Systemic lupus erythematosus (SLE) is a complex disease trait of unknown aetiology. Genome-wide linkage studies in human SLE identified several linkage regions, including one at 1q23, which contains multiple susceptibility genes, including the members of the signalling lymphocyte activation molecule (SLAM) locus. In mice there is a syntenic linkage region, Sle1. The SLAM genes are functionally related cell-surface receptors, which regulate signal transduction of cells in the immune system. Family-based association study in UK and Canadian SLE families identified variants in the promoter and coding region of SLAMF7 and LY9 contributing to SLE disease susceptibility. The strongest association was from rs509749, in exon 8 of LY9 (P=0.00209). rs509749 encodes a Val/Met nonsynonymous change in amino acid 602 in the cytoplasmic domain of LY9. In the parents and affected individuals from the Canadian SLE families, the risk allele of rs509049 skews the T-cell population by increasing the number of CD8+ memory T cells, while decreasing the proportion of CD4+ naïve T cells and activated T cells. Since rs509749 lies within the consensus binding site for SAP/SH2D1a, which influences downstream signalling events from LY9, the mechanism for increased CD8+ memory T cells may include differential binding SAP/SH2D1a to the cytoplasmic domain of LY9.
Epidemiological studies reveal a significant genetic contribution to the pathogenesis of systemic lupus erythematosus (SLE). The relative risk to siblings of affected individuals (λs) is estimated to be 15- to 20-fold over that of the unrelated population.1 This increase in risk is similar in magnitude to that seen in several other autoimmune diseases, such as multiple sclerosis and type 1 diabetes mellitus. Although only limited numbers of studies have been published, twin concordance rates provide additional evidence for a genetic contribution in SLE; the concordance rate is significantly greater in monozygotic than dizygotic twins, 24 vs 2% in one study and 57 vs 5% in a smaller study.2, 3 Although small, the twin studies indicate a 10-fold increase in disease concordance in monozygotic twins compared with nonidentical twins.
Over the last several years, a number of groups have published genome-wide and targeted linkage analyses in SLE4, 5, 6, 7 There is considerable heterogeneity within these linkage studies, no doubt reflecting small study sizes, genetic heterogeneity and clinical heterogeneity in SLE. The most consistently mapped lupus susceptibility loci reside in the following regions: 1q23, 1q25–31, 1q41–42, 2q35–37, 4p16–15.2, 6p11–21 (MHC) and 16q12, all of which have been corroborated at least once in an independent cohort.8, 9 Of these loci, the evidence for the 1q23 locus is therefore striking since (1) it has been identified in multiple genome-wide linkage scans in humans, (2) it has been replicated in subsequent linkage studies that have targeted this region and (3) the syntenic region in mice has also been linked in more than one mouse model of spontaneous lupus. Specifically, in humans this region has been identified in three genome wide scans and in two replication studies. Furthermore, the evidence of linkage in these studies was found in populations of European and African derived populations.4, 5, 10, 11, 12, 13, 14 Finer localization is not possible with these studies since the sample sizes and linkage approach limit the power of resolution. Nonetheless, the studies are very informative in the sense that they consistently provide evidence for linkage to this region.
In mice, genome-wide linkage studies have implicated the region that is syntenic to the chromosome 1 region identified in human studies, in three different models of spontaneous lupus: the F1 intercross (New Zealand Black × New Zealand White; NZB/W), New Zealand Mixed/Aeg2410 New Zealand mice (NZM) and BXSB mouse strains.15, 16, 17 The phenotype of these mice is very similar to that in SLE patients, with the production of autoantibodies as well as multi-organ involvement, including severe nephritis. Interestingly, it has been shown that this locus is necessary for the production of nephrophilic autoantibodies and clinical glomerulonephritis.18 Fine mapping of this locus in one mouse strain has demonstrated the presence of a cluster of genes that contribute to disease susceptibility.19 This latter observation is very consistent with the human linkage data insofar as a cluster of disease susceptibility genes is likely to lead to a consistently observed linkage region as is the case for 1q23 in human SLE.
Within the 1q23 linkage region, the cluster of genes in the signalling lymphocyte activation molecule (SLAM) locus constitutes a strong set of candidate genes. This locus is a cluster of functionally related genes that belong to the CD2 superfamily of molecules that regulate the responses of cells of the immune system. These molecules are expressed in T cells, B cells, NK cells, myeloid and plasmacytoid dendritic cells, macrophages and monocytes and are involved in the regulation of immune responses in a wide variety of cell types. Six molecules in this cluster (SLAMF6 (Ly108), CD84, SLAMF1 (SLAM), SLAMF7 (CS1, CRACC), LY9 (CD229) and CD244) recruit active src kinases to their cytoplasmic tails using the adaptors of SAP (SLAM-associated protein), in T cells, NK cells and thrombocytes and EAT-2, in antigen-presenting cells (APCs) and B cells,20, 21, 22 while the lipid-linked CD48 interacts with src kinases in lipid rafts present in the plasma membrane of T cells and APCs. Recently, genetic polymorphisms in this cluster in different mouse strains have been found to be associated with production of anti-nuclear autoantibodies, a hallmark of SLE, as well as functional alterations of T and B cells.23, 24 Thus, emerging data provide strong arguments that these genes play an important role in the key processes believed to lead to SLE and therefore represent strong functional candidates.
Two independent association screens of the SLAM locus
Given the strong prior probability, based upon human and mouse linkage and functional results, for the molecules in the SLAM locus having a role in SLE susceptibility, two family collections, one from the United Kingdom and the other from Canada, were independently typed for markers across this locus. Both research groups selected single nucleotide polymorphisms (SNPs) across the region from the SLAMF7 gene at the telomeric end of this 1p locus all the way to the intelectin genes at the centromeric end. The genomic organization of the SLAM locus, from SLAMF7 through to ITNL1, is shown in Figure 1. In both populations, the composition of which is shown in Table 1, the initial genotyping showed evidence of association for multiple SNPs around SLAMF7/LY9 (Table 3). To maximize the power of this candidate region association approach both groups were interested in combining the two datasets, and so a second round of genotyping was performed in both populations to increase the density of markers in this region and to ensure that associated SNPs from either population were typed in both set of samples. This meant that the final list of 40 markers genotyped across the locus included a total of 31 SNPs genotyped in the 461 UK SLE nuclear families and an overlapping set of 32 markers typed in the 271 Canadian families (Table 2). The genotyping in both populations was subject to quality control, as described in Materials and methods, to determine the final list of markers suitable for the combined analysis. Since the list of markers typed in both populations was overlapping, there was a common core of 23 variants genotyped in both populations. The identities of all analysable markers, together with those removed following quality control are listed in Table 2 and the chromosomal location of these markers is illustrated in Figure 1. Using this common core of 22 variants, a cross population comparison of parental minor allele frequencies was used to demonstrate that there were no significant differences in parental allele frequency between the two populations for any of the genotyped markers across the SLAM locus, therefore supporting a combined analysis of these two populations (Table 2).
TDT analysis for single SNPs
The results of the transmission disequilibrium test (TDT) analysis using TRANSMIT, in both complete trios and single parent families, which have one or more unaffected siblings, are presented in Table 3. These data demonstrate that there were two variants in the LY9-CD244 region of the SLAM cluster showing significant association (P<0.05) in the UK SLE families: rs509749 in LY9 (P=0.0149) and rs480104 in CD244 (P=0.0145). There were also multiple associated variants in the region around the intelectin genes, ITNL1 and ITNL2, the strongest of which was a variant in the ITLN1–ITLN2 intergenic region, rs4656959 (P=0.00823). In the Canadian samples, there was significant association from a second variant within LY9, rs3817407 (P=1.17 × 10−3), but no significant evidence of association in the ITLN1–ITLN2 intergenic region. The association of these variants from LY9 in two independent populations suggest that LY9 may contribute to disease susceptibility, but there was no significant evidence for association of other genes in these populations.
Before carrying out TDT analysis on the combined UK/Canadian dataset, we demonstrated homogeneity in the proportions of transmitted to untransmitted alleles between the UK and Canadian populations, using Pearson's χ2 test (Supplementary Table 2) and homogeneity in odds ratios by the Breslow–Day test (P>0.05) in both populations for all markers tested, except for rs493646, in the upstream region of SLAMF7. rs493646 does not impact on the observed pattern of association because it shows an r2 of less than 0.12 with the other associated variants that were genotyped across the locus. Therefore, given the overall similarity between the UK and Canadian samples, to maximize the information from both populations, it was deemed appropriate to undertake TRANSMIT-TDT analysis on a single combined dataset, by merging all the samples from the UK and Canadian families into a single dataset (Table 3). The strongest association was found for the coding SNP rs509749, located in exon 8 of LY9. There was undertransmission of the rare G allele of rs509749 (P=0.00209). This SNP causes a nonsynonymous change of residue 602 (Met-602 (A) to Val-602 (G)) in the cytoplasmic domain of LY9. There is a second variant, rs1333065, located 6.1 kb upstream of LY9 which also shows undertransmission of the rare G allele (P=0.025). The association in rs509749 (P=0.0292) was also confirmed in complete trios from the joint UK/Canadian dataset (Supplementary Table 1). In the joint UK/Canadian dataset, there is an overtransmitted haplotype which is tagged by the overtransmitted A alleles of both rs509749 and rs1333065 (P=0.0177). This haplotype, which stretches 34 kb across LY9 and into the first intron of CD244, carries the A alleles of rs1333065, rs3817407, rs509749 and the G allele of rs480104. When the association of the SNPs tagging this risk haplotype were modelled in the joint UK/Canadian dataset, a dominant model for association best fitted the pattern of association, with the strongest effect being for rs509749 (P=0.0328).
In an attempt to dissect out the causative alleles in LY9, we used conditional logistic analysis to determine which variants made the strongest contribution to the association, using SNPs typed in both the UK and Canadian families within LY9 and rs1333065 which is located 6.4 kb immediately upstream of the gene. When the effects of each SNP were estimated conditional on the haplotype background across LY9, the nonsynonymous SNP rs509749 showed the strongest reduction in association (P=0.7) (Supplementary Table 3), which supports the hypothesis that rs509749 makes a major contribution to the association in LY9. In addition, because we found associated variants in both the SLAMF7-LY9 region and in the intelectin genes ITLN1 and ITLN2 for the UK samples (Table 3), we used further conditional logistic regression analysis to investigate whether there was only a single association signal from the two regions and not two independent signals. The results of these conditional analyses showed that there was only a single association signal from the two regions. This is because the effect of the SLAMF7-LY9 haplotype disappeared, when it was estimated conditional on the variant showing the strongest association in the ITLN1–ITLN2 region (P=0.373) and the effect from the ITLN1–ITLN2 haplotype disappeared when it was estimated conditional on the synonymous coding variant in LY9, rs509749 (P=0.376) (Supplementary Table 4).
Effect of LY9 polymorphism on T-cell subsets
Since LY9 is expressed on peripheral T cells, we wanted to see whether LY9 polymorphisms affected the size of individual T-cell populations in peripheral blood. Regression analysis in Canadian parental samples established that the genotype of the LY9 variant, rs509749, was correlated to the relative numbers of the major T cell subsets, since there was a significant association between the A allele of rs509749 and a decrease in the proportion of both CD4+ naïve T cells (P=0.017) and activated T cells (P=0.025) but an increase in the number of CD8+ memory T cells (P=0.014) (Table 4). Similar trends in T-cell populations were observed in SLE affected individuals (data not shown).
Seven members of the SLAM family are located within the SLE linkage region on human chromosome 1 (1q23), and the syntenic linkage region in mouse (Sle1b). Linkage studies in humans and in animal models of lupus have suggested an important role in SLE. This has been further supported by functional studies in mice and humans that have demonstrated that the SLAM and SLAM-related molecules are cell-surface receptors expressed on multiple haematopoietic cells, where they mediate adhesion between T cells and APCs in the immune synapse and hence regulation of signal transduction in B cells, T cells and APCs.25 The current family-based association testing of this genomic region in two independent populations has provided significant evidence for association to SLE. Specifically, multiple SNPs within the SLAMF7/LY9 region were found to be associated in each of the studies and a combined analysis identified that the strongest association within this region was to rs509749, a nonsynonymous SNP in exon 8 of LY9. We used conditional logistic regression to show that rs509749 was the variant making the strongest contribution to the association observed from the SLAMF7-LY9 region.
LY9 is expressed on the surface of monocytes and present at low levels on NKT cells. However, there is little information regarding the exact function of LY9. In Ly9−/− knockout mice there is a defect in T-cell proliferation and Th2 cytokine production, indicating a positive role for LY9 in T-cell function.26 Conversely, in human T cells, co-ligation of LY9 with the T-cell receptor partially reduces ERK activation and production of the Th1 cytokine IFN-γ, which may indicate a negative role for the protein in T-cell function.25 This apparent discrepancy may be partly due to different down-stream signalling events following interaction of LY9 with one of its adaptor molecules, SAP/SH2D1a. The Src 2 homology domain of SAP/SH2D1a binds with high affinity to two immunoreceptor tyrosine based switch motifs (ISTM) (T-(I/V)-pY-N-N-(I/V)) in the cytoplasmic tail of LY927 and is required for maximal phosphorylation of LY9.25 However, when SAP is bound to LY9, the downstream signalling molecule SH-2 cannot be activated and remains in its inactive state.28 Inactive SHP-2 promotes apoptosis, but active SHP-2 (bound to the phosphorylated pY603 in LY9), will promote the production of inflammatory cytokines,28 as illustrated in Figure 2.
Interestingly, the SNP showing the strongest association in LY9, rs509749, is a nonsynonymous variant lying within the consensus binding site for SAP/SH2D1a (T601-(I/V)602-pY603-N604-N605-(I/V)606; Figure 2). The variant alleles of rs509749 variant are located in the first position of the codon for (I/V602). Since SAP/SH2D1a is an adaptor molecule that directly interacts with LY9 and plays an important role in downstream signalling events, it is possible that the Met602Val change and other variation within the binding site may influence the interaction between these molecules as well as in their signalling cascade. We, therefore, hypothesize that the rs509479 mutation will affect the binding affinity of SAP/SH2D1a for LY9 and thereby determine the direction of these downstream signalling events, as shown in Figure 2. If the A allele, coding for Met602, reduces the affinity of SAP/SH21Da for LY9, the phosphorylated pT603 will be exposed to activate SHP-2, with consequential increased production of inflammatory cytokines and enhanced immune response.
With this in mind, we used regression analysis in Canadian samples to demonstrate that the overtransmitted A allele of rs509749 affected proportions of different T-cell sub-populations, because the risk allele of this variant was associated with decreased numbers of CD4+ naïve T cells and activated T cells and with increased numbers of CD8+ memory T cells (Table 4). This skewing in the T-cell populations may indicate a state of chronic T-cell activation.
Although we have presented evidence that rs509479 is the SNP showing the strongest association in the UK and Canadian SLE families, that it is located within the consensus binding site for SAP/SH2D1a, and that it is correlated with differences in specific T-cell populations, it is formally possible that other variants in LD with rs509479 could actually be the causal variants and/or that variants in addition to rs509479 have a role to play in determining an individual's risk to developing SLE.
In summary, therefore, the data presented in this paper represent an initial screen across the SLAM locus, and the identification of novel associations within the LY9 gene. The variant showing the strongest association is correlated to skewed populations of T cells in peripheral blood. However, there are compelling arguments to support the other members of the SLAM family as SLE susceptibility genes, and so future work will involve high-density mapping of the entire SLAM locus to discover novel associations in these other genes and by the generation of a detailed haplotype structure in the region, to determine the relationship between the existing associations and unknown causal alleles.
Materials and methods
Details of study cohorts
The collection of the UK Caucasian SLE families consisted of a total of 461 complete trios and 199 independent single-parent families (Table 2). There were also 271 independent complete trios collected from several Canadian cities, 103 of which also had siblings. For risk alleles having minor allele frequencies 40–60%, the 732 families used in this collection have a power of 40–42% to detect an association of P=0.05 with an α score of 0.1 (http://pngu.mgh.harvard.edu/~purcell/gpc/dtdt.html). All probands conformed to the ACR criteria for SLE29 with a diagnosis of SLE being established by telephonic interview, health questionnaire and details from clinical notes. Written consent was obtained from all participants, including relatives. In the United Kingdom, ethical approval was obtained from Multi-Centre Research Ethics Committee (MREC) and in Canada, the study was approved by the Research Ethics Board of the University Health Network and each participating recruitment centre.
Preparation of genomic DNA
Genomic DNA from the UK samples was isolated from anti-coagulated whole blood by a standard phenol–chloroform extraction. For Canadian samples, the DNA was extracted from whole blood using the Gentra Autopure LS isolation system (Gentra Systems Inc., Minneapolis, MN, USA) according to the manufacturer's instructions.
Selection of markers
In this study a total of 33 markers were selected across the SLAM locus, which were identified from the public databases, dbSNP (http://www.ncbi.nlm.nih.gov/SNP/index.html) and from HAPMAP (http://www.hapmap.org/). Following preliminary genotyping in 461 UK trios, markers were excluded from the analysis if they showed a genotyping success of less than 85%, had more than 5% of families with Mendelian errors as identified by PEDCHECK and/or had Hardy–Weinberg (HWE) P-value in the parental samples of less than P=0.05 (Table 2). Six markers from the list of SNPs typed in the UK samples were removed from the analysis, because they had more than 5% families showing Mendelian errors, and two further markers were removed because they had a HWE P-value of less than 0.05 in the founder chromosomes. A further marker genotyped in both the UK and Canadian populations was removed from the analysis due to possessing a HWE P-value of less than 0.05. However, none of the 32 markers typed in the Canadian samples failed quality control.
The genotyping in the UK samples was performed using MALDI–TOF mass spectrometry (Sequenom, San Diego, CA, USA)30 and analysis of the raw genotype data carried out using the MassArray Typer v3.4 software (Sequenom). For the Canadian samples, genotyping was performed by the SNPstream UHT platform (Beckman Coulter, Mississauga, Ontario, Canada), with genotypes being called by the UHTGetGenos software (Beckman Coulter). After visual inspection of the clusters, manual adjustments were made for some of the assays, or by the Sequenom iPlex system.
Cellular phenotyping and serologic testing
Peripheral blood mononuclear cells (PBMC) were isolated from heparinized blood by Ficoll density gradient centrifugation. Cells were isolated within 16–20 h of the blood drawing. Samples were transported, processed and analysed by flow cytometry previously described by Wither et al. (manuscript submitted to Arthritis and Rheumatism, 2007). The cellular phenotypes measured were the percentage of invariant NKT cells (CD3+ Vα24+ Vβ11+), CD4+ memory/effector T cells (CD4+ CD45RO+ CD45RA), CD4+ naïve T cells (CD4+ CD45RO− CD45RA+), recently activated CD4+ T cells (CD4+ CD69+), CD8+ memory/effector T cells (CD8+ CD45RO+ CD45RA−) and CD8+ naïve T cells (CD8+ CD45RO− CD45RA+).
All sample genotype and phenotype data were managed by, and analysis files generated with, BC/GENE and BC/CLIN software (Biocomputing Platforms Ltd, Espoo, Finland). Alleles were counted in parental samples, as population controls, for each variant analysed across the SLAM locus. These allele frequencies were calculated for each allele as a fraction of the total alleles for each SNP. A comparison of the parental allele frequencies between the UK and Canadian collections was made from χ2 analysis using a 2 × 2 contingency table, with the level of significance being presented as a P-value with 1 d.f. Pearson's test was used to compare the ratio of transmitted to untransmitted (T:U) families for each marker and the Breslow-Day test in PLINK was used to test homogeneity of odds ratios between the two populations.
Association of alleles to SLE for both individual and multiple SNPs was tested by TDT, which compares the observed and expected transmission of alleles from heterozygous parents to affected offspring. This analysis was carried out using GENEHUNTER (complete trios)31, 32 or TRANSMIT (trios and single parent families).
To determine the variants on a haplotype making the strongest contribution to the haplotype association, conditional logistic regression, using WHAP (http://pngu.mgh.harvard.edu/purcell//whap/) was performed in samples from the UK and Canadian parental-affected trios. This analysis tested the individual contribution to the association in LY9 from SNPs located in the gene or immediately upstream, by conditioning on each constituent variant in turn. To determine whether there were independent association signals in the SLAMF9-LY9 region and the ITLN1–ITLN2 regions, we performed conditional logistic regression for each region. To investigate whether there was an independent signal from the SLAMF7-LY9 region, the SNP showing the strongest association in the ITLN1–ITLN2 region, rs4656959, was used to condition the association from the variants in the SLAMF7-LY9 region. To find out whether there was an independent signal from the ITLN1–ITLN2 region, the nonsynonymous coding variant rs509749 in LY9 was used to condition (control for) the association from the variants in the ITLN1–ITLN2 genes.
The association between genotype at rs509749 and cellular phenotype was evaluated in a mixed regression model using SPSS v.14 in Canadian parental samples. All cellular data showed a normal distribution, with the exception of that for invariant NK T cells (CD3 + Vα24+ Vβ11+) and recently activated CD4+ T cells (CD4+ CD69+). The data for both cell types showed marked positive skews and required log-transformation prior to analysis. Alleles at rs509749 were assumed to act in an additive (co-dominant) manner.
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This work was funded by a grant from the National Institutes of Allergy and Infectious Diseases (AI065687) to Timothy J Vyse, John D Rioux and Cox Terhorst and Arlene Sharpe, through a Senior Fellowship Award from the Wellcome Trust to Timothy J Vyse and by a grant from the Canadian Institutes of Health Research (no. 62840) to Joan Wither and Paul Fortin. We acknowledge the work of Christine Stevens and Angela Richardson for help with the genotyping of the UK samples, Andrew Wong and Paul Spencer in recruiting patients and families into the study and we would like to thank our clinical colleagues for helping us recruit study participants. Our thanks and appreciation is extended to all the patients and their relatives for generously donating blood samples and all the general practitioners and practice nurses for collecting them. We appreciate the contribution to the genotyping from the Broad Institute Center for Genotyping and Analysis, which is supported by grant U54 RR020278-01 from the National Center for Research Resources. We appreciate the contribution to CaNIOS studies made by the Arthritis and Autoimmune Research Centre Foundation and by Lupus Canada. Dr Fortin's salary is supported in part by a Distinguished Senior Research Investigator Award from The Arthritis Society (Canada) and the Arthritis Centre of Excellence, University of Toronto. We thank Glinda Cooper, a GenES co-investigator, for helpful discussions and Jaime O Claudio, the National Scientific and Development Coordinator for CaNIOS for assistance in preparation of the article and Jiandong Su, CaNIOS Database Administrator for assistance with management of the GenES database. We also acknowledge the investigators who contributed patients to the study (see Appendix).
CaNIOS GenES Investigators are as follows. Janet Pope: Division of Rheumatology, St Joseph's Health Centre, London, Ontario, Canada; Dafna Gladman and Murray Urowitz: University of Toronto Lupus Clinic, Centre for Prognosis Studies in the Rheumatic Diseases, Toronto Western Hospital, University Health Network; Department of Medicine, University of Toronto, Toronto, Ontario, Canada; John Hanly: Division of Rheumatology, Department of Medicine, Queen Elizabeth II Health Sciences Centre and Dalhousie University, Halifax, Nova Scotia, Canada; C Douglas Smith: Division of Rheumatology, Ottawa Hospital, Ottawa, Ontario, Canada; Ann Clarke, Sasha Bernatsky and Christian Pineau: Division of Rheumatology, McGill University Health Center, Montreal, Quebec, Canada; Christine Peschken and Carol Hitchon: Winnipeg Health Science Center, Winnipeg, Manitoba, Canada; Michel Zummer: Department of Rheumatology, Maisonneuve-Rosemont Hospital, Montreal, Quebec, Canada; Susan Barr: Division of Rheumatology, Department of Medicine, University of Calgary, Calgary, Alberta, Canada; Gilles Boire: Division of Rheumatology, Department of Medicine, Faculty of Medicine and Health Sciences, Université de Sherbrooke, Sherbrooke, Quebec, Canada; Eric Rich, Jean-Luc Senecal: Division of Rheumatology, Centre Hospitalier de l'Université de Montréal, Department of Medicine, University of Montreal School of Medicine, Montreal, Quebec, Canada; Simon Carette and Robert Inman: Toronto Western Hospital, University Health Network, Toronto, Ontario, Canada; and the CaNIOS research assistants/coordinators that recruited the patients: Sara Hewitt and Janine Ouimet: Division of Rheumatology, St Joseph's Health Centre, London, Ontario, Canada; Tamara McKenzie, Diona Dobaille, Menisha Hodge, Tammy Koonthanan, Kiran Pabla and Yang Zhou: Division of Rheumatology, Toronto Western Hospital, University Health Network; Tina Linehan: Division of Rheumatology, Department of Medicine, Queen Elizabeth II Health Sciences Centre and Dalhousie University, Halifax, Nova Scotia, Canada; Kathryn Drouin: Division of Rheumatology, Ottawa Hospital, Ottawa, Ontario, Canada; Nancy Branco and Elizabeth Piniero: Division of Clinical Epidemiology, Montreal General Hospital, and McGill University, Montreal, Quebec, Canada; Andrea Craig, Diane Ferland, and Donna Hart: Winnipeg Health Science Center, Winnipeg, Manitoba, Canada Winnipeg; Diane Ferland: Department of Rheumatology, Maisonneuve-Rosemont Hospital, Montreal, Quebec, Canada; Whitney Steber and Patrice Nedinis: Calgary Health Sciences Centre, University of Calgary, Calgary, Alberta, Canada; Celine Boulet and Isabelle Gagnon: Department of Medicine, Division of Rheumatology, University of Sherbrooke, Sherbrooke, Quebec, Canada and Diane Therrien: Division of Rheumatology, Hôpital Notre-Dame, Montreal, Quebec, Canada.
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Graham, D., Vyse, T., Fortin, P. et al. Association of LY9 in UK and Canadian SLE families. Genes Immun 9, 93–102 (2008). https://doi.org/10.1038/sj.gene.6364453
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