Human Molecular Genetics, 2001, Vol. 10, No. 25 2907-2916
© 2001 Oxford University Press
Dissection of the HLA association with multiple sclerosis in the founder isolated population of Sardinia
1Dipartimento di Neuroscienze, University of Cagliari, Centro Sclerosi Multipla, Ospedale Binaghi, Via Is Guadazzonis 2, Cagliari 09126, Italy and 2Laboratorio di Immunogenetica, Dipartimento di Scienze Biomediche e Biotecnologie, University of Cagliari, Ospedale Microcitemico, Via Jenner, Cagliari 09121, Italy
Received August 2, 2001; Revised and Accepted October 8, 2001.
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ABSTRACT |
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 TOP ABSTRACT INTRODUCTIONRESULTSDISCUSSIONMATERIALS AND METHODSREFERENCES |
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Several studies have indicated that multiple sclerosis (MS) is associated and linked to the major histocompatibility complex (MHC)/human leukocyte antigen (HLA) region of chromosome 6p21.3, but the exact location and nature of the primarily associated locus within the HLA complex is still controversial and largely presumptive. By linkage disequilibrium mapping, we have systematically investigated this chromosome region in the founder population of Sardinia to determine the relative associations of the various loci with MS. An overall 11.4 Mb region, which encompasses the whole HLA complex, was scanned with 19 microsatellite markers and with single nucleotide polymorphisms within 12 functional candidate genes and assessed for MS association using the extended transmission disequilibrium test (ETDT). A peak of association represented by the three adjacent DRB1, –DQA1 and –DQB1 loci was detected in the class II region. Two additional less significant areas of association were detected, respectively, in the centromeric side of the class II region at the DPB1 locus and, telomeric of the classically defined class I loci, at the D6S1683 microsatellite. Conditional ETDT analysis indicated that these regions of association could be independent of each other. Within the main peak of association, DRB1 and DQB1 contribute to the disease association independently of each other whereas DQA1 had no detectable primary genetic effects. We evaluated the haplotype distribution at the region showing the strongest association and found five DQB1–DRB1 haplotypes positively associated with MS in Sardinia. These consistently included all the haplotypes previously found associated with MS in the various human populations, thus supporting a primary effect of the products of these loci in MS. Overall these results are consistent with a multilocus model of the MHC encoded susceptibility to MS.
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INTRODUCTION |
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TOPABSTRACTINTRODUCTIONRESULTSDISCUSSIONMATERIALS AND METHODSREFERENCES |
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Multiple sclerosis (MS) is a chronic inflammatory and demyelinating disease of the central nervous system which leads to severe neurological disability due to axonal loss (1). The disease, which is more common in young adults, is strongly clustered in families: the sibling lifetime risk/population prevalence ratio,
s, ranges from 15 to 20 (2). Adopted siblings of MS patients have the same risk of MS as individuals in the general population; thus the observed familial clustering is more likely due to sharing of genes rather than environmental factors (3). The rapid reduction in risk from monozygotic twins (30.8%), to full siblings (3.46%), to half siblings (1.47%) and to cousins (0.88%) suggests the presence of a polygenic trait with multiplicative interactions between the various disease loci (2,4,5). Three independent whole genome scans were not able to locate major predisposing loci (6–9). However, some of these linkage studies demonstrated a modest, but definite effect of the major histocompatibility complex (MHC)/human leukocyte antigen (HLA) region. This is in agreement with a long series of case-control studies showing an association of specific HLA class II haploypes with MS (10). However, despite this prior evidence indicating that MS is linked (6–8,11) and associated (reviewed in 10) to the MHC region, to date there are no systematic studies addressed to fine map the primary disease associated locus (i). Moreover, the association of MS with the HLA class II haplotypes appears to be heterogeneous in different populations. The most common association is with the DRB1*1501–DQA1*0102–DQB1*0602 haplotype (the serologically defined DR2, DQw1 haplotype) detected in European (12) and in European-derived populations (13) as well as in Ashkenazi and non-Ashkenazi Jews (14), in Turkish (15) and in Mexican Mestizo (16) patients. In the Sardinian population, the disease has been found to be associated with the DRB1*0301–DQA1*0501–DQB1*0201 and DRB1*0405–DQA1*0501–DQB1*0301 haplotypes (17,18). Until now little effort has been made in the identification of the disease loci within the HLA/MHC. Within the MHC region, identification of the primary variant responsible for MS susceptibility is complicated by the high degree and irregular distribution of the linkage disequilibrium (LD) observed between the different loci (19,20). Proof of a primary association of any HLA locus must take into account the LD patterns and therefore requires dense maps of markers, very large clinical resources and statistical methods that are able to delineate the relative associations and the individual contributions of the various loci once LD has been taken into account. Moreover, even the choice of the population to study appears critical in LD mapping surveys. The Mediterranean island of Sardinia appears well suited to identify the MHC loci involved in MS predisposition. With approximately 140 cases per 100 000 inhabitants, it has one of the highest incidences of MS in Europe (21). Hence, large clinical resources can be collected on the island. The Sardinian population does not show large-scale genetic heterogeneity (22). This reduces the risk of artifacts owing to population admixture, which can seriously complicate case-control studies. Sardinia is a genetic isolate; the present population is the result of a fixation of alleles and haplotypes, rare or absent elsewhere (23). Since the patterns of LD around the etiologic allele(s) could vary in different populations, this genetic differentiation might be extremely useful for fine-mapping analysis in order to define which HLA alleles and motifs are shared in the patients from distantly related populations. These inter-population shared regions are most likely to contain the etiologic polymorphisms.
We mapped the main HLA disease component to the loci DRB1 and DQB1 encoding the ß subunits of the HLA class II presenting molecules. Other additional genetic effects are associated with the locus encoding the ß subunit of the third class II presenting molecule DPB1, and at the D6S1683 microsatellite.
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RESULTS |
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TOPABSTRACTINTRODUCTIONRESULTSDISCUSSIONMATERIALS AND METHODSREFERENCES |
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Linkage disequilibrium mapping
We initially evaluated a sample set of 375 Sardinian MS families using the extended transmission disequilibrium test (ETDT). Nineteen microsatellite markers and a number of single nucleotide polymorphisms (SNPs) defining allelic variation at 12 expressed genes across an 11.4 Mb region which encompasses the whole HLA complex, were scored. Six markers in three regions showed some evidence of association at or below the 5% significance level after correction by 31, the number of markers tested (Fig. 1). A main area of association was located in the class II sub-region and peaked at DQB1 (P = 4.4 x 10–6, Pc = 1.4 x 10–4) including also D6S2447 (P = 1.5 x 10–4, Pc = 4.7 x 10–3), DQA1 (P = 1.1 x 10–5, Pc = 3.4 x 10–4) and DRB1 (P = 1.1 x 10–5, Pc = 3.4 x 10–4). In the centromeric portion of the class II region there was a second area of association defined by DPB1 (P = 2.5 x 10–4, Pc = 7.8 x 10–3). Evidence for a third potential association was located telomeric of the classical class I loci and peaked at D6S1683 (P = 9.1 x 10–4, Pc = 2.8 x 10–2).
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Next, we wanted to assess whether these associations were independent of each other or whether some of them reflected hitchhiking effects owing to the LD between these loci. To address this, we used a variant of the ETDT, the conditional ETDT (CETDT) (Materials and Methods), which allowed us to analyse the overall effects of allelic variation at these loci once LD between them had been taken into account. To provide more power for this conditional analysis, an additional 115 Sardinian MS families were genotyped (total number of families analysed 490) at the peak loci in the three regions of association (DPB1, DQB1, DQA1, DRB1 and D6S1683) (Table 1). We initially evaluated, with the CETDT, the individual contribution of the DQB1, DQA1 and DRB1 loci to MS within the individual peak of association. DRB1 conditioned on DQB1–DQA1 and DQB1 conditioned on DRB1–DQA1 were significantly independent at or below the 5% level of significance in these 490 MS families (CETDT P = 4.9 x 10–3 and 5.3 x 10–2, respectively). The DQA1 locus, when conditioned on DQB1–DRB1 had no significantly detectable genetic effect in this population (CETDT P = 0.2). The association of DQB1–DRB1 was also independent of DPB1 and D6S1683 (CETDT P = 3.6 x 10–6 and 1.7 x 10–3, respectively).
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We evaluated the individual contribution of the other areas of association. In the most centromeric region, DPB1 was analysed conditional on DQB1–DRB1 and D6S1683, respectively, and found that it was associated in all cases (CETDT P = 3.5 x 10–4 and 2.7 x 10–2, respectively). In the most telomeric area of association, D6S1683 was independent of DQB1–DRB1 and DPB1 (CETDT P = 2.0 x 10–2 and 7.1 x 10–3, respectively).
A critical issue in LD mapping projects is the marker spacing and intermarker LD within the map. Based on previous analyses showing that the LD is stronger in the HLA class I and III in comparison with the class II region (20,24,25), we used an uneven distribution of markers with higher marker density in the class II than in the class I and III. This distribution is also consistent with the observed hot spots for recombination detected within the class II region (19,20,26). Nevertheless, we wanted to assess the coverage of the map we used in this study by checking the intermarker LD, measured as normalized D' values and corresponding P-values (Materials and Methods), between the various markers considered in this study. As shown in Figure 2, there was good coverage, with strong LD for all the contiguous markers in the HLA regions of interest. Moreover, as found with the CETDT analysis, it is also evident that the average LD between the markers defining the peaks of association is consistent with the independence of effects.
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Association of the individual alleles and haplotypes with MS
By analysis of the relative predisposition of the two-locus DRB1–DQB1 haplotypes (Table 2), we can see which haplotypes underlie the associations. We have included in this analysis the familial sample set of 490 families (previously considered in the CETDT analysis) and an additional independent case-control sample set of 378 sporadic patients and 631 newborns. The results obtained in these two data sets were very similar and were merged to establish the relative associations of the various haplotypes with MS in the Sardinian population (Materials and Methods). Overall, there were five DRB1–DQB1 positively associated haplotypes going from HLA–DRB1*1303–DQB1*0301 [odds ratio (OR) = 2.7, P = 2.8 x 10–4], followed by DRB1*0405–DQB1*0301 (OR = 2.2, P = 6.0 x 10–7), DRB1*0301–DQB1*0201 (OR = 1.7, P = 6.1 x 10–12), DRB1*1501–DQB1*0602 (OR = 1.6, P = 4.5 x 10–2) and DRB1*0405–DQB1*0302 (OR = 1.5, P = 1.4 x 10–2). There were several DRB1–DQB1 haplotypes showing an apparent negative association with MS (Table 2). However, no significantly negatively associated haplotypes were present when displacement effects driven by the positively associated haplotypes were taken into account by using the relative predispositional effect (RPE) method (27) (Table 3). Additionally, it should be noted that the DRB1*1601–DQB1*0502 haplotype, which shows an apparent negative association with the disease, and the DRB1*1501–DQB1*0602, which is positively associated with the disease in this sample set and in several populations, share the same DQA1*0102 allele, further suggesting that the DQA1 locus is not contributing in a major way to the genetic susceptibility to MS.
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We also considered the association of particular alleles at the DPB1 and D6S1683 loci in the 490 MS families (Tables 4 and 5). At DPB1, the DPB1*0301 allele was positively associated with the disease (OR = 1.6, P = 1.4 x 10–5; Table 4). As for the DRB1–DQB1 haplotypes also at DPB1 no significantly negatively associated alleles were present when displacement effects driven by positively associated alleles were taken into account by using the RPE method (27) (data not shown).
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At D6S1683, allele 4 (186 bp) and allele 3 (184 bp) were, respectively, significantly increased and decreased in patients (OR = 1.5, P = 2.8 x 10–5 and OR = 0.5, P = 2.0 x 10–5, respectively; Table 5). The negative associations of allele 3 at D6S1683 appear to be genuine in that significant results for this allele were obtained also using the RPE method (27) (data not shown).
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DISCUSSION |
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TOPABSTRACTINTRODUCTIONRESULTSDISCUSSIONMATERIALS AND METHODSREFERENCES |
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We have analysed a large sample set of MS patients from the isolated founder population of Sardinia to establish the disease-associated component(s) within the MHC/HLA region. Using a LD mapping approach, we have found that within this region the DRB1–DQB1 loci are associated with MS in this population. Moreover, at least two other markers, namely DPB1 and the microsatellite D6S1683, were also associated with risk of MS. Conditional analysis revealed that these multiple associations were independent of each other after taking into account LD. The overall genetic contributions of the HLA loci to MS susceptibility were relatively modest compared to other HLA-associated diseases such as type 1 diabetes (T1D) (25). For instance, contrast the strength of association and the statistical significance of the main peak in the class II region at DRB1 observed in 257 Sardinian T1D families (ETDT = 252.5, P =3.6 x 10–42) (25) and in the 375 MS families reported in this study (ETDT = 73.4, P = 1.0 x 10–5). These results are also consistent with the previously reported relatively low contribution of the MHC/HLA region to the familial aggregation of susceptibility to MS (9,28). Despite the relatively small genetic effect of the HLA region in MS predisposition, a number of DRB1–DQB1 haplotypes conferring susceptibility to MS were detected in this large Sardinian sample and consistently accounted for the main associations previously reported in the other human populations. For instance, the DRB1*1501–DQA1*0201–DQB1*0602 and the DRB1*1303–DQA1*0501–DQB1*0301 haplotypes were found to be respectively associated with MS in all the northern European populations (12,13) and in non-Ashkenazi Jews (29) as well as patients from Minnesota (USA) (30) and were among the most positively associated haplotypes also in this study of Sardinian patients. These haplotypes are too rare in the general Sardinian population (1.4 and 0.8%, respectively) to have permitted the detection of their positive association with MS in previous studies on smaller sample sets (18). Similarly, the DRB1*0405–DQA1*03–DQB1*0302 and the DRB1*0301–DQA1*0501–DQB1*0201 haplotypes, associated with MS in the Sardinian population, have been found to be associated with MS, respectively, also in Turkey (15) and in some studies of northern European (31) and non-European (16) patients. The presence of a multiallelic association at these class II loci, is also suggested by a recent report in which some evidence of linkage was obtained not only on affected sib pair families with parents carrying DRB1*15 but also in a subgroup of affected sib pair families with parents negative for DRB1*15 (28). These findings indicate that, using an adequate sample size to provide sufficient statistical power and an informative population, virtually all the worldwide HLA-susceptibility determinants for MS can be found.
These similarities between distinct populations indicate a primary association of these class II loci with MS, thus supporting a direct role of their products in the disease pathogenesis. Also, alleles of the locus HLA–DPB1, encoding for the ß subunit of the third HLA class II presenting molecule, might be primarily associated with MS. This is consistent with the fact that the DPB1*0301 allele found to be associated with MS in this Sardinian sample, was also associated with the disease in the distantly related population of Japan (32) and of Australia (33). Moreover, the DPB1*0301 allele was found to preferentially restrict the T-cell responses against an epitope within the myelin proteolipid protein (PLP) which together with the myelin basic protein (MBP) and the myelin oligodendrocyte glycoprotein (MOG) is one of the candidate autoantigens in MS (34).
Further sequencing and association analyses on larger sample sets are necessary to allow the exclusion mapping of MHC polymorphisms responsible for the MS association of the D6S1683 microsatellite in the telomeric portion of the HLA region. Irrespective of the nature of the DNA variation primarily responsible for the HLA telomeric association, these results are consistent with a report showing, in a Scadinavian sample set, a modifying effect of HLA class I markers on the association of the HLA class II DRB1*1501–DQA1*0102–DQB1*0602 haplotype (35). In another report the microsatellite D6S461, 10.7 cM telomeric of HLA–DRB1, showed significant evidence for LD with the disease, in three independent data sets, suggesting a gene effect in this region (6).
Taken together these observations support a complex multilocus, and in the case of DRB1–DQB1, multiallelic model, of the MHC-encoded susceptibility to MS. However, in the absence of all the markers and haplotypes in the region, it is difficult to firmly establish the actual number and the relative importance of loci involved, and formally, it is still possible, although highly unlikely, that an untyped locus might explain all the associations detected in the MHC.
Overall, our results are fully consistent with the rat animal model in which specific MHC haplotypes determined the degree of disease susceptibility, clinical course and central nervous system pathology in a hierarchical and allele-specific way, with the major effect being mapped on the MHC class II subregion and with modifiers in the MHC (36). Interestingly, as we observed in our patients, as in the rat model, no single MHC class II haplotype was able to provide dominant protection (36).
These observations have some mechanistic implications. The observed lack of dominant protection might indicate that HLA class II presenting molecules are unlikely to play a major role in dominant tolerance mechanisms, such as the clonal deletion of autoreactive T cells in the thymus, if these mechanisms are operative at all in conferring a resistance from MS. In contrast, T1D HLA class II associations show a highly skewed distribution from susceptibility to dominant protection (37). Moreover, the multiple associations of MS with specific HLA class II haplotypes seen in humans might underlie the presence of distinct immunopathogenetic pathways involved in the presentation of different critical autoantigens (or different epitopes from the same autoantigen) to CD4 T cells. However, we cannot exclude the possibility that this apparent class II allelic heterogeneity is instead related to the presence of common structural motifs shared in a complex way by different HLA–DR, –DQ, –DP disease-associated molecules with one or very few primary autoantigenic epitopes. Mechanistic considerations aside, we can conclude that MS is primarily associated in humans, as it is in the rat animal model, to the MHC loci encoding the ß chains of the class II antigen presenting molecules and to additional unknown susceptibility gene polymorphism(s) in LD with the microsatellite D6S1683.
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MATERIALS AND METHODS |
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TOPABSTRACTINTRODUCTIONRESULTSDISCUSSIONMATERIALS AND METHODSREFERENCES |
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Subjects
The whole family data set consisted of 490 Sardinian MS families and included 444 simplex families (one affected child and both parents), 40 MS multiplex families (more than one affected sibling and their parents) and six ‘vertical’ families with one parent and one affected offspring. 264 of these families have been previously analysed at the DRB1–DQA1–DQB1 loci (18). We also considered an independent sample set of 378 MS sporadic patients (total independent Sardinian patients equal to 868). 56.4% of the patients were from the province of Cagliari, 25.7% were from the province of Sassari, 11.4% were from the province of Nuoro and 6.5% were from the province of Oristano. Among the MS patients there were 593 females and 275 males (F/M ratio 2.16), mean age 35.4, range 10–72 years. The disease’s course was relapsing remitting in 73%, secondary progressive in 19% and primary progressive in 8% of patients. All index patients and affected first-degree relatives of index patients met the criteria of clinically or laboratory-supported definite MS (38). Patients were born and lived in Sardinia and were of Sardinian descent from at least three generations.
In the present study we have also considered the DRB1–DQA1–DQB1 frequencies of 631 Sardinian newborns that have been previously reported by Lampis et al. (22).
Microsatellites and sequence-specific oligonucleotide (SSO) typing
375 MS families were genotyped for 19 microsatellite markers (D6S291, D6S439, D6S1629, D6S1560, D6S2445, D6S2444, D6S2447, D3A, D6S273, 82-1, TNFc, TNFa, C1-2-A, D6S265, MOG51, D6S1683, D6S258, D6S306 and D6S2223) and for a number of SNPs defining allelic variation at 12 expressed genes (Tapasin, DPB1, DMB, TAP-1, TAP-2, DOB, DQB1, DQA1, DRB1, HSP70-1, HSP70-2 and HSP70–HOM). An additional 115 MS families were genotyped at the loci peaking the regions of association (DPB1, DQB1, DQA1, DRB1 and D6S1683) (Results). The primer sequences for D6S291, D6S439, D6S1629, D6S1560, D6S2445, D6S2444, D6S2447, D6S273, C1-2-A, D6S265, D6S1683, D6S258 and D6S306, were obtained from Foissac and Cambon-Thomsen (39). The primer sequences for MOG51 were obtained from Malfroy et al. (40). Sequences for TNFa and TNFc were obtained from Udalova et al. (41). The primer sequence for D6S2223 was obtained from The Genome Database (GDB; http://www.gdb.org). Sequences for D3A and 82-1 were established by Hsieh et al. (42). Genotyping was performed by separating fluorescently tagged PCR products on a polyacrylamide gel using ABI 377 sequencer and the GeneScan 3.1, Genotyper 2.0 software (Perkin-Elmer, Applied Biosystems). PCR product standards consisting of the amplification product of two different standards for each marker were loaded on each gel for correct allele assignment. The two standards consisted of the Centre d’Etude des Polymorphisms Human (CEPH) individual number 1347.02 and a pool of DNAs. The alleles at each microsatellite were given a numerical value (1, 2, 3, etc.) starting with the allele with the lowest number of base pairs. The physical map of the region with relative order, map position and distances between markers were obtained from the Sanger Centre (http://www.sanger.ac.uk/HGP/Chr6/MHC.shtml; A.Mungall, personal communication). Physical map position of the microsatellite markers are shown in Figure 1. Markers were PCR amplified and genotyped a second time when showing failures during the first round of amplifications. On average 75.9% of all parents were heterozygous for the microsatellites investigated, with outlying markers D6S2444, TNFc, D6S2223, D6S2445, showing heterozygosity lower than 60%. The average number of alleles per microsatellite was 11, but it was only three when we considered alleles with a parental frequency of at least 10%.
The expressed genes considered in this study were typed as follows. The polymorphic second exons of the HLA–DRB1, –DQA1, –DQB1, –DPB1 genes were amplified and the amplified products were dot-blot analysed using primers and SSO probes described previously (43–45). This includes characterization of 36, 9, 15 and 22 alleles for the DRB1, DQA1, DQB1 and DPB1 loci, respectively.
DMB (six alleles) polymorphisms were typed using primers and conditions previously described by Djilali-Saiah et al. (46). DOB (two alleles) was typed with amplification of the fourth exon and subsequent dot-blot analysis of the amplified products using 5'-GTGTCTAGTACAGATTCTG-3', 5'-CACTCCTCACAGGCTCAT-3' as PCR primers and 5'-GTGGGAATCATCATCCAG-3', 5'-GTGGGAATCGTCATCCAG-3' as (SSO) probes.
The Tapasin gene (two alleles) was typed by amplification of the fourth exon and subsequent dot-blot analysis of the amplified products using 5'-AAATGGGACCTTCTGGCTGC-3', 5'-AAGCTCCAGGGTGACCTGTC-3' as PCR primers and 5'-GGCTGCCTAGAGTTCAACCC-3', 5'-GGCTGCCTACAGTTCAACCC-3' as (SSO) probes. (J.Copeman, personal communication).
TAP-1 (four alleles) and TAP-2 (eight alleles) were typed according to Powis et al. (47).
HSP70-1 (two alleles), HSP71-2 (two alleles) and HSP–HOM (two alleles) were typed according to Vinasco et al. (48).
Statistical analysis of the data
The degree of association of the various loci with MS was established using the ETDT (49). This test takes into account the transmission or non-transmission of alleles of a marker relative to the alleles of the marker present on the other parental chromosome. The ETDT takes multiple alleles into account and obtains a global P-value indicative of the degree of significance of the association with the disease of all allelic variation at each individual locus. Therefore, the ETDT could be considered a particularly suitable method for the analysis of a region showing a multiallelic association. The P-values obtained with the ETDT were corrected for the number of markers tested, 31.
In order to distinguish primary associations from those due to LD at the test loci, we used a variant of the ETDT, the CETDT (50). This test allows us to analyse the overall effect of one locus taking the association of other linked loci into account. The CETDT compares the transmission of haplotypes constructed from all the loci against the null hypothesis that all haplotypes identical at the conditioning loci have equal transmission probabilities. This conditional method appears particularly suitable in the presence of multiallelic association at the conditioned locus.
To establish which alleles and haplotypes were accounting for the main locus contributions detected with the ETDT and CETDT analyses, we performed an analysis of the relative association of the various markers using a case-control design. The control frequencies of the various alleles and haplotypes, referred to as affected family-based control (AFBAC), have been calculated as described by Thomson for single alleles (51). The distribution of the patient and control alleles and haplotypes, determined with a gene counting procedure, was then arranged in a 2 x 2 contingency table and tested by Fisher’s exact or Pearson’s chi-squared test and the OR computed. This analysis was used to establish the relative association of alleles and haplotypes at loci with prior evidence of association (ETDT and CETDT results). For this reason, in this case we did not apply a correction for multiple testing. Moreover, in the case of particular DRB1–DQB1 haplotypes and DPB1 alleles, the prior evidence of association was also strongly supported by specific prior evidence obtained in different studies from distinct populations (Discussion).
When more than one allele or haplotype is associated with the susceptibility to a complex trait, the calculation of ORs might be influenced by the frequency and penetrance of the other associated alleles (or haplotypes) at the same disease locus (i). In particular, it is difficult to distinguish whether the negatively associated alleles are such by default; that is, their reduced frequencies in patients are due to algebraic constraints (the sum of the frequencies must be 100%) driven by the positively associated alleles or whether they are genuinely negatively associated and hence indicative of active protection. To establish the RPE of the various alleles (or haplotypes) we have applied the RPE method (27). The RPE method calculates a P-value for each allele according to the formula 
2 = (Oi – Ei)2 / Ei, where Oi is the observed number of occurrence of allele i in patients and Ei is the number expected according to the control allele frequency. The total P-value for the data set is calculated by adding together the non-zero alleles. If the total P-value is less than the significance criteria (in our analysis P = 0.0001) then the alleles with the smallest P-value are sequentially removed from the data set and the expected frequencies are re-calculated as though that allele did not exist. This procedure is iterated until the total P-value becomes non-significant.
Haplotypes in MS families were established following the co-segregation of alleles within families and using computer program TDTPHASE written by F.Dudbridge and available at http://www-gene.cimr.cam.ac.uk/tdt/. Only certain haplotypes from parental genotype data, and in the absence of inter-crosses (that is when both parents are heterozygous for the same alleles), were considered in the analyses shown in this work. In all the association analyses performed, we selected the probands from all the families with more than one affected sibling. The DRB1–DQB1 haplotypes in the 631 control newborns and in the 378 MS sporadic patients were assigned following the known patterns of LD in Caucasians and Sardinians (22,52). In the case of rare associations, the haplotypes were accepted only when the haplotype present on the other chromosome was well defined. Ambiguous assignments were resolved by excluding those individuals.
The LD patterns between the various marker loci measured in this study were calculated on the parental chromosomes from 375 MS families using a normalized disequilibrium (total D') multiallelic extension of Lewontin’s standardized measure of disequilibrium (53,54). The D' values range from 0 to 1, with 0 reflecting perfect independence between alleles at the two loci compared and 1 reflecting complete LD (53,55). The respective P-values were calculated using the Markov chain method described by Guo and Thompson (54) (available at http://anthropologie.unige.ch/arlequin). In all cases, 100 000 tables were explored. This P-value is reported with each pairwise comparison and was considered significant each time the ratio was <0.05. LD P-values were corrected for multiple testing using the step-down Holm-Sidak procedure as described in Lautenberger et al. (56). Each P-value is corrected by the formula Pc = 1 – [1 – P(uncorrected)]n where n is the number of uncorrected P-values for that locus less than or equal to the value to be corrected. For example if the eighth most significant P-value was 0.01 before correction, the corrected value would be Pc = 1 – (1 – 0.01)8 = 0.0773.
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ACKNOWLEDGEMENTS |
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We are grateful to the patients and their relatives for their kind co-operation in our study. We also wish to thank John Todd and Antonio Cao for continuous help, advise and support, Cesare Zavattari for writing a program that allows assignment of the allele sizes into their appropriate allele bins, Heather Cordell and Frank Dudbridge for statistical advice, James Copeman for information about tapasin, Rosanna Lampis for information about primers and PCR conditions to genotype the SNPs within the MHC region, Andrew Mungall (Sanger Center) for the establishment of the physical map. The work has been supported by Federazione Italiana Sclerosi Multipla and Dompé-Biotec grants. The DNA MS bank is supported by a grant of the Federazione Italiana Sclerosi Multipla.
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FOOTNOTES |
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+ To whom correspondence should be addressed. Tel: +39 070 6095681; Fax: +39 070 6095558; Email: fcucca@mcweb.unica.it Correspondence may also be addressed to M.G.Marrosu. Tel: +39 070 6092806; Fax: +39 070 6092929; Email: gmarrosu@unica.it
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REFERENCES |
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TOPABSTRACTINTRODUCTIONRESULTSDISCUSSIONMATERIALS AND METHODSREFERENCES |
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