Human Molecular Genetics

Human Molecular Genetics Advance Access originally published online on June 2, 2004

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Human Molecular Genetics, 2004, Vol. 13, No. 15 1633-1639
DOI: 10.1093/hmg/ddh169
Human Molecular Genetics, Vol. 13, No. 15 © Oxford University Press 2004; all rights reserved

Comparative high-resolution analysis of linkage disequilibrium and tag single nucleotide polymorphisms between populations in the vitamin D receptor gene

Sergey Nejentsev^1,*, Lisa Godfrey¹, Hywel Snook¹, Helen Rance¹, Sarah Nutland¹, Neil M. Walker¹, Alex C. Lam¹, Cristian Guja³, Constantin Ionescu-Tirgoviste³, Dag E. Undlien⁴, Kjersti S. Rønningen⁵, Eva Tuomilehto-Wolf⁶, Jaakko Tuomilehto^6,7, Melanie J. Newport², David G. Clayton¹ and John A. Todd¹

¹Juvenile Diabetes Research Foundation/Wellcome Trust Diabetes and Inflammation Laboratory, ²Department of Medicine, Cambridge Institute for Medical Research, University of Cambridge, Cambridge, UK, ³Clinic of Diabetes, Institute of Diabetes, Nutrition and Metabolic Diseases, N. Paulescu, Bucharest, Romania, ⁴Institute of Medical Genetics, Ulleval University Hospital, University of Oslo, Oslo, Norway, ⁵Laboratory of Molecular Epidemiology, Division of Epidemiology, Norwegian Institute of Public Health, Oslo, Norway, ⁶Diabetes and Genetic Epidemiology Unit, National Public Health Institute, Helsinki, Finland and ⁷Department of Public Health, University of Helsinki, Helsinki, Finland

Received April 2, 2004; Accepted May 21, 2004

	ABSTRACT

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A genome-wide map of single nucleotide polymorphisms (SNP) and a pattern of linkage disequilibrium (LD) between their alleles are being established in three main ethnic groups. An important question is the applicability of such maps to different populations within a main ethnic group. Therefore, we have developed high-resolution SNP, haplotype and LD maps of vitamin D receptor gene region in large samples from five populations. Comparative analysis reveals that the LD patterns are identical in all four European populations tested with two small regions of 1.3 and 5.7 kb at which LD is disrupted completely resulting in three block-like regions over which there is significant and extensive LD. In an African population the pattern is similar, but two additional LD-breaking spots are also apparent. This LD pattern suggests combined action of recombination hotspots and founder effects, but cannot be explained by random recombination and genetic drift alone. Direct comparison indicates that the tag SNPs selected in one European population effectively predict the non-tag SNPs in the other Europeans, but not in the Gambians, for this region.

	INTRODUCTION

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Genetic association studies aim to identify human genome sequence variants that cause complex diseases. The human genome contains regions in which single nucleotide polymorphism (SNP) alleles are distributed non-randomly, i.e. they are in linkage disequilibrium (LD). These regions, known as the LD or haplotype blocks, have elevated local LD and limited haplotype diversity (1,2). Because of the underlying LD structure all common sequence variation in a given genome region may be captured by a limited number of the tag SNPs that are used to scan genomic regions for disease association and to reduce redundancy and costs of association studies (3,4). Recently, the HapMap project was launched to establish the LD patterns and to catalogue the tag SNPs in the entire human genome in three main ethnic groups (5).

The origin of the LD blocks in the human genome is not understood completely. Using direct studies of recombination in sperm Jeffreys and co-workers (6,7) have shown that boundaries of the LD blocks co-localize with recombination hotspots at least in some areas of the genome. Recent evidence suggests the presence of recombination hotspots across the human genome every 200 kb on average (8). Recombination hotspots should determine similar LD block structure in different human populations, unless recombination rates vary between populations. However, computer simulations suggest that the block-like structure of the human genome may have been generated by uniform recombination and random genetic drift (9–11). This model implies that the LD block boundaries may vary significantly between populations that will complicate genetic association studies. Therefore, it is not clear how informative the LD block structure and tag SNPs established by the HapMap project would be in various human populations, particularly those not represented in the HapMap panel (5). Studies of genomic regions in different populations featuring large sample size and dense SNP maps may provide answers to these questions. Moreover, comparative inter-population analysis of LD blocks will help to understand their evolutionary origin.

Here we have developed and studied a high-resolution SNP map of the vitamin D receptor (VDR) gene region on chromosome 12q12–q14. VDR is a protein that mediates the effects of vitamin D₃ in bone and mineral metabolism, regulation of growth and differentiation in many target tissues and acts as a modulator of the immune system (12). The VDR gene contains eight protein-coding exons 2–9, six untranslated exons 1a–1f, which are spliced alternatively, and several promoter regions (13,14). Four common VDR SNPs were studied intensively for association with various human traits and were reported to affect risk of osteoporosis, breast and prostate cancers and immune-mediated disorders (15). However, the association observed is inconsistent in different studies suggesting that the SNPs tested may merely be markers in LD with true causal variant(s), which remain unknown. Construction of comprehensive SNP, haplotype and LD maps of the VDR gene will facilitate association studies and fine mapping of the causal sequence variants for a range of diseases.

	RESULTS

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In total, we sequenced 94 kb in a 164 kb region on chromosome 12q12–q14 around the VDR gene and found 245 SNPs, of which 128 were not present in the dbSNP database build 119 (Supplementary Material, Table S1). We then genotyped 98 SNPs (i.e. one SNP per 1.7 kb on average) in 458 families from Great Britain. We calculated allele frequency in 916 parents and noticed 68 common SNPs with minor allele frequency (MAF)>10% (Fig. 1A). For these SNPs, we calculated pairwise |D'| (Fig. 1B), r² and P-values. A large sample of 916 subjects allowed us high confidence in estimating LD. Three separate regions could be distinguished in the VDR gene and were designated LD blocks A, B and C; SNPs in each of these blocks are in LD with SNPs located inside a block, but show very little, if any, LD with SNPs located in other blocks (Fig. 1). Within blocks B and C, regions of markedly higher LD can also be distinguished; we refer to them as B1, B2, C1, C2 and C3. However, their boundaries cannot be established unambiguously, because considerable LD exists between SNPs in these regions (Fig. 1).

View larger version (106K): [in this window] [in a new window]   Figure 1. High-resolution LD analysis of the VDR gene region. (A) Exons (black boxes) and SNPs (black lines). (B) Pairwise |D'|. The SNP numbers correspond to numbers in Supplementary Material, Table S1. SNPs with MAF >10% are shown. (C) LD blocks (black bars), unassigned FokI SNP (red dot) located in an LD-breaking spot between blocks and size of the LD-breaking spots (in kb) are shown.

 
Block A localizes 3' to the VDR exon 9 and spans at least 10.5 kb. The VDR exons 3–9 localize in the block B, which spans 40.8 kb. A 5.7 kb LD-breaking spot, located 3' of the VDR gene separates blocks A and B. Blocks B and C are separated by another 1.3 kb LD-breaking spot; it includes the VDR exon 2 and a commonly studied FokI SNP, which is the only SNP out of the 68 that has no detectable LD with any other SNP and cannot be assigned to any block. All non-coding VDR exons localize in at least 92.9 kb block C. Both blocks A and C have one of their boundaries outside the region studied.

In order to compare the LD block structure in different populations we tested all 68 VDR SNPs, which are common in the Britons, in four other population samples, three European and one African. We found 13 SNPs with MAF <10% in, at least one, usually Gambian, population sample. We restricted comparative analysis to 55 SNPs common in all five populations. This provides consistency and avoids bias in the conclusions due to the analysis of SNPs underrepresented in some populations. The location of the two LD-breaking spots, which separate blocks A, B and C, was identical in all five populations studied (Fig. 2). However, in the Gambians we additionally found separate blocks C1, C2 and C3, which correspond to the regions of higher LD within block C in the Europeans. Accordingly, LD-breaking spots in the Gambians co-localize with spots of decreased LD within block C in the Europeans, but clearly more LD extends over these spots in the Europeans (Fig. 2). In contrast, block structure in all four European populations is similar remarkably.

View larger version (101K): [in this window] [in a new window]   Figure 2. The LD structure in the VDR region in the five populations. Pairwise |D'|, the LD blocks (black bars), location of the LD-breaking spots seen in all five populations (thick arrows) and LD-breaking spots apparent in the Gambians only (thin arrows) are shown.

 
We then reconstructed haplotypes from the 55 SNPs in each population separately and found similar haplotype diversity in the Europeans, which is a fraction of haplotype diversity in the Gambians (full list of haplotypes is available in Supplementary Material, Table S2). In each of the five populations, we then selected a minimal set of tag SNPs, which have r²0.8 with all non-tag SNPs (tag SNPs for each population are highlighted in Supplementary Material, Table S2). As predicted by a higher haplotype diversity, we found that notably more tag SNPs are required to capture common variation in the Gambians compared with the Europeans: 42 versus 24–26, respectively. Then we tested how efficiently tag SNPs selected in one population sample predict non-tag SNPs in the four other populations. We found that tag SNPs selected in each of the European population samples usually effectively predict non-tag SNPs in all other Europeans, but not in the Gambians (Fig. 3). Conversely, the tag SNPs selected in the Gambian population predict the non-tag SNPs in the Europeans rather well. However, this is achieved at a cost of a larger number of the tag SNPs in the Gambians.

View larger version (21K): [in this window] [in a new window]   Figure 3. Inter-population comparison of the tag SNPs. Among the 55 VDR SNPs a set of tag SNPs was selected in one of the five populations (x-axis) so that tag SNPs capture all non-tag SNPs with r20.8. Then r2 between tag SNPs and non-tag SNPs (y-axis) was calculated in the four other populations (British, red, Finnish, blue; Norwegian, green; Romanian, yellow and Gambian, black).

 
Similarly, in the four European population samples we have analysed the tag SNPs selected from all 68 VDR SNPs that were common in the Britons (MAF>10%). All these 68 SNPs were also common in the other Europeans (MAF>8%). We found a similar pattern: the tag SNPs selected in each of the four European population samples effectively predict the non-tag SNPs in the other samples (Supplementary Material, Fig. S1). The tag SNPs selected among the 68 SNPs were more efficient than the tag SNPs selected among the 55 SNPs because the extra tag SNPs capture additional information about some of the non-tag SNPs.

We then investigated how informative was the analysis of the four classically typed SNPs, FokI, ApaI, TaqI and BsmI, in previous studies of the VDR gene region aimed to discover disease association. FokI is not in LD with any other common SNP and, therefore, was not informative for capturing information from those SNPs. The three other SNPs localized to block B and do not capture any information about SNPs in other blocks. We then tested ApaI, TaqI and BsmI as the tag SNPs for other block B SNPs and found that out of the 29 SNPs in block B, which are common in the Britons, these three SNPs predict only 11 with r²>0.8 and 15 SNPs with r² ranging between 0.05 and 0.43. Therefore, in various disease association studies that genotyped only FokI, ApaI, TaqI and BsmI, information about a large fraction of common SNPs in the VDR region was not captured.

	DISCUSSION

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Here we constructed a dense SNP, haplotype and LD maps of the VDR gene region in the five populations. Given multiple association studies and high interest in the VDR gene these maps provide a basis for future research of the VDR sequence polymorphism in human diseases.

Structural analysis of the LD blocks in the VDR gene region in various populations allows insight into the mechanism of their origin and into the history of the populations tested. Identical boundaries of blocks A and B in all five populations indicate general mechanism that acted in both the African and the European populations. Potentially it may be explained by either recombination hotspots or random crossovers and drift, which led to a block structure in the common ancestors that was fixed ever since. However, additional LD appeared in the history of all European populations. Such LD may have been created by a bottleneck or a founder effect after divergence of the ancestors of all modern Europeans from the ancestors of the Gambians (16,17). This LD pattern is not ubiquitous in the region but is restricted to the LD blocks, does not extend across major LD-breaking spots between them, and, therefore, argues in favour of localized recombination.

The mechanism that may have led to the observed LD block structure is suggested by the observation that recombination hotspots vary dramatically in recombination intensity and both highly and weakly active hotspots exist (6,8,18). Thus, highly active recombination hotspots probably manifest by breaking LD in all populations (e.g. LD-breaking spots between blocks A, B and C, Fig. 2). In contrast, weakly active recombination hotspots occasionally may manifest only in the ancient and diverse African populations, such as the Gambians, that have accumulated and retained enough recombinant chromosomes (18), whereas in the Europeans residual LD may still be present over a weakly active recombination hotspot (e.g. LD-breaking spots within block C, Fig. 2). Such a model is consistent with a well-known shorter average LD in the African populations in comparison with the European populations (19,20). Direct recombination analysis is required to test it in the VDR gene region. Nevertheless, highly structured pattern of the VDR LD blocks in different populations can be explained by combined action of recombination hotspots of varying intensity and demographic history events, but argues against the hypothesis that the LD blocks may have been generated by randomly distributed crossovers and genetic drift alone (9–11). Weakly active recombination hotspots localized inside the LD blocks, rather than random recombination, may explain a phenomenon of decreasing LD with distance within a block (21).

The remarkable similarity of the LD patterns in all the European populations tested is in line with the model assuming that no major founder effects occurred at the origin of specific farming communities in Europe (22). A bottleneck suggested at the founding of the Finnish population (23) left no significant trace of additional LD in the VDR gene. Consequently, studies of the Finnish population provide no major advantage of stronger LD between SNPs compared with the other Europeans (24).

Our direct inter-population comparison shows that the tag SNPs selected in one European population sample usually effectively predict the non-tag SNPs in the other Europeans, but not in the Gambians (Fig. 3), at least for this chromosome region. This observation implies that tag SNPs selected in the CEPH families of the European origin in the HapMap project (5) may be applied in other Europeans with only moderate loss of power. However, comparative analysis of multiple genomic regions in various populations is required to answer this question confidently. As expected, our data show that tag SNPs should be established separately for populations of the European and African origin.

This study of the VDR region illustrates the relative advantages of various populations for genetic associating mapping. Initial disease association is easier to detect in the Europeans than in the Africans, because fewer tag SNPs are required to characterize common variation (Fig. 3). However, should association be detected, subsequent fine mapping experiments will require testing of SNPs in an LD block region that often will be smaller in the Africans than in the Europeans (Fig. 2). Provided that the Africans have higher haplotype diversity, analysis of additional haplotypes may further help fine mapping of a causal variant. Therefore, genetic analysis in populations of different origin potentiates studies of complex diseases.

	MATERIALS AND METHODS

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Subjects
We searched for SNPs by sequencing eight unrelated Britons and also sequenced coding VDR exons 2–9 in 40 individuals from the same population to find additional rarer variants. We genotyped 285 new-borns from The Gambia, 458 British families (916 parents), 90 Norwegian families (180 parents), 63 Finnish families (126 parents) and 64 Romanian families (128 parents). The Gambian population sample comprises 285 unrelated new-borns collected in five centres across the country. All European subjects were Caucasian. All families have type 1 diabetes offspring. LD and haplotypes are similar for the chromosomes transmitted and non-transmitted to the affected offspring and, therefore, their combined analysis is shown. We obtained permission from the relevant ethical committees and informed consent from all participating subjects.

Sequencing
We designed primers using Primer3 (http://www.broad.mit. edu/cgi-bin/primer/primer3_www.cgi), amplified genomic DNA using protocol described elsewhere (25) and sequenced 500–700 bp PCR fragments with an ABI Big Dye Terminator v2 kit and an ABI 3700 capillary sequencer (ABI, Foster City, CA, USA). We sequenced 94 kb in a 164 kb region on chromosome 12q12–q14 around the VDR structural gene, between –34 045 bp upstream of the exon 1f and 27 338 bp downstream exon 9, allowing for the possibility of uncharacterized long-range regulatory elements. We found 245 VDR SNPs using the Staden sequence analysis package (26). Of these, 128 SNPs were not present in the dbSNP build 119. All 245 SNPs are listed in Supplementary Material, Table S1 with the position corresponding to Human Genome build 34.

Genotyping
We selected a total of 98 out of the 245 SNPs distributed evenly to avoid large gaps in the SNP map, one SNP per 1.7 kb on average. The density was higher around exons and lower in the 5' and 3' untranslated regions. Four commonly studied SNPs, FokI, ApaI, TaqI and BsmI, were also included. We genotyped 98 SNPs using Invader (Third Wave Technologies, Madison, WI, USA), TaqMan (Perkin Elmer Applied Biosystems, Foster City, CA, USA) or BeadArray (Illumina Inc, San Diego, CA, USA) assays. We tested genotype frequency for each SNP in each population with Arlequin version 2.000 (http://lgb.unige.ch/arlequin/) and found no deviation from the Hardy–Weinberg equilibrium (P>0.01).

Statistical analysis
We calculated pairwise r² (27), |D'| (28), P-values and generated graphical images with GOLD (29) and edited images using GIMP version 1.2.3 (http://www.gimp.org). We reconstructed haplotypes using expectation–maximization algorithm within SNPHAP version 0.2.1 (http://www-gene.cimr.cam.ac.uk/clayton/software/) and then selected a minimal set of tag SNPs that have r²0.8 with all non-tag SNPs in each of the five populations using ‘htstep’ and ‘htsearch’ programs within STATA version 8.1 (http://www.stata.com). Then we calculated r² between tag SNPs and non-tag SNPs, which they predict, in the other four population samples using ‘lpredict’ program written by David Clayton for STATA version 8.1.

	SUPPLEMENTARY MATERIAL

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Supplementary Material is available at HMG Online.

	ACKNOWLEDGEMENTS

 
The authors thank William Wang and Jason Cooper for interesting and productive discussions. This work was funded by the Wellcome Trust, the Juvenile Diabetes Research Foundation International, the Academy of Finland, the Sigrid Juselius Foundation and the Novo Nordisk Foundation. Genotypic data will be available on request to researchers on the basis of a Data Access Agreement. Please refer to our web site (https://www-gene.cimr.cam.ac.uk/todd/access-agreement.html) and contact Neil Walker (neil.walker{at}cimr.cam.ac.uk).

	FOOTNOTES

 
^* To whom correspondence should be addressed at: JDRF/WT Diabetes and Inflammation Laboratory, Cambridge Institute for Medical Research, University of Cambridge, Wellcome Trust/MRC building, Addenbrooke's Hospital, Cambridge CB2 2XY, UK. Tel: +44 1223762106; Fax: +44 1223762102; Email: sergey.nejentsev{at}cimr.cam.ac.uk

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