Copyright Ó 2006 by the Genetics Society of America
DOI: 10.1534/genetics.105.052225
Fine-Scale Map of Encyclopedia of DNA Elements Regions in the
Korean Population
Yeon-Kyeong Yoo,*,1 Xiayi Ke,†,1 Sungwoo Hong,‡ Hye-Yoon Jang,* Kyunghee Park,‡ Sook Kim,*
TaeJin Ahn,‡ Yeun-Du Lee,* Okryeol Song,‡ Na-Young Rho,* Moon Sue Lee,‡ Yeon-Su Lee,‡
Jaeheup Kim,‡ Young J. Kim,§ Jun-Mo Yang,** Kyuyoung Song,†† Kyuchan Kimm,‡‡
Bruce Weir,§§ Lon R. Cardon,† Jong-Eun Lee* and Jung-Joo Hwang‡,***,2
*DNA Link, Seoul, 120-110, Korea, ‡Bio Lab, ***Future Technology Group, Samsung Advanced Institute of Technology, Yongin-si,
Gyeonggi-do 449-901, Korea, †Wellcome Trust Center for Human Genetics, University of Oxford, Oxford OX3 7BN,
United Kingdom, §National Genome Information Center, Korea Research Institute of Bioscience and Biotechnology,
Daejeon-si, 305-333, Korea, **Department of Dermatology, Samsung Medical Center, Sungkyunkwan University
School of Medicine, Seoul, 135-230, Korea, ††Department of Biochemistry and Molecular Biology, University of
Ulsan College of Medicine, Seoul, 138-736, Korea, ‡‡National Genome Research Institute, Korean
National Institute of Health, Seoul 122-701, Korea and §§Department of Biostatistics,
University of Washington, Seattle, Washington 98195-7232
Manuscript received October 13, 2005
Accepted for publication May 13, 2006
ABSTRACT
The International HapMap Project aims to generate detailed human genome variation maps by densely
genotyping single-nucleotide polymorphisms (SNPs) in CEPH, Chinese, Japanese, and Yoruba samples.
This will undoubtedly become an important facility for genetic studies of diseases and complex traits in the
four populations. To address how the genetic information contained in such variation maps is transferable to
other populations, the Korean government, industries, and academics have launched the Korean HapMap
project to genotype high-density Enc yclopedia of DNA Elements (ENCODE) regions in 90 Korean
individuals. Here we show that the LD pattern, block structure, haplotype diversity, and recombination rate
are highly concordant between Korean and the two HapMap Asian samples, particularly Japanese. The
availability of information from both Chinese and Japanese samples helps to predict more accurately the
possible performance of HapMap markers in Korean disease-gene studies. Tagging SNPs selected from
the two HapMap Asian maps, especially the Japanese map, were shown to be very effective for Korean samples. These results demonstrate that the HapMap variation maps are robust in related populations and will
serve as an important resource for the studies of the Korean population in particular.
T
HE International HapMap Project is an international effort to document the common DNA sequence variations in the human genome to facilitate
genetic studies of common diseases and complex traits
(Gibbs et al. 2003) and apply them to other populations. The phase I mapping of this project, which genotypes samples from the Yoruba in Ibadan, Nigeria
(YRI); the Japanese in Tokyo ( JPT); the Han Chinese
in Beijing (CHB); and Utah residents with ancestry
from northern and western Europe (CEU) at a density
of 1 single-nucleotide polymorphism (SNP) every 5 kb,
has been completed (International Hapmap Consortium 2005). The HapMap has been widely regarded
as an important resource for disease association and
population studies (Dudbridge and Koeleman 2004;
1
These authors contributed equally to this work.
Corresponding author: Bio Lab, Future Technology Group, Samsung
Advanced Institute of Technology, Mt. 14, Nongseo-ri, Giheung-eup,
Yongin-si, Gyeonggi-do, 449-901, Korea.
E-mail: [email protected]
2
Genetics 174: 491–497 (September 2006)
Evans et al. 2004; Thomas et al. 2004; Morton 2005;
Pittman et al. 2005). Doubts and controversy, however, have also surrounded the project from the outset
(Terwilliger et al. 2002; Nielsen et al. 2004). One of
the most critical questions is how robust such dense
common variation maps produced for the four populations are to other populations.
Linkage disequilibrium (LD) patterns are known to
vary across the human genome and across populations
(Gabriel et al. 2002; Phillips et al. 2003; Ke et al.
2004a). Two important questions arise from these observations, i.e., how LD patterns are maintained between a HapMap population and a population that the
HapMap is applied to and how strong is a population to
which the HapMap is applied. These two questions are
related and can often be assessed by choosing tagging
SNPs ( Johnson et al. 2001; Gibbs et al. 2003; Stram
2004) in one population sample and applying them
to another population (Ke et al. 2004b; Ahmadi et al.
2005; Mueller et al. 2005). In a recent study involving four gene regions in several European populations
492
Y.-K. Yoo et al.
TABLE 1
Summary of genotyping data
Region
ENr112
ENr131
ENr113
ENm013
ENr321
ENr232
ENr213
Chromosome
location
No. of SNPs
with genotypes
Average marker
spacing
2p16.3
2q37.1
4q26
7q21.13
8q24.11
9q34.11
18q12.1
345
1513
266
640
653
445
557
1.45
0.33
1.88
1.72
0.77
1.12
0.90
(Mueller et al. 2005), tagging SNPs selected from the
HapMap CEU data were found to perform very efficiently in local European samples in two of the four regions because of high conservation of LD. In the other
two regions, however, restricted applicability of CEUderived tagging SNPs was observed due to significant
variation in LD among populations (Mueller et al.
2005).
For Asian populations, there are two HapMaps available, CHB and JPT. We are interested in the crosspopulation robustness of the HapMap in general and
the applicability of JPT and CHB maps to the Korean
population, in particular. We are also interested in potential advantages of having two related Asian HapMaps. To
this end, the Korean government, industries, and academic institutions launched the Korean HapMap project in 2003, which involved the fine mapping of 7 of the
10 HapMap Encyclopedia of DNA Elements (ENCODE)
regions.
MATERIALS AND METHODS
DNA samples and SNP selection: The samples used for the
study comprised 90 Korean samples randomly selected from
the cohort samples preserved in the Korean National Institute
of Health without family history of major diseases. SNP sites
were selected from dbSNP database build 119 (http://www.
ncbi.nlm.nih.gov/SNP) after applying repeat masking.
Genotyping: Multiplex SNP analyses were performed for
ENr213, ENr232, and ENr321 with the GenomeLab SNPstream
genotyping platform (Beckman Coulter) and its accompanying
SNPstream software suite as described by Denomme and Van
Oene (2005). For ENr131 as well as for ENr112, ENr113, and
ENm013, Sequenom’s MassARRAY system was used with standard conditions as described before (Bansal et al. 2002) except
the amount of genomic DNA (2.5 ng) was decreased with multiplexing assays. All genotypes were confirmed by an operator for
final genotype calls.
The average genotyping error rate was estimated at 0.1%
by routinely running duplicated sample wells in the plate. The
genotyping results in the study are available for download
from http://www.ngic.re.kr:8080/khapmap/.
LD analysis: All analyses in this study were based on the
HapMap ENCODE genotype downloaded at February 2005,
unless otherwise stated. Only markers that were polymorphic in
Regions chosen for analysis
and no. of polymorphic SNPs
shared by KR, JPT, and
CHB (spacing, 1 kb)
410 (1.20)
258 (1.94)
218 (2.25)
the Chinese, Japanese (http://www.hapmap.org/), and Korean
samples were used (‘‘shared data sets’’ thereafter). This selection of markers, therefore, allowed direct comparison of LD
structure between population samples and all other analyses
except tagging. For the sliding-window analysis, average pairwise r2 was calculated from 10- to 50-kb interspaced SNPs in
50-kb sliding windows (5-kb increment between windows).
Haplotype blocks were defined using Haploview (Barrett
et al. 2005) based on block definition of Gabriel et al. (2002).
Haplotypes and their frequencies in block regions were estimated using snphap (http://www-gene.cimr.cam.uk/clayton/
software/).
Fst analysis: Fst was calculated according to the Wright’s
F-statistic (Wright 1951). Because Fst estimates for SNPs in LD
are correlated (Weir et al. 2005), here we selected SNPs with
pairwise r2 all ,0.20 from the CHB 1 JPT combined set.
Recombination analysis: Phase 2.1 (Li and Stephens 2003;
Crawford et al. 2004) was used to analyze the shared data sets
as described above.
Tagging analysis: Tagging SNPs were selected from the
full-density CHB and JPT maps (as well as the combined
CHB 1 JPT map) using Tagger (de Bakker et al. 2005; http://
www.broad.mit.edu/mpg/tagger/) in aggressive tagging mode
(r2 or haplotype r2 $ 0.80, minor allele frequency cutoff ¼ 5%,
and other settings at default value). Tagging efficiency was defined as n/nh, where nh is the number of tagging SNPs selected
to cover the region and n is the total number of markers genotyped (Ke et al. 2004b). The performance of those tagging SNPs
was evaluated again using Tagger with the same settings by applying tags to the 90 Korean (KR) samples. A marker is ‘‘captured’’ if the pairwise r2 or haplotype r2 with the tags is $0.80.
RESULTS
SNP genotyping: The Korean HapMap project undertook fine mapping of 7 of the 10 HapMap ENCODE
regions using DNA samples from 90 individuals from
South Korea (Table 1, supplemental Figure 1 at http://
www.genetics.org/supplemental/). Marker density varied from different levels of an average 1 SNP/1.88 kb in
ENr113 to having the lowest 1 SNP/330 bp in ENr131
(Table 1).
ENr131, ENr213, and ENr321 were the three most
densely genotyped regions and were used to investigate
the applicability of Japanese and Chinese HapMap to
the Korean population (Table 1). As described in detail
in the following sections, ENr131 had a low level of LD
Robustness of Asian HapMap for Korean Population
493
Figure 1.—Comparison of Korean, Japanese, and Chinese in patterns of LD, haplotype block structure, and recombination rate
in ENCODE regions (ENr131, ENr213, and ENr321). (a) Patterns of LD in sliding windows: dark blue lines denote the HapMap
Chinese sample (CHB); red and pink lines denote the HapMap Japanese sample ( JPT); and the yellow, green, and brown lines
denote three random Korean (KR) samples of 45 individuals. (b–d) Haplotype block structure. (e) Recombination rate: blue lines
denote CHB, red lines denote the JPT, the green lines denote average of three random KR samples of 45 individuals.
and ENr213 had a high level of LD, whereas ENr321 had
an intermediate level of LD. Findings from these three
dense maps, we hope, would shed light on studies of
genomewide scale. To match the sample sizes of JPT
and CHB samples in the International HapMap Project,
45 KR individuals were randomly selected from the 90
Koreans and used for comparisons of the three populations, unless stated otherwise.
Population difference and LD conservation: The
three ENCODE regions had a different level of average
LD at a broad scale, with ENr131 being the lowest
and ENr213 the highest (Figure 1a). LD patterns in all
494
Y.-K. Yoo et al.
TABLE 2
Comparison of block structure and SNP tagging efficiency
Marker
spacing
(kb):
Region
ENr131
1.2
ENr213
2.25
ENr321
1.94
Block structure, sequence coverage of blocks (%)
KRa
69
65
54
JPT
69
67
62
CHB
69
72
55
KRb
76
76
57
CHB 1 JPT
75
74
69
Block structure, average block size (kb)
11.3
18.8
KRa
JPT
9.5
18.4
CHB
10.3
19.7
KRb
11.3
20.9
CHB 1 JPT
10.3
22.6
KRa
JPT
CHB
KRb
CHB 1 JPT
a
b
9.3
14.8
9.0
10.2
11.1
SNP tagging efficiency
3.8
4.2
3.9
5.3
3.7
5.5
3.9
4.2
4.2
5.7
4.4
4.4
4.7
4.2
4.7
Average of three random samples of 45 individuals.
All 90 individuals.
three regions were similar among Korean, Japanese,
and Chinese in general although some differences were
apparent in ENr213 and ENr321. Average Fst-values
between Korean and Japanese and between Korean and
Chinese were 0.0060, 0.0044, and 0.0062 and 0.0064,
0.0075, and 0.0095 for ENr131, ENr213, and ENr321,
respectively. This observation indicates that the Korean
population is very close to Japanese and Chinese populations in general and, perhaps, closer to Japanese
than to Chinese. This observation was reflected in the
sliding-window analysis showing more departures of
Korean samples from Chinese samples in ENr213 and
ENr321 than ENr131 (Figure 1a). The close relatedness
of the haplotypes of the Korean, Japanese, and Chinese
populations was further confirmed by phylogenetic analysis (supplemental Figure 2 at http://www.genetics.org/
supplemental/) (Akey et al. 2002).
The haplotype block structures of the three regions
were generally concordant among Korean, Japanese,
and Chinese. Block structure had the highest concordance in ENr131 (Figure 1, b–d) although the average
LD in this region was lower than in the other two (Figure
1a). This was consistent with the mean LD trends, where
the lines representing three different samples within
the Korean population as well as three Asian populations overlapped tightly. Differences were observed,
however, in ENr213 and ENr321, which were consistent
with the departures observed in the broad scale of LD
patterns (Figure 1, Table 2).
Comparison of the major haplotypes in haplotype
blocks of the ENr131, ENr213, and ENr321 regions
between KR and JPT/CHB showed that close to 90% of
their haplotypes in block regions defined in JPT and
CHB were major haplotypes, and this number was still
.85% when haplotypes were reconstructed for the
same regions using the Korean samples (Table 3). Furthermore, the majority of the major haplotypes as defined in JPTand CHB remained major haplotypes in KR,
demonstrating a high degree of haplotype conservation
across the three populations (Table 3). In ENr321,
where a higher concordance of block structure was observed between KR and CHB than between KR and
JPT (Figure 1, b–d, Table 2), the major haplotypes were
found, however, to be more conserved between KR and
JPT (0.749) than between KR and CHB (0.662). This indicates that the Koreans and Japanese are perhaps more
closely related in this particular region.
Different patterns and behaviors were still observed
between populations when the combined CHB 1 JPT
data sets were compared with the complete whole Korean
sample of sets containing 90 individuals (Table 2). In
ENr321, the sequence coverage of haplotype blocks was
69% for CHB 1 JPT combined samples and 57% for
Korean samples. This difference was primarily due to
the difference between the Japanese and Korean samples (Table 2). It was also revealed that major haplotype
conservation between CHB 1 JPT and KR was in general
between what was obtained by comparing CHB and JPT
separately with KR, except for ENr213 where higher conservation was achieved in CHB 1 JPT samples (Table 3).
Recombination rate: The pattern of estimated recombination rate in ENr131 was very simple compared
to the other two regions and remarkably similar between
KR, JPT, and CHB (Figure 1e). In contrast, recombination rates of ENr213 and ENr321 regions were more
varied across regions as well as among populations
(Figure 1e). This was consistent with the broad view of
LD and the primary reason for the highest similarity of
block structure in ENr131 and less concordance among
the three populations in the ENr213 and ENr321 regions although they had a higher average LD (Figure 1).
In ENr213, the amplitude of recombination rate variations was smaller than in ENr131 and ENr321, in agreement with the fact that this region had the highest LD
(Figure 1).
Transferability of tagging SNPs: We selected tagging SNPs from CHB, JPT, or CHB 1 JPT samples and
applied them to the Korean population. At the same
marker densities, tagging efficiency was very similar
among the three populations in ENr131 and ENr321
with some difference in ENr213 (Table 2). When tagging SNPs were selected from full-density sets of CHB,
JPT, and CHB 1 JPT, higher tagging efficiencies were
obtained as expected (4.5, 5.8, and 5.2 in ENr131; 7.0,
7.0, and 7.7 in ENr213; 5.8, 5.9, and 6.1 in ENr321). In
all three regions, at least 80% of SNPs in the Korean
Robustness of Asian HapMap for Korean Population
495
TABLE 3
Haplotype conservation across populations
Population 1
Total frequency
of major haplotypes
in population 1
Total frequency of
major haplotypes
in population 2
Total frequency
of major haplotypes
in population 1
conserved as major
haplotypes in
population 2
Frequency
correlation
coefficient of
major haplotypes
between populations
(P-value)
Population 2
Region
CHB
CHB
CHB
CHB
KR
KR
KR
KR
ENr131
ENr213
ENr321
Total
0.887
0.926
0.881
0.897
6
6
6
6
0.07
0.05
0.101
0.006
0.859
0.841
0.858
0.854
6
6
6
6
0.09
0.09
0.09
0.008
0.714
0.735
0.662
0.707
6
6
6
6
0.132
0.156
0.115
0.018
0.900
0.800
0.838
0.866
(,0.0001)
(0.0003)
(0.0003)
(,0.0001)
JPT
JPT
JPT
JPT
KR
KR
KR
KR
ENr131
ENr213
ENr321
Total
0.874
0.892
0.894
0.884
6
6
6
6
0.06
0.07
0.07
0.004
0.882
0.791
0.845
0.852
6
6
6
6
0.06
0.138
0.09
0.009
0.759
0.710
0.749
0.745
6
6
6
6
0.138
0.161
0.096
0.017
0.927
0.769
0.898
0.911
(,0.0001)
(0.0154)
(,0.0001)
(,0.0001)
KR
KR
KR
KR
ENr131
ENr213
ENr321
Total
0.869
0.879
0.841
0.864
6
6
6
6
0.066
0.076
0.090
0.075
0.847
0.813
0.793
0.823
6
6
6
6
0.093
0.082
0.127
0.102
0.727
0.742
0.704
0.724
6
6
6
6
0.096
0.121
0.126
0.110
0.750
0.893
0.785
0.770
(,0.0001)
(,0.0001)
(,0.0001)
(,0.0001)
CHB
CHB
CHB
CHB
1
1
1
1
JPT
JPT
JPT
JPT
Haplotypes and their frequencies were estimated in high-LD regions (i.e., Gabriel blocks of five or more markers defined by
Haploview) for ENr131, ENr213, and ENr321 from a starting population sample, i.e., population 1. They were then compared with
haplotypes estimated in the same regions (exact boundaries) of a test population sample, i.e., population 2. Major haplotypes were
those with frequency $0.10. Phasing was done separately for the starting and test populations using snphap.
samples can be captured by tagging SNPs selected from
the CHB, JPT, and CHB 1 JPT HapMap samples (Figure
2). In ENr131 and ENr213, tagging SNPs selected from
JPT were better at capturing SNPs in the Korean samples
and more effective than those selected from CHB. Some
additional benefit was also observed when tagging SNPs
were selected from CHB 1 JPT than those selected from
JPT alone (e.g., ENr213 and ENr321). Since higher
tagging efficiency was observed for JPT and CHB 1 JPT
than for CHB, the advantage of using JPT (particularly
in ENr131) or CHB 1 JPT as reference for the Korean
Figure 2.—Evaluation of tagging performance. Tagging
SNPs were selected from HapMap Chinese (CHB), Japanese
( JPT), and the combined samples using Tagger at full density
and applied to the 90 Korean individuals. An aggressive tagging mode with the same settings in Tagger was used for both
selection of tagging SNPs and a test of their performance.
Open bars denote CHB, shaded bars JPT, and solid bars the
combined set of Chinese and Japanese samples (CHB 1 JPT).
population was more apparent. The difference of JPT
and CHB 1 JPTseems to correspond well to the local LD
feature of a region. In ENr131, little variation of LD was
observed between populations as well as within the
Korean population (Figure 1a). As a result, JPT alone
seems to be good enough to capture the variations in
Korean samples. Whereas in ENr321 and especially in
ENr213 more variations were observed between as well
as within populations, and the combined CHB 1 JPT sets
seems to perform better than JPT alone.
We have also examined some extreme cases of population difference in SNP allele frequency, which could
affect tagging performance. If a common SNP in a reference population is monomorphic in the test population and this SNP happens to be selected as a tagging
SNP, the power of the whole tagging SNP sets may drop
in the test population. On the other hand, if a SNP is
monomorphic in a reference population but polymorphic in the test population, the tagging SNP sets
selected from the reference population could be unprepared for this particular SNP and may fail to tag it. As
shown in supplemental Table 1 (http://www.genetics.
org/supplemental/), overall these extreme cases were
rare in regions and samples analyzed in this study
whether the comparison was made against the early
version of HapMap ENCODE data (February 2005) or
the latest (October 2005). This demonstrated again
that Korean, Japanese, and Chinese are indeed closely
related populations and tagging SNPs selected from
the CHB/JPT HapMap are highly transferable to the
496
Y.-K. Yoo et al.
Korean population. It is interesting, however, to observe
that ENr213 did show more variations than ENr131 and
ENr321 among the three populations (i.e., cases where
SNPs were monomorphic in KR but polymorphic in
CHB/JPT or vice versa. This is consistent with observations made about LD and recombination.
DISCUSSION
LD patterns can be refined at a local level using
haplotype block analysis resulting in delineation of haplotype blocks for high-LD and nonblocks for low-LD
regions (Daly et al. 2001; Gabriel et al. 2002; Phillips
et al. 2003). Haplotype blocks are generally associated
with limited haplotype diversity, such that a few major
haplotypes explain the majority of the diversity in a
block (Daly et al. 2001; Gabriel et al. 2002; Phillips
et al. 2003). The Korean population is known to be
very close to the Chinese and Japanese. The haplotype
block structures of the three ENCODE regions, ENr131,
ENr213, and ENr321, were generally concordant among
Korean, Japanese, and Chinese, although some differences were observed in ENr213 and ENr321. Block
structure in ENr131 had the highest concordance.
ENr213 had the highest sequence coverage by blocks,
especially when density difference was taken into account. In ENr321, block structure was more similar
between Korean and Chinese than between Korean and
Japanese. Because haplotype blocks were defined using
LD thresholds, certain variations may change the block
boundaries in particular regions (Ke et al. 2004b),
especially when the regions are small.
The human genome is known to be delimited by recombination into hotspot and coldspot regions (Gabriel et al.
2002; Phillips et al. 2003; Crawford et al. 2004; McVean
et al. 2004). Coldspot regions usually correspond to haplotype blocks, whereas hotspots typically occur where haplotype blocks are expected to break down (Gabriel et al.
2002; Phillips et al. 2003). The recombination rate was
very simple in ENr131 showing low LD, while recombination rates of ENr213 and ENr321 were more varied. In
general, the patterns of LD and recombination are highly
conserved between the Koreans and Chinese and Japanese. Having two related East Asian population HapMaps
enables us to examine a region in close detail via comparison. If the Japanese and Chinese maps are highly concordant, as in ENr131, a Korean sample would likely
share similar patterns of LD and recombination (Figure
1). On the other hand, if differences are observed between
Japanese and Chinese maps, as in ENr213 and ENr321, a
Korean sample might be expected to reveal greater variability within and between populations. This understanding could assist in interpreting disease associations in a
particular region.
A more practical use of human genome variation
maps is perhaps to design tagging SNPs for related populations in regional or genomewide association studies
( Johnson et al. 2001; Gibbs et al. 2003; Stram 2004).
The present results also show that tagging SNPs selected
from the Chinese and particularly from the Japanese
samples are highly transferable to our Korean samples.
Even in regions where differences were observed among
the three groups, tagging SNPs from the Japanese performed at least as effectively as those from Korean samples. These observations suggest that the Japanese and
Chinese HapMaps will be robust for the Korean population and serve as an important resource for the association and population studies of the Koreans and
possibly other Asian populations.
The authors gratefully acknowledge the Korean National Institute
of Health for providing DNA samples and the Korean HapMap consortium. This work was supported by grants from the Korea Ministry of
Science and Technology, the National Research and Development
Program, the Korean Haplotype Information Development Program,
the Samsung Corporate Research Fund, and the DNA Link Research
Fund. X.K. was supported by the Wellcome Trust. L.R.C. was supported
by the Wellcome Trust and National Institutes of Health (NIH). B.S.W.
was supported by the NIH.
LITERATURE CITED
Ahmadi, K. R., M. E. Weale, Z. Y. Xue, N. Soranzo, D. P. Yarnall
et al., 2005 A single-nucleotide polymorphism tagging set for
human drug metabolism and transport. Nat. Genet. 37(1): 84–
89.
Akey, J. M., G. Zhang, K. Zhang, L. Jin and M. D. Shriver, 2002 Interrogating a high-density SNP map for signatures of natural selection. Genome Res. 12(12): 1805–1814.
Bansal, A., D. van den Boom, S. Kammerer, C. Honisch, G. Adam
et al., 2002 Association testing by DNA pooling: an effective
initial screen. Proc. Natl. Acad. Sci. USA 99: 16871–16874.
Barrett, J. C., B. Fry, J. Maller and M. J. Daly, 2005 Haploview: analysis and visualization of LD and haplotype maps. Bioinformatics
21(2): 263–265.
Crawford, D. C., T. Bhangale, N. Li, G. Hellenthal, M. J. Rieder
et al., 2004 Evidence for substantial fine-scale variation in recombination rates across the human genome. Nat. Genet. 36(7):
700–706.
Daly, M. J., J. D. Rioux, S. F. Schaffner, T. J. Hudson and E. S.
Lander, 2001 High-resolution haplotype structure in the human genome. Nat. Genet. 29(2): 229–232.
de Bakker, P. I., R. Yelensky, I. Pe’er, S. B. Gabriel, M. J. Daly et al.,
2005 Efficiency and power in genetic association studies. Nat.
Genet. 37(11): 1217–1223.
Denomme, G. A., and M. Van Oene, 2005 High-throughput multiplex single-nucleotide polymorphism analysis for red cell and
platelet antigen genotypes. Transfusion 45(5): 660–666.
Dudbridge, F., and B. P. Koeleman, 2004 Efficient computation
of significance levels for multiple associations in large studies
of correlated data, including genomewide association studies.
Am. J. Hum. Genet. 75(3): 424–435.
Evans, D. M., L. R. Cardon and A. P. Morris, 2004 Genotype prediction using a dense map of SNPs. Genet. Epidemiol. 27(4):
375–384.
Gabriel, S. B., S. F. Schaffner, H. Nguyen, J. M. Moore, J. Roy et al.,
2002 The structure of haplotype blocks in the human genome.
Science 296: 2225–2229.
Gibbs, R. A., J. W. Belmont, P. Hardenbol, T. D. Willis, F. Yu
et al., 2003 The International HapMap Project. Nature 426:
789–796.
International Hapmap Consortium, 2005 A haplotype map of
the human genome. Nature 437: 1299–1320.
Johnson, G. C., L. Esposito, B. J. Barratt, A. N. Smith, J. Heward
et al., 2001 Haplotype tagging for the identification of common
disease genes. Nat. Genet. 29(2): 233–237.
Robustness of Asian HapMap for Korean Population
Ke, X., S. Hunt, W. Tapper, R. Lawrence, G. Stavrides et al.,
2004a The impact of SNP density on fine-scale patterns of linkage disequilibrium. Hum. Mol. Genet. 13(6): 577–588.
Ke, X., C. Durrant, A. P. Morris, S. Hunt, D. R. Bentley et al.,
2004b Efficiency and consistency of haplotype tagging of dense
SNP maps in multiple samples. Hum. Mol. Genet. 13(21): 2557–
2565.
Li, N., and M. Stephens, 2003 A new multilocus model for linkage
disequilibrium, with application to exploring variations in recombination rate. Genetics 165: 2213–2233.
McVean, G. A., S. R. Myers, S. Hunt, P. Deloukas, D. R. Bentley
et al., 2004 The fine-scale structure of recombination rate variation in the human genome. Science 304(5670): 581–584.
Morton, N. E., 2005 Linkage disequilibrium maps and association
mapping. J. Clin. Invest. 115(6): 1425–1430.
Mueller, J. C., E. Lohmussaar, R. Magi, M. Remm, T. Bettecken
et al., 2005 Linkage disequilibrium patterns and tagSNP transferability among European populations. Am. J. Hum. Genet.
76(3): 387–398.
Nielsen, R., M. J. Hubisz and A. G. Clark, 2004 Reconstituting the
frequency spectrum of ascertained single-nucleotide polymorphism data. Genetics 168: 2373–2382.
Phillips, M. S., R. Lawrence, R. Sachidanandam, A. P. Morris, D. J.
Balding et al., 2003 Chromosome-wide distribution of haplo-
497
type blocks and the role of recombination hot spots. Nat. Genet.
33(3): 382–387.
Pittman, A. M., A. J. Myers, P. Abou-Sleiman, H. C. Fung, M. Kaleem
et al., 2005 Linkage disequilibrium fine-mapping and haplotype
association analysis of the tau gene in progressive supranuclear
palsy and corticobasal degeneration. J. Med. Genet. 42(11):
837–846.
Stram, D. O., 2004 Tag SNP selection for association studies. Genet.
Epidemiol. 27(4): 365–374.
Terwilliger, J. D., F. Haghighi and T. S. Hiekkalinna, 2002 Goring
HHH A biased assessment of the use of SNPs in human complex
traits. Curr. Opin. Genet. Dev. 12(6): 726–734.
Thomas, D., R. Xie and M. Gebregziabher, 2004 Two-stage sampling designs for gene association studies. Genet. Epidemiol.
27(4): 401–414.
Weir, B. S., L. R. Cardon, A. D. Anderson, D. M. Nielsen and W. G.
Hill, 2005 Measures of human population structure show
heterogeneity among genome regions. Genome Res. 15: 1468–
1476.
Wright, S., 1951 The genetical structure of populations. Ann.
Eugen. 15: 323–354.
Communicating editor: R. W. Doerge