the 8 possible haplotypes (Figure 1). The haplotype frequencies were different between the female patients with SLE and the female controls, as determined by nonparametric heterogeneity test (P = 0.04) or by chi-square test, after grouping haplotypes with low expected values (P = 0.03). The difference was mainly due to a lower frequency of the ACG haplotype (PD1.3 A, PD1.5 C, and PD1.6 G, respectively) and a higher frequency of the GCG haplotype in SLE patients. The ACG haplotype included almost all PD1.3 A alleles, and therefore it was equivalent to the allelic data on PD1.3. In contrast, the GCG haplotype was the most common haplotype and was the only one overrepresented in SLE patients (P = 0.03, odds ratio 1.2, 95% CI 1.0–1.5, compared with the other haplotypes). This difference suggested that an SLE susceptibility polymorphism could be linked to this haplotype in Spanish patients.

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Figure 1. Haplotype frequencies of PDCD1 as defined by the PD1.3, PD1.5, and PD1.6 single-nucleotide polymorphisms (SNPs). Haplotypes are indicated according to the allele at the PD1.3, PD1.5, and PD1.6 SNPs, respectively. Haplotype frequencies for the Spanish female controls and Spanish female patients with systemic lupus erythematosus (SLE) were estimated from 918 and 772 chromosomes, respectively. Haplotype frequencies for the Swedish female controls were modified from Table 1 of reference3, after correction of some errors following indications from the authors (64 chromosomes). The Other haplotypes comprise GCA, ATG, and ACA. Bars show the 95% confidence intervals. ∗ = P < 0.05 versus Spanish controls.

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We also compared the haplotype frequencies obtained in female Spanish controls with that reported previously in female Swedish controls (Figure 1). There were significant differences between the 2 populations in spite of the small size of the Swedish sample (64 chromosomes) (P = 0.04, as determined by chi-square test after grouping low-frequency haplotypes). In this case, the major components of the difference were the frequencies of the ACG and the GTA haplotypes. Again, the ACG haplotype included most PD1.3 A alleles, and the excess observed in the Spanish population has already been mentioned. On the contrary, the GTA haplotype was about twice as frequent in the Swedish sample as in the Spanish controls (P = 0.02, odds ratio 2.2, 95% CI 1.1–4.2, compared with the other haplotypes). Therefore, this analysis found significant differences in the haplotype structure of the PDCD1 locus between the Spanish and the Swedish populations.

DISCUSSION

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

In the study of the genetics of SLE, there is no precedent of a clear association to a susceptibility polymorphism as that reported by Prokunina et al (3). The discovery of the susceptibility PD1.3 A allele started from linkage results and led to a polymorphism in a gene that, on functional grounds, could clearly be involved in the pathogenesis of SLE and whose association was shown in 4 different sets of patients from 3 different populations with large sample sizes. Another 6 SNPs on the gene were excluded by analysis in a set of multicase Swedish families or in a large association study. However, we have obtained results in the present study that call into question a straightforward scenario: in a large Spanish cohort, the PD1.3 allele associated with SLE susceptibility was the G allele, not the A allele. Therefore, our results have confirmed the involvement of the PDCD1 gene in susceptibility to SLE because it was also associated with the disease in our patients, but our study casts doubt on the role of the PD1.3 SNP since its alleles were associated with opposing effects in different populations.

In light of our results, it is important to note 2 limitations of the study by Prokunina et al. First, the search for polymorphisms in the PDCD1 locus was done by sequencing the gene in only 10 individuals. Second, evidence of a functional role of the PD1.3 SNP was limited to the findings on an electrophoretic mobility shift assay (EMSA). The fact that other polymorphisms had remained undetected in the PDCD1 locus has been suggested by a recent report describing 9 new SNPs (2 of them with frequent minor alleles) in a fragment of the gene (19) and by the identification of 3 different SNPs in our very preliminary search (Ferreiros-Vidal I, et al: unpublished observations). Therefore, it is possible that other polymorphisms in PDCD1 could be associated with SLE susceptibility. With regard to the functional study by Prokunina et al, the EMSA showed diminished binding between a synthetic oligonucleotide containing the sequence of the PD1.3 A allele and the RUNX1 transcription factor. This result only indicates that the prediction of a RUNX1 binding site in this sequence was correct and that it would be altered by the PD1.3 SNP. However, this does not necessarily imply that the specific site in intron 4 is functional in vivo; many of the more than 170 RUNX1 binding sites predicted in PDCD1 (18 of them in the same intron as that of PD1.3) would also bind to RUNX1 in an EMSA.

Discordant results in genetic association studies, as has been shown between these studies, are common in the genetic investigation of complex diseases and they have been extensively discussed (20–23). Recently, several meta-analyses of association studies have identified potential causes for the lack of concordance (20, 21). A very important cause seems to be an inflated positive result in the first report describing a new association. This overestimation of the true effect has been attributed to chance deviation from the true population values, characterized as the “winner's curse” phenomenon (20, 21). However, this artifact is very improbable in the case of PD1.3, since the first report included a confirmation of the effect in 4 different sets of samples (with risk ratios ranging from 2.2 to 5.3).

A second, very common cause of lack of replication is insufficient sample size. This limitation causes underpowered studies that fail to find evidence of true, but not very strong, effects. This possibility can also be disregarded in the current situation, because we used a sufficiently large sample size and we found not only a lack of association, but also a significant difference compared with the previous study. Consequently, the post hoc power to detect even the weakest of the effects reported by Prokunina et al, a 2.2 risk ratio in European American families, was 100% in our study (with α = 0.05).

A third possible cause of lack of replication is population stratification that will lead to biased or spurious results. Recent analyses have shown that the danger of spurious association due to this cause had been exaggerated previously (22, 23). In the case of PD1.3, this cause can be ruled out confidently, because Prokunina et al used family controls and because, in our study, the population was ethnically homogeneous and the results were consistent across 5 groups of cases and controls recruited independently in distant towns.

Finally, we are left with genetic heterogeneity, which seems the most likely cause of the discrepancy. This cause of contradictory results has frequently been suspected in the study of genetic associations, but its importance has been hard to assess. It has eluded demonstration when it has been specifically analyzed in a meta-analysis of association studies, probably because of the low number of studies that have contained ethnic information (21). However, there is no doubt that genetic heterogeneity plays a role in many monogenic diseases, and there are, also, several well-documented examples in complex diseases.

Two scenarios involving genetic heterogeneity are possible with regard to PDCD1. First, the most likely cause of the discrepancy between our results and the results from Prokunina et al is a lack of effect of the PD1.3 alleles on SLE susceptibility, in addition to differences in the haplotype structure of the PDCD1 locus. The PD1.3 alleles and the causal polymorphism (or polymorphisms) will be in different haplotypes in each population; the PD1.3 A allele will be in the same haplotype as that of an unknown SLE susceptibility polymorphism in the populations studied by Prokunina et al, whereas in the Spanish population, the haplotype including the causal polymorphism will contain the PD1.3 G allele. This possibility is supported by the differences in the distribution of haplotype frequency between the Spanish and the Swedes that appeared in our analysis, and by the differences in the allele frequencies of 2 of the PDCD1 SNPs between the Spanish and the Mexican and European American populations.

A second scenario, which seems less likely, implies an interaction between PD1.3 and other environmental or genetic factors that would vary among the studied populations. In this second scenario, the PD1.3 A allele will affect PDCD1 expression, but the changes will result in contradictory effects on SLE susceptibility depending on the interaction with unknown factors.

Consequently, although it appears to be confirmed that genetic variation on PDCD1 determines the predisposition to SLE, it is still unclear which polymorphisms are implicated, and therefore what mechanisms could be involved. A more thorough search for polymorphisms in the locus and new association studies in other populations will be needed to solve the questions raised by our results.

Acknowledgements

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

We thank Francisco Cotelo-Romar for his help in genotyping, Ludmila Prokunina and Marta Alarcón-Riquelme (Uppsala University, Uppsala, Sweden) for cross-checking the genotypes and for their helpful comments, and Olfert Landt (TIB MOLBIOL, Berlin, Germany) for designing the LightCycler hybridization probes.

REFERENCES

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