Human Molecular Genetics, 2000, Vol. 9, No. 20 2959-2965
© 2000 Oxford University Press
The inter-regional distribution of HLA class II haplotypes indicates the suitability of the Sardinian population for case–control association studies in complex diseases
1Dipartimento di Scienze Biomediche e Biotecnologie, Università di Cagliari, Ospedale Microcitemico, Via Jenner, Cagliari 09121, Italy, 2Dipartimento di Zoologia e Antropologia Biologica, Università di Sassari, Sassari 07100, Italy, 3Servizio di Diabetologia Pediatrica, Ospedale G. Brotzu, Cagliari 09121, Italy, 4Dipartimento di Neuroscienze, Università di Cagliari, Ospedale Binaghi, Cagliari 09121, Italy and 5Wellcome Trust Centre for Molecular Mechanisms in Disease, University of Cambridge, Addenbrookes 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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We have analysed HLA class II gene-based substructure of the Sardinian population in order to evaluate the possible influence of this parameter in the mapping of common disease loci using association methods. We first examined the distribution of the HLA-DRB1-DQA1-DQB1 haplotypes in 631 newborns from seven different regions of the island, and found that the most frequent haplotypes were uniformly distributed in all regions, but at frequencies unique to Sardinia. Other haplotypes, common in other white European populations, are consistently rare or absent across the whole island. Analysis of molecular variance (AMOVA) showed a very low degree of genetic differentiation between the coastal regions, which have suffered repeated invasions over many years, and the most internal and isolated part of the island. This suggests that there has been little genetic flow from the various populations that have invaded the island during the last 3000 years and that Sardinia is a relatively homogeneous population. The validity of these unrelated control HLA haplotype frequencies and our claim of homogeneity were established by demonstrating the near identity of the affected family-based control (AFBAC) HLA haplotype frequencies in 243 type 1 diabetes and 495 multiple sclerosis families from Sardinia and those of the unrelated controls. These results indicate that robust case–control studies can be carried out in Sardinia offering cost efficiency over certain family-based designs.
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INTRODUCTION |
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TOPABSTRACTINTRODUCTIONRESULTSDISCUSSIONMATERIALS AND METHODSREFERENCES |
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There is considerable debate over both the choice of population for the identification of the gene polymorphisms underlying common, complex diseases and the use of families versus unrelated cases and controls (1–3). It is advised that prior knowledge of the genetic structure of the study population is an essential prerequisite for such research. The most critical factor in the design of disease association studies is the choice of the controls. Artefacts might occur if the controls are not genetically or ethnically matched with the patients. Statistical approaches are being developed that attempt to take into account substructure in a case–control design by typing a range of presumably non-disease-associated polymorphisms across the genome (4–6). However, given these potentially serious problems, the use of ‘internal’, family-based controls has been recommended (7–9). Unfortunately complete families take considerably more effort to collect than unrelated cases and controls and, for the simplest family type, two parents and an affected sibling, there is a third more genotyping required than for the case–control design.
Some populations such as that from the Mediterranean island of Sardinia may be well suited to case–control designs because they are perceived as homogeneous. The Sardinian population is indeed an ancient genetic isolate with a unique distribution of alleles at multiple loci and with one of the highest frequencies in the world of many common genetic disorders such as type 1 diabetes mellitus (T1DM) and multiple sclerosis (MS). However, our knowledge of the inter-regional genetic structure of the Sardinian population is largely derived from studies carried out using serological markers, which have provided somewhat contradictory results (10–12). Contu et al. (10) found substantial homogeneity in the distribution of serologically defined human leukocyte antigen (HLA) class I markers in large sample sets from different regions of the island. In contrast, Capello et al. (12), analysing a combination of classical serological markers, recently reported some evidence of heterogeneity at the microgeographic level.
Not only the present day genetic structure, but also the origin of the Sardinians is still somewhat controversial. Direct evidence, based on 14C dating carried out on fossil bones, indicates that humans were already present in Sardinia in the pre-Neolithic age, 
750 generations ago [15 590 ± 140 before present (BP)] (13). Around 3500 years BP the local population was of a substantial size (300 000 inhabitants), which is indicated by the presence of 7000 fortified stone towers across the island. In the 29th century BP Sardinia was occupied by the Phoenicians, but the most internal and isolated region of the island (the Barbagia region) was not affected at all by this occupation nor by the subsequent Carthaginian (25th century BP) and Roman (2238 years ago) invasions. There is no evidence of large-scale admixture between populations from coastal and internal regions over the last 2000 years. Until a few decades ago, the population of the most internal areas lived in rather strict geographical and cultural isolation, distributed in small and widely scattered villages.
In the present study we carried out a detailed analysis of molecularly defined HLA class II DRB1-DQA1-DQB1 haplotypes obtained in seven different regions of the island. The aims of the present study were as follows: (i) to determine the level of genetic heterogeneity among the different regions of Sardinia, including the most internal and isolated area; (ii) to determine whether there is a restriction in the number of alleles and haplotypes detected in the Sardinian population, which would be indicative of a major population bottleneck in the history of this population; and (iii) to compare the DRB1-DQA1-DQB1 haplotype frequencies established in the Sardinian newborns with two sets of affected family-based control (AFBAC) frequencies, respectively, obtained from 243 T1DM and 495 MS families from the same island.
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RESULTS |
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TOPABSTRACTINTRODUCTIONRESULTSDISCUSSIONMATERIALS AND METHODSREFERENCES |
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Inter-regional distribution of the HLA class II haplotypes
The distribution of HLA-DRB1-DQA1-DQB1 haplotypes of 631 newborns from seven regions of Sardinia was established (see Materials and Methods). We found that the most frequent haplotypes (notably DR3, DRB1*0301-DQA1*0501-DQB1*0201 and DR2AZH, DRB1*1601-DQA1*0102-DQB1*0502) were uniformly distributed in all regions but at frequencies unique to the Sardinians (Table 1). Other haplotypes, common in other European populations (for instance, DR2, DRB1*1501-DQA1*0102-DQB1*0602), are consistently rare or absent across the whole island. We have performed a hierarchical analysis of the molecular variance components associated with the different possible levels of genetic differentiation (within populations, among populations and between groups of populations). Analysis of molecular variance (AMOVA) components associated with genetic differentiation among populations were not significant (Fst = 0.00; P
0.43). Most of the variation is found within populations (>99%). Less than 1% of the variation was accounted for by differentiation between the seven groups. Also, in comparing the population from the most internal and isolated Barbagia region with the populations from all the other Sardinian regions we have not found any significant differences between groups.
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The results of a multi-dimensional scaling analysis carried out on the DRB1-DQA1-DQB1 haplotype frequency correlation matrix illustrates the relatively low degree of genetic differentiation found between the different subregions. The divergence among the various Sardinian populations is indeed very low compared with the continental Italian population as a whole (Fig. 1).
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Controls versus pseudo-controls
We next compared the HLA haplotype frequencies in the newborns from the whole island with two sets of family-based controls: the AFBAC frequencies (14) obtained from 243 T1DM and 495 MS families from the southern part of the island (Table 1 and Materials and Methods). The haplotype frequencies of the newborns and of the pseudo-controls are virtually overlapping and in fact, comparing these sets of data, the AMOVA components associated with genetic differentiation among populations were not significant (Fst = 0.00; P
0.41).
Hardy–Weinberg testing
We tested fit to Hardy–Weinberg equilibrium (HWE) expectations for the most frequent individual DRB1-DQA1-DQB1 genotypes. There was no significant departure from HWE for any of the 23 genotypes having a frequency higher than 1% in the Sardinian newborns (average P values equal to 0.61 ± 0.22). The overall distribution of P values in the whole sample set was also calculated by the exact test using the Markov-chain approach (15) and was equal to 0.43. These results, together with the historical data, and with the absence of pseudo-association at unlinked loci (16) (F. Cucca and J. Todd, unpublished data) allow us to exclude recent admixture in this population.
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DISCUSSION |
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TOPABSTRACTINTRODUCTIONRESULTSDISCUSSIONMATERIALS AND METHODSREFERENCES |
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We analysed the distribution of HLA DRB1-DQA1-DQB1 haplotypes in seven different regions of Sardinia and found that the most frequent haplotypes are uniformly distributed in the island but at frequencies unique to this population.
The lack of significant large-scale genetic heterogeneity between the coastal regions, repeatedly invaded by outside populations, and the most internal and isolated part of the island, which was unaffected by these occupations, suggests that there has been little genetic flow from the invading populations over the last 3000 years. The high demographic ratio between the native people and the invaders may explain these findings. Sardinia was indeed densely populated (at least 300 000 inhabitants, 3500 years ago) before the arrival of any conquerors in the island.
These data contrast with what is observed in other genetic isolates, such as Finland. In the Finnish population, regional variations in the distribution of HLA haplotypes (17) and clear substructure were observed (18), part of it described by an east–west genetic gradient within the country owing to two main waves of settlers (18). Note that the lack of large-scale heterogeneity in the Sardinian population is not related to a restriction of the variability within it. For instance, in this sample set we detected all the HLA-DRB1, -DQA1 and -DQB1 allelic lineages present in modern humans and 41 DRB1-DQA1-DQB1 haplotypes. These data indicate that the Sardinian population as a whole has been large enough during its history to avoid major bottlenecks at the macro-population level. Nevertheless, analysis of chromosome X (19) and of mitochondrial DNA and chromosome Y (L. Morelli, unpublished data) reveals that many bottlenecks with founder effects and subsequent genetic drift occurred during the settlement of the dispersed villages present in the island and affected the frequencies of the rarer markers. This explains some degree of micro-heterogeneity observed in comparing genetic distances between small areas and isolated villages (12). Importantly, the various alleles observed in any part of the island appear to have originated from the same ancestral gene pool.
A possible reservation to this picture is that the HLA markers are not neutral and the allele frequencies observed in Sardinia might have been subjected to the skewing effect of selection. While further analysis of neutral loci will provide additional information about the inter-regional structure of the Sardinian population, a major ‘homogenizing’ effect of selection on the observed haplotype frequencies seems unlikely considering the large number of HLA haplotypes observed in this population. Most importantly, we have found that the analysis of a DRB1-DQA1-DQB1 three-locus haplotype is able to separate the various human populations in a manner consistent with their historical and geographical relationships (Fig. 2). Hence, founder effects and differentiation by isolation and drift appear to be the dominant forces explaining the HLA-based structure of the present human population.
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Taken together these results indicate that because of the lack of large-scale heterogeneity, the Sardinians indeed represent a suitable population for case–control association studies addressed to dissect the complex traits common in the island. This view is further supported by the fact that the HLA haplotype frequencies in the newborns from the whole island and those of two sets of AFBAC frequencies, obtained from a large number of T1DM and MS families from the southern part of the island, are almost identical.
These observations are also in agreement with the theoretical expectation that the AFBAC-derived chromosomes provide an unbiased estimate of allele frequencies in the general population under the assumption of random mating and in the absence of recent population stratification (14).
In conclusion, the distribution of HLA haplotypes in the newborns from different regions of Sardinia suggests the lack of large-scale heterogeneity between different regions of the island. We have also demonstrated that the haplotype frequencies detected in the Sardinian newborns as a whole are not significantly different from those obtained from two large independent sets of AFBAC controls. These results indicate that the Sardinians, by virtue of the absence of significant genetic stratification, and because of the very high frequency of many complex traits in the island are an ideal population for case–control association studies addressed to map common disease polygenes.
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MATERIALS AND METHODS |
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TOPABSTRACTINTRODUCTIONRESULTSDISCUSSIONMATERIALS AND METHODSREFERENCES |
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Sample selection
In this study the island of Sardinia was divided into the districts of Cagliari, Carbonia, Oristano, Lanusei, Tempio, Sassari and Sorgono on geographical and historical bases (Fig. 3). The samples were represented by the Guthrie cards used for newborn screening programmes in the delivery departments of these districts. Each district collected samples from newborns from the surrounding villages in areas ranging from a maximum of 60 km around Cagliari to a minimum of 20 km around Sorgono, which is located in the middle of the Barbagia area. The fathers’ and mothers’ surnames were used to confine the analysis to newborns of Sardinian origin.
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An informed consent agreement was obtained from the parents of all the children considered in this manuscript.
HLA class II oligotyping
DNA of the 631 newborns was extracted using the chelex method starting from dried blood spots (Guthrie cards) (20). DNA of 243 T1DM and 495 MS families was obtained according to standard protocols (21). Amplification of the polymorphic second exon of the HLA-DRB1, DQA1 and DQB1 genes and dot-blot analysis of amplified DNA with sequence-specific oligonucleotide (SSO) probes were carried out as previously reported (22–24). DR4-specific amplification and SSO probe hybridization for the various DRB1*04 alleles were performed in all the DR4-positive individuals (25). The DRB*11, DRB*12, DRB*13 and DRB*14 subtypes were genotyped as previously described (26,27).
Haplotype analysis and statistical tests
The identification of the various DRB1-DQA1-DQB1 haplotypes in 631 unrelated newborns was performed following the known patterns of linkage disequilibrium in Caucasians and Sardinians. In 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. This accounted for 1.7% of the total number of newborns analysed.
AFBAC haplotypes were selected as described for single alleles by Thomson (14) from of 243 T1DM and 495 MS simplex families. The AFBAC frequencies are based on the chromosomes that are never transmitted from the parents to affected children and therefore provide a source of unequivocally established haplotypes based on family data (14). Parental haplotypes were reconstructed using a software written by Frank Dudbridge available at ftp://ftp-gene.cimr.cam.ac.uk/pub/software/ . Offspring were used only for phase information.
In order to obtain a visual output of the DRB1-DQA1-DQB1 haplotype distribution, we have performed a multi-dimensional scaling analysis (28) carried out using the correlation matrix of the frequencies (Figs 1 and 2).
A hierarchical AMOVA (29–31) was used to determine the significance of the differences among the samples. The sum of the squared differences between haplotypes was calculated between all pairs of individuals in the same group and between groups. The variances were tested against the distributions of the theoretical values according to the method described by Excoffier et al. (30) and using the Arlequin program version 1.1 (32). Hardy–Weinberg equilibrium for genotypes more frequent than 1.0% was evaluated by testing with the 
2 test (1 df) observed versus expected occurrence estimated from the data. The overall deviation was also calculated according to the exact test based on the Markov-chain approach (15) using the Arlequin program version 1.1 (32).
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ACKNOWLEDGEMENTS |
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We wish to thank Antonio Cao, Mario Silvetti, Efisio Angius, Annabel Smith, Rosa Casalotti, Iain Eaves and Michael Whalen for help, advice and support, Margi Chessa, Paola Frongia and Rossella Ricciardi for help in collecting the DNA samples from Sardinian type 1 diabetes families, and the Regione Autonoma Sardegna (L.R.11, 30-4-90) 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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+ These authors contributed equally to this work
§ To whom correspondence should be addressed. Tel: +39 070 6095681; Fax: +39 070 6095558; Email: fcucca@mcweb.unica.it
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C. Motzo, D. Contu, H. J. Cordell, R. Lampis, M. Congia, M. G. Marrosu, J. A. Todd, M. Devoto, and F. Cucca Heterogeneity in the Magnitude of the Insulin Gene Effect on HLA Risk in Type 1 Diabetes Diabetes, December 1, 2004; 53(12): 3286 - 3291. [Abstract] [Full Text] [PDF]
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 M. G. Marrosu, M. Lai, E. Cocco, V. Loi, G. Spinicci, M. P. Pischedda, S. Massole, G. Marrosu, and P. Contu Genetic factors and the founder effect explain familial MS in Sardinia Neurology, January 22, 2002; 58(2): 283 - 288. [Abstract] [Full Text] [PDF]
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M. G. Marrosu, R. Murru, M. R. Murru, G. Costa, P. Zavattari, M. Whalen, E. Cocco, C. Mancosu, L. Schirru, E. Solla, et al. Dissection of the HLA association with multiple sclerosis in the founder isolated population of Sardinia Hum. Mol. Genet., December 1, 2001; 10(25): 2907 - 2916. [Abstract] [Full Text] [PDF]
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F. Cucca, R. Lampis, M. Congia, E. Angius, S. Nutland, S. C. Bain, A. H. Barnett, and J. A. Todd A correlation between the relative predisposition of MHC class II alleles to type 1 diabetes and the structure of their proteins Hum. Mol. Genet., September 1, 2001; 10(19): 2025 - 2037. [Abstract] [Full Text] [PDF]
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P. Zavattari, R. Lampis, C. Motzo, M. Loddo, A. Mulargia, M. Whalen, M. Maioli, E. Angius, J. A. Todd, and F. Cucca Conditional linkage disequilibrium analysis of a complex disease superlocus, IDDM1 in the HLA region, reveals the presence of independent modifying gene effects influencing the type 1 diabetes risk encoded by the major HLA-DQB1, -DRB1 disease loci Hum. Mol. Genet., April 1, 2001; 10(8): 881 - 889. [Abstract] [Full Text] [PDF]
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P. Zavattari, E. Deidda, M. Whalen, R. Lampis, A. Mulargia, M. Loddo, I. Eaves, G. Mastio, J. A. Todd, and F. Cucca Major factors influencing linkage disequilibrium by analysis of different chromosome regions in distinct populations: demography, chromosome recombination frequency and selection Hum. Mol. Genet., December 1, 2000; 9(20): 2947 - 2957. [Abstract] [Full Text] [PDF]
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