Human Molecular Genetics, 2000, Vol. 9, No. 20 2967-2972
© 2000 Oxford University Press
Confirmation of the DRB1-DQB1 loci as the major component of IDDM1 in the isolated founder population of Sardinia
1Dipartimento di Scienze Biomediche e Biotecnologie, University of Cagliari, Via Jenner, Cagliari 09121, Italy, 2Servizio di Diabetologia Pediatrica, Ospedale G. Brotzu, Via Peretti, Cagliari 09121, Italy and 3Wellcome Trust Centre for Molecular Mechanisms in Disease, University of Cambridge, Addenbrooke’s Hospital, Cambridge CB2 2XY, UK
Received 18 August 2000; Revised and Accepted 13 October 2000.
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ABSTRACT |
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 TOP ABSTRACT INTRODUCTIONRESULTSDISCUSSIONMATERIALS AND METHODSREFERENCES |
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There is considerable uncertainty and debate concerning the application of linkage disequilibrium (LD) mapping in common multifactorial diseases, including the choice of population and the density of the marker map. Previously, it has been shown that, in the large cosmopolitan population of the UK, the established type 1 diabetes IDDM1 locus in the HLA region could be mapped with high resolution by LD. The LD curve peaked at marker D6S2444, 85 kb from the HLA class II gene DQB1, which is known to be a major determinant of IDDM1. However, given the many unknown parameters underlying LD, a validation of the approach in a genetically distinct population is necessary. In the present report we have achieved this by the LD mapping of IDDM1 in the isolated founder population of Sardinia. Using a dense map of microsatellite markers, we determined the peak of LD to be located at marker D6S2447, which is only 6.5 kb from DQB1. Next, we typed a large number of SNPs defining allelic variation at functional candidate genes within the critical region. The association curve, with both classes of marker, peaked at the loci DRB1-DQB1. These results, while representing conclusive evidence that the class II loci DRB1-DQB1 dominate the association of the HLA region to type 1 diabetes, provide empirical support for LD mapping.
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INTRODUCTION |
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TOPABSTRACTINTRODUCTIONRESULTSDISCUSSIONMATERIALS AND METHODSREFERENCES |
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It is anticipated that linkage disequilibrium (LD) mapping can be used to locate disease susceptibility genes in common multifactorial diseases (1). However, even though many genome-wide linkage studies have been carried out in complex diseases, so far no convincing sublocalization of a disease-associated region, containing markers in LD with the disease phenotype, under the peak of linkage has been reported. The main factors accounting for these failures are represented by the locus and allelic heterogeneity and by the low penetrance of the polygenes involved in complex traits (2,3). However, part of the explanation lies also in technical barriers, which include the absence of the human genome sequence, a dense map of polymorphisms each with a robust assay and an affordable high throughput method of scoring them in data sets large enough to provide substantial statistical power. Rapid progress is being made towards lowering these barriers but the question remains that even if we had a very dense map of single nucleotide polymorphisms (SNPs), could we find the aetiological SNPs in the face of the likely model of multiple disease susceptibility genes with multiple disease-associated alleles within one linked chromosome region?
Many of the features of this ‘worst-case’ scenario for the genetic basis of common disease are provided in the association of the human leukocyte antigen (HLA) region on chromosome 6p21 with the autoimmune disease type 1 diabetes mellitus (T1DM), namely multiple genes and allelic heterogeneity [insulin-dependent diabetes mellitus 1 (IDDM1)] (4). We therefore used IDDM1 as a model locus to test the possibility of mapping the aetiological polymorphism to any degree of confidence. An advantage of IDDM1 is that it is a major disease locus in every population studied so far in which affected sib pairs have been genotyped and from which the locus-specific sibling risk:population prevalence ratio (
s) can be estimated (IDDM1 s = 2.7 in UK families). Hence, in a modest number of families (n = 200–400), very significant LD values between marker alleles and disease can be obtained. One possible criticism of the choice of the HLA region is the ‘unusual’ degree of LD observed in the region. However, given that several other regions in the genome show even greater LD (5) and that, in general, systematic studies of the LD patterns in the human genome are in their infancy, it is perhaps premature to reach this conclusion.
We previously carried out such an LD mapping analysis of IDDM1 in 385 UK affected sib pair (ASP) families, the UK being a large cosmopolitan population (6). Transmission analysis of alleles of 25 polymorphic microsatellite markers allowed the sublocalization of IDDM1, or at least its major determinant in these UK families, to a 570 kb region. The peak of LD with disease was at marker D6S2444. D6S2444 is within 85 kb of the HLA-DQB1 locus, which is, given a combination of genetic and biological data, an established primary component of IDDM1. However, LD between markers and the disease polymorphism depends not only on patterns and frequency of recombination along chromosomes but also on several largely immeasurable factors such as population demography, including effective population size, population admixture, mutation, drift, breeding system and selection (2). Therefore, it was crucial to validate the approach in a population genetically and demographically different from the UK population. In order to increase mapping resolution and to rule out the possibility that the LD map obtained in the UK population was a specific property of polymorphic microsatellite markers we also incorporated SNPs into the map.
The population of Sardinia offered several advantages for this analysis. Sardinia is not a mixed population. It is historically well separated from the UK population by at least 15 000 years and 750 generations, and indeed it is well known that several HLA haplotypes vary significantly in frequency between the UK and Sardinians. The demographic features of the two populations could not be more different, whilst still demanding that both be white European. If factors other than the activity and frequency of recombination points were influencing the UK mapping results or if the HLA class II loci are not the major determinants of IDDM1, in Sardinia or even in the UK, then LD mapping in Sardinia could well give very different results. However, a clear confirmation is provided by this study of Sardinian families, in which we are able to accurately map the main HLA disease component to the loci DRB1 and DQB1. Indeed, in Sardinia the most associated marker was 6.5 kb from DQB1. Our empirical data are discussed considering the implications for the application of LD mapping strategies in multifactorial disorders.
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RESULTS |
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TOPABSTRACTINTRODUCTIONRESULTSDISCUSSIONMATERIALS AND METHODSREFERENCES |
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LD mapping
Twenty-two microsatellite markers covering 9.452 Mb of the extended HLA region were scored in 257 Sardinian T1DM families (Fig. 1). The peak of association was at D6S2447, 6.5 kb telomeric of HLA-DQB1 [extended transmission disequilibrium test (ETDT) Pc = 7.6 x 10–31]. Note that marker D6S265 is located close to the classical HLA locus HLA-A and 2478 kb telomeric of DQB1, and was still strongly associated with the disease (Pc = 5.4 x 10–5), whereas marker D6S2444, located only 85 kb centromeric of DQB1, showed no association. Remarkably, the overall shape of the curve was very similar to that obtained previously in UK families (shown in Fig. 1 as a dashed line). Next, we typed 106 SNPs, including 94 in the HLA-DPB1, -DQB1 and -DRB1 genes, in order to increase the informativity of the map and to provide a comparison of the associations of markers with those of the actual aetiological SNPs (in the exon 2 sequences of the HLA-DQB1 and -DRB1 genes). The other SNPs were chosen because they were in functional candidate genes (tapasin, DMA, DMB, LMP2, LMP7, DOB and HSP70–2) and had common enough minor allele frequencies to provide statistical power in the association study. Ostensibly, the shape of the plot with all 33 loci markers did not change (Fig. 2) except that statistical significance of the main peak in the class II region increased from –log10(Pc) = 30.1 to –log10(Pc) = 42.9 by inclusion of the aetiological SNPs in the exon 2 sequences of the HLA-DQB1 and -DRB1 genes.
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Two other features of the mapping results are notable: first, the relation between the association of the markers with the disease and their patterns of global LD with the disease loci DRB1-DQB1 and, second, the importance of the allele specific LD patterns of the neutral markers with the disease loci.
Figure 3 shows, as a dashed line, the pairwise D' multiallelic values detected between each marker and DRB1-DQB1, here considered as a superlocus, and the respective association with the disease, expressed as the –log of the P value previously obtained with the ETDT. The associations of the various loci with the disease tend to follow those with DRB1-DQB1. In particular, LD of the various loci with DRB1-DQB1 was stronger in the telomeric side than in the centromeric part of the map. Accordingly, the allelic association curve drops more suddenly in the region centromeric of DQ-DR and more linearly on the telomeric side in the chromosomal region located between the DRB1 and the class I loci, which encompasses the whole class III region. Additionally, we evaluated LD between the various loci and DR3 (DRB1*0301-DQB1*0201) and DR4 (DRB1*0405-DQB1*0302), which are the main predisposing haplotypes in this population (Fig. 4 and Materials and Methods). There was a remarkable similarity in the shape of these curves except for the class III (markers D6S273 and TNFc) and the most telomeric part of the map (markers D6S2223), where LD of the various markers with DR3 was stronger than with DR4.
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The importance of the allele-specific LD patterns is illustrated by the behaviour of marker D6S2444. This marker, located 85 kb centromeric of DQ, was in very significant LD with the DRB1-DQB1 disease superlocus (D' = 0.55, P < 1 x 10–7) but was not associated with the disease itself (ETDT Pc = 0.19 ). Allele 2 (153 bp) of D6S2444 is the most common allele in the Sardinian sample but was in positive LD both with (DR3) DRB1*0301-DQA1*-0501-DQB1*0201 (D' = 0.5, Pc < 1 x 10–7) and (DR5) DRB1*11–12-DQA1*-0501-DQB1*0301 (D' = 0.7, Pc < 1 x 10–7), which represent, respectively, the most common predisposing and the most common protective haplotypes in the Sardinian population. This marker allele distribution, i.e. the same allele present on protective and predisposing haplotypes, results in a cancelling out of the marker association with the disease. Note that marker D6S2444, despite its lack of association with the disease using the single point analysis, was strongly associated using a two-point analysis, in conjunction with D6S2447 (ETDT Pc value = 1.23 x 10–25).
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DISCUSSION |
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TOPABSTRACTINTRODUCTIONRESULTSDISCUSSIONMATERIALS AND METHODSREFERENCES |
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Using a high-resolution allelic association study of the HLA region in Sardinian T1DM families, we have demonstrated that the HLA class II genes DRB1 and DQB1 are the main determinants of IDDM1 in this population. Remarkably, the overall shape of the plot obtained in this study of Sardinian families is very similar to that previously observed in the distantly related population of the UK. Taken together the Sardinian and UK results are conclusive evidence that the DRB1-DQB1 HLA class II loci dominate the association of the major histocompatibility complex (MHC) region with the disease.
Overall, our empirical results support the feasibility of LD mapping in candidate regions. Nevertheless, based on our data some recommendations for strategies are advised. As illustrated in Figure 2, the allelic association curve is heavily influenced by the global LD patterns of the various markers with the disease loci. As the multiallelic D' declines to 0.6, the degree of association with the disease decreases rapidly. These results suggest that marker density in the critical region must be high in order to ensure strong LD between the markers employed in the map and the aetiologic SNPs (7). Our data also suggest that the underlying pattern of LD between the neutral markers and the disease locus determines the general shape of the association curve. In the case of IDDM1, differences in the LD patterns of the various loci with DRB1-DQB1 explain why both in the UK and in the Sardinian samples the allelic association curve drops more steeply in the region centromeric of DQ-DR, and more linearly on the telomeric side. The pattern of LD of the region with disease correlates well with the recognized hot spots of recombination, particularly in the region just centromeric of the HLA-DQB1 (8–10).
Allele-specific LD patterns and marker informativity, i.e. the distribution of marker alleles on disease-associated chromosomes, are also critical parameters. This distribution could vary considerably between two populations, particularly two as historically distinct as Sardinia and the UK. A good example of this was provided by marker D6S2444. In the Sardinians, no association with the disease is observed with this marker which is located only 
85 kb centromeric of the main disease locus HLA-DQB1. In striking contrast this marker was the most strongly associated in the UK study (6). This conflicting observation was explained by the fact that the most common allele at this marker in the Sardinian population is simultaneously in very significant LD with the most common predisposing and protective DRB1-DQB1 haplotypes. In contrast, allelic variation at D6S2444 was informative in the UK families owing to its distinct allelic distribution on DRB1-DQB1 predisposing and protective haplotypes (data not shown). These results underline how LD patterns between markers and disease can be complicated. Two point haplotype analysis alleviates these problems while also increasing the information content of the map by raising the number of heterozygous parents (11–14).
In conclusion, we have mapped the main components of IDDM1 to the loci DRB1 and DQB1 in the isolated founder population of Sardinia. Our empirical observations are applicable to association studies of disease loci with weaker genetic effects except that the sample sizes required to detect significant associations will be proportionally higher (7).
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MATERIALS AND METHODS |
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TOPABSTRACTINTRODUCTIONRESULTSDISCUSSIONMATERIALS AND METHODSREFERENCES |
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Subjects and HLA typing
The data set consisted of 257 Sardinian T1DM families, all from a paediatric department of the southern part of the island. The sample set included 273 affected children, their parents and 241 unaffected siblings (average age of the patients at disease onset was 8.15 ± 4.5 years (females: 8.2 ± 3.9; males: 8.1 ± 4.2). Overall the whole data set was genotyped for 22 microsatellite markers. The primer sequences for D6S291, D6S439, D6S1629, D6S1560, D6S1568, D6S2445, D6S2444, D6S2447, D6S273, C1-2-A, D6S265, D6S258, D6S1683 and D6S306 were obtained from Foissac and Cambon-Thomsen (15). Sequences for TNFa, TNFc, TNFd and TNFe were obtained from Udalova et al. (16). Sequences for D3A, 82-1 and 82-2/9N-1 were established by Hsieh et al. (17). The primer sequence for D6S2223 was obtained from the Genome Database (GDB; http://www.gdb.org ). Genotyping was performed by separating fluorescently tagged polymerase chain reaction (PCR) products on a polyacrylamide gel using ABI 373 and ABI 377 DNA sequencers and the GeneScan 3.1 and Genotyper 2.0 software (Perkin-Elmer Applied Biosystems, Warrington, UK). 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 no. 1347.02 and of 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 was obtained from the Sanger Centre (http://www.sanger.ac.uk/HGP/Chr6/MHC.shtml ; A. Mungall, personal communication). Characteristics and physical map positions of the microsatellite markers are shown in Table 1. Markers were PCR amplified and genotyped a second time when showing failures during the first round of amplifications. On average 74.2% of all parents were heterozygous for the microsatellites investigated, with outlying markers D6S2445, D6S2444, TNFe, TNFc and D6S2223 showing heterozygosity < 60%. The average number of alleles per microsatellite was 10, but it was only 3 when we considered alleles with a parental frequency of at least 10%.
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The expressed genes considered in this study were typed as follows. The polymorphic second exons of the HLA-DRB1, -DQBl and -DPB1 genes were amplified and the amplified products were dot-blot analysed using primers and sequence-specific oligonucleotide (SSO) probes previously described (18–20). This includes characterization of 51, 25 and 18 SNPs for the DRB1, DQB1 and DPB1 loci, respectively. Alleles at the polymorphic second exon of DQA1 were inferred by the known patterns of LD with DQB1 and DRB1 in the Sardinians.
LMP2 (1 SNP), LMP7 (1 SNP), DMA (4 SNPs) and DMB (3 SNPs) polymorphisms were typed using primers and conditions previously described (21,22). DOB (1 SNP) was typed with amplification of the fourth exon and subsequent dot-blot analysis of the amplified products using 5'-GTGTCTAGTACAGATTCTG-3' and 5'-CACTCCTCACAGGCTCAT-3' as PCR primers and 5'-GTGGGAATCATCATCCAG-3' and 2 (1 SNP) gene was typed using primers and conditions previously described (23).
The tapasin (1 SNP) gene was typed with amplification of the fourth exon and subsequent dot-blot analysis of the amplified products using 5'-AAATGGGACCTTCTGGCTGC-3' and 5'-AAGCTCCAGGGTGACCTGTC-3' as PCR primers and 5'-GGCTGCCTAGAGTTCAACCC-3' and 5'-GGCTGCCTACAGTTCAACCC-3' as SSO probes (J. Copeman, personal communication).
Statistical analysis
The degree of association of the various loci with T1DM was established using the ETDT (24). 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 significance of the association with the disease at each individual locus. Therefore, the ETDT could be considered a particularly suitable method for the analysis of a region showing a multiallelic, two-sided association. The P values were corrected for number of loci considered and the –log of the corrected P values were plotted versus the physical position of the loci in the map. For this analysis only one affected child was randomly selected from all the families with more than one affected sibling.
Haplotyping was performed in all analyses considered in this manuscript following the method proposed by Clayton (25) and using computer programs written by Frank Dudbridge (CIMR, Cambridge University, UK; available by anonymous ftp at http://diesel.cimr.cam.ac.uk ). When haplotypes are not certain from parental genotype data, all possibilities are considered with weighting proportional to their population frequencies, which are estimated by the expectation-maximization algorithm.
The total normalized disequilibrium (total D') between DRB1-DQB1 and the various marker loci was calculated using a multiallelic extension of Lewontin’s standardized measure of disequilibrium (26,27) and ranges from 0 to 1, with 0 reflecting perfect independence between alleles at the two loci compared and 1 reflecting complete LD. The respective P values were calculated using the Markov chain method described by Guo and Thompson (28). The LD between the various loci and DR3 (DRB1*0301-DQA1*0501-DQB1*0201) and DR4 (DRB1*0405-DQA1-0301-DQB1*0302) was computed, using the methods described above, by selecting 100 positive haplotypes for each category, evaluated respectively in the same background of 100 non-DR3-DR4 haplotypes.
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ACKNOWLEDGEMENTS |
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We wish to thank Antonio Cao, Stefano De Virgiliis, Mario Silvetti, Mathias Herr, Elisabetta Deidda and Michael Whalen for help, advice and support, Cesare Zavattari for writing a program that allows assignment of the allele sizes into their appropriate allele bins, Frank Dudbridge for statistical advice, Margi Chessa, Paola Frongia and Rossella Ricciardi for help in collecting the Sardinian T1DM families, James Copeman for information about tapasin and Andrew Mungall (Sanger Center) for the establishment of the physical map. We would also like to thank the Italian Telethon, the Regione Autonoma Sardegna (L.R.11, 30–4-90) and the Wellcome Trust for financial support. F.C. and J.A.T. are recipients of a Wellcome Trust Biomedical Research Collaboration grant and J.A.T. was a Wellcome Trust Principal Research Fellow.
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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
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REFERENCES |
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