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Genomics Inform > Volume 12(4); 2014 > Article
Lee, Han, Tark, and Kim: Short Reads Phasing to Construct Haplotypes in Genomic Regions That Are Associated with Body Mass Index in Korean Individuals


Genome-wide association (GWA) studies have found many important genetic variants that affect various traits. Since these studies are useful to investigate untyped but causal variants using linkage disequilibrium (LD), it would be useful to explore the haplotypes of single-nucleotide polymorphisms (SNPs) within the same LD block of significant associations based on high-density variants from population references. Here, we tried to make a haplotype catalog affecting body mass index (BMI) through an integrative analysis of previously published whole-genome next-generation sequencing (NGS) data of 7 representative Korean individuals and previously known Korean GWA signals. We selected 435 SNPs that were significantly associated with BMI from the GWA analysis and searched 53 LD ranges nearby those SNPs. With the NGS data, the haplotypes were phased within the LDs. A total of 44 possible haplotype blocks for Korean BMI were cataloged. Although the current result constitutes little data, this study provides new insights that may help to identify important haplotypes for traits and low variants nearby significant SNPs. Furthermore, we can build a more comprehensive catalog as a larger dataset becomes available.


Genome-wide association studies (GWASs) have been a useful tool to identify genetic variants that affect various traits [1]. Numerous novel important genetic variants associated with disease susceptibility have been identified through GWASs [2]. However, genome-wide association (GWA) results for complex diseases can generally explain only a small part of the genetic variants for complex diseases [3]. Generally, a GWA study identifying an association between a trait and a genetic variation may be limited in understanding complex diseases involving multiple functional loci [4].
Recently, many approaches to detect an association between a trait and one or multiple genetic variants have been proposed to study numerous data from GWASs [5]. Inference about linkage disequilibrium (LD), known as non-independent association of alleles at different loci, provides information for the association of genetic variants affecting complex traits [6]. Using LD analysis, it is possible to characterize multiple genomic variants associated with phenotypes in terms of haplotypes. A haplotype defines a combination of phased alleles in a chromosomal region [4]. Thus, haplotype analysis is useful to understand multiloci genetic associations and to identify susceptibility loci for diseases. In spite of the advantages of haplotype analysis, performing an analysis on a genome-wide scale is not simple, due to the uncertainty and complexity of haplotypes [7].
Whole-genome sequencing (WGS) of a reference population may provide insights into the potential causal variants hidden within the LD block of interest. While the 1000 Genomes Project aims to provide such information in worldwide populations or ethnic groups [8], it would be more informative to focus on a reference population on a national scale than on a global scale. Recently, several Korean reference WGS datasets have been published based on next-generation sequencing (NGS) platforms [9]. Here, we tried to construct haplotype blocks affecting body mass index (BMI) in Korean individuals through integrative analysis of NGS and GWA data. We analyzed haplotypes using NGS data only in ranges that were nearby significant single-nucleotide polymorphisms (SNPs) that were identified by GWA results. The analysis may have several advantages in haplotype analysis. The complexity and the time can be reduced by analyzing smaller regions that are known to harbor significant GWA signals. We cataloged all possible 44 haplotype blocks within the LD blocks of significant SNPs for BMI.


Overall pipeline of the analysis

The NGS raw data were mapped to reference sequences using an alignment tool. The significant SNPs for BMI were selected from the GWA result data. The LD blocks that encompassed the significant SNPs were searched with Haploview [10], and those NGS reads that were mapped to the LD ranges were selected with Samtools. For each LD block, the heterozygous variants from the short reads were phased with Samtools. Finally, the phased haplotypes harboring the significant SNPs were cataloged (Fig. 1).

Genotyping, imputation, and GWA analysis

The Korean samples and genotype data used in this study have been described by Cho et al. [11]. Briefly, through the Korea Association Resource (KARE) project, a total of 10,038 participants were recruited from Ansan and Anseong population-based cohorts, aged 40 to 69. The genotypes that had been measured on Affymetrix Genome-Wide Human SNP array 5.0 (Affymetrix, Santa Clara, CA, USA) were filtered for quality control, resulting in 8,842 samples and 352,228 markers. The genotype data were expanded to a total of 1,827,004 SNPs through imputation with IMPUTE [12] using the Japanese in Tokyo, Japan (JPT)/Han Chinese in Beijing, China (CHB) component of HapMap as the reference.
BMI traits were tested for association by linear regression analysis with an additive model after adjustments for recruitment region, age, and sex as covariates using PLINK. The threshold of genome-wide significance was set at p < 5 × 10-4. The significant SNPs were clustered into LD blocks (r2 > 0.9) using HaploView [13].

NGS data analysis

We downloaded previously published whole-genome NGS data from the TIARA database, sequenced at Seoul National University College of Medicine (TIARA; [14]. The short reads from seven samples (AK3, AK4, AK5, AK6, AK7, AK14, and AK20) were mapped to the human reference genome (hg19) using the alignment algorithm in Genomic Short-read Nucleotide Alignment Program (GSNAP) [15]. From the resulting BAM file, we selected the mapped reads within the LD blocks that encompassed the significant SNPs from the BMI GWA result using the Samtools view function [16], creating a separate BAM file for each LD block.

Heterozygous SNP calling and phasing

The heterozygous SNPs were phased using the phase function in Samtools, version 0.1.19-44428cd [17]. The Samtools phase algorithm calls heterozygous SNPs automatically and phases those that segregate on the same DNA fragment as inferred from paired-end read information in the BAM file. We used the default options for the Samtools phase function. We confirmed the heterozygous SNPs through independent calling using the Samtools mpileup function [17]. Whenever an LD block split into multiple phased haplotype blocks, we kept those that encompassed the significant SNPs from the GWAS.


GWA analysis

The genotype data of 8,842 samples in a total of 10,038 participants in the KARE were analyzed to find significant SNPs associated with BMI in Korean individuals. Among the total of 1,827,004 loci, 435 loci were identified using a p-value cutoff (p < 5 × 10-4) from the GWAS. The default algorithm in the HaploView program for identifying LD blocks was taken from confidence intervals [13]. We set a ±20-kb window option and r2 > 0.9 to capture LD blocks of 435 significant SNPs based on the KARE genotype data. For 435 loci that were significant in the association analysis (p < 5 × 10-4), 53 LD blocks were identified using HaploView (Table 1). These 53 LD blocks contained all 435 significant loci. Thus, a typical LD block included multiple significant loci.

Phasing short reads

We downloaded whole-genome paired-end NGS data of seven samples from the TIARA database to discover haplotypes with the SNPs associated with BMI within the 53 LD blocks from the GWA analysis. We mapped the short reads to the human reference genome (hg19), and those reads with bad mapping quality (mapping quality <5) were filtered out. The average mapping rate was about 93%. We separated the mapped reads into the 53 LD blocks for each of seven individuals (Table 1). Each group of separated reads was called for variants, and the heterozygous SNPs were phased using the phase function in Samtools. Not all of the SNPs in an LD block were always phased into a contiguous haplotype block. Whenever multiple haplotype blocks were found for a given LD block, we kept only those that encompassed the significant SNPs identified from the GWAS.

Construction of the haplotype blocks

After phasing heterozygous SNPs using Samtools, we searched for phased haplotypes that harbored significant risk alleles for BMI from the GWA analysis results. We found 44 haplotype blocks in 23 of 53 LD blocks. We could not find haplotypes with significant risk alleles in the rest of the LD blocks. We ordered 44 haplotypes and established a catalog for possible haplotype blocks associated with BMI variations in the Korean population (Table 2). For example, AK4 and AK7 share the TCTGAGCC haplotype, which comprises the variants at bps 246142250, 246142279, 246144436, 246146137, 246146178, 246146444, 246148791, and 246149166 in chromosome 1. Bp 57895600 in chromosome 18 has been previously known as rs8089366 (G/T) in the dbSNP database and was a significant SNP for BMI in KARE. On the other hand, bp 57900630, which participated in the same haplotype, has not been registered in the dbSNP database. In Table 2, 16 LD blocks were shared by several individuals, while the rest of the LD blocks were carried by single individuals.

Distribution of haplotype blocks

We calculated the average space between alleles in a haplotype block from our results. For example, AK4, AK5, and AK7 share CGCC at bps 182065282, 182065314, 182065341, and 182065351 in chromosome 4 (Table 2). The haplotype block ranges from bp 182065282 through bp 182065351; the length of the haplotype is 69 bp. Since there are four SNPs, the average space is about 17. We plotted the distribution of the average spaces between SNPs within a haplotype (Fig. 2). The number of alleles found for a haplotype is also plotted in Fig. 2.


The GWAS is a powerful method to identify genomic variants affecting traits. Many studies have found important SNPs associated with diseases. However, the study is not well suited to identify rare variants or small effects of several SNPs [18]. Here, we tried to find haplotype blocks in 7 Korea individuals that harbor SNPs that are significantly associated with BMI in a Korean population. The significant SNPs for BMI were detected through GWASs, and the haplotypes were discovered by analyzing the NGS data. We selected short reads within the LD ranges that encompassed the significant SNPs from the GWAS results, and the heterozygous variants that were called from those reads were phased, based on the paired-end information. As a result, we detected 44 haplotype blocks harboring SNPs that are significantly associated with BMI in Koreans. The haplotypes may affect BMI in Korea individuals. However, it is not statically powerful, due to the small number of samples (7 individuals) used in this study. Actually, the alleles of a haplotype need to be compared with those of the haplotype of the same range in other individuals to have high accuracy. We could not follow this strategy. For example, most of the haplotypes found by this analysis were discovered in a single individual. In our small dataset, a significant SNP from a GWAS is not likely to be shared by several individuals, because many SNPs have a minor allele frequency of around 10% in a population. This problem may be solved if a large dataset is used. In addition, read depth also reduces accuracy. In Table 2, several haplotypes were identified in a single GWA LD block. These haplotypes could not be joined, as there were not enough short reads that could bridge them using the phase function in Samtools. In addition, AK7 has the GATX haplotype at bps 57903745, 57904011 (rs590654), 57904088 (rs590215), and 57907787 in chromosome 18, and AK4 has XAXC in the same locus. An X represents a missing allele, probably due to low coverage. We were able to recover them, as Samtools was still able to phase them. We filled in these alleles manually to build the haplotype. In Table 2, the green letters represent such alleles that have been filtered by read depth and recovered manually. Although the accuracy is not high, this integrative method has several advantages. First, rare variants can be detected. For example, AK4 has the TT risk allele haplotype at bps 57895600 and 57900630 in chromosome 18. The former was associated with BMI and was registered in the dbSNP database, while the latter was not registered in the dbSNP database. Bp 57900630 is possibly a low-frequency risk allele, which is difficult to be detected by GWAS. Second, we can explain a trait systematically. rs559623 is an SNP (C/A: forward strand) that is associated with BMI. It may act with bp 57844375 in chromosome 18 as CC according to our results (Table 2). Traits can be explained properly by multiple variants. Finally, this method is useful for studying phasing. Many SNPs from an array or NGS reads are unphased genotypes. However, the bulk of SNP information needs to be phased for identifying co-located alleles. While GATK HaplotypeCaller, Samtools Phase, and Beagle are used to phase variants, there are several problems, such as the long execution time and the need for large system memory. In fact, it takes too long to phase entire chromosomes or a single chromosome. However, with this method, it is possible to analyze efficiently with regard to time and memory, as it does not consider reference and genotype data of the entire chromosome. This method focuses only on the data within regions that are nearby important variants from the GWA analysis results. We constructed a crucial haplotype catalog for BMI traits in Korean individuals by integratively analyzing NGS data and GWA analysis data.


The genotype and phenotype data were kindly provided by the Korea National Institute of Health, Centers for Disease Control and Prevention, the Republic of Korea. Financial support for this work was made available by the National Research Foundation of Korea (NRF-2012M3A9D1054705), funded by the Ministry of Education, Science, and Technology.


This is 2014 KOBIC best paper awarded.


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Fig. 1
Pipeline for this study. First, next-generation sequencing raw data (fastq) are aligned using Genomic Short-read Nucleotide Alignment Program (GSNAP), and then, the reads without bad mapping quality are selected. In addition, significant single-nucleotide polymorphisms (SNPs) are learned from the genome-wide association study results, and linkage disequilibrium (LD) ranges with the SNPs are searched by HaploView. Using Samtools, reads within the LD ranges are selected and phased. As a result, a haplotype catalog can be constructed, including haplotypes with significant SNPs.
Fig. 2
Boxplots of average space between alleles (A) and the number of alleles in haplotype blocks (B).
Table 1.
Representative LD blocks from the GWA resultsa
CHR LD No. from GWAS SNP from GWAS SNP ID in LD Locus Manimum/Maximum
1 3 rs10753250 rs10753250 31973266 31973266/31977853
rs10158101 31975641
rs10914422 31977853
1 5 rs7542777 rs7542777 33788884 33789670/33796229
rs11061 33789670
rs1130800 33789968
rs11554674 33790496
rs7512470 33796229

LD, linkage disequilibrium; GWA, genome-wide association; CHR, chromosome; GWAS, genome-wide association study; SNP, single-nucleotide polymorphism; BMI, body mass index; KARE, Korea Association Resource.

a The LD ranges include SNPs that are significantly associated with BMI; 435 loci significant SNPs were analyzed from the KARE genotype data.

Table 2.
Haplotypes with significant SNPsa
CHR LD Sample Haplotype Locus
1 3 AK20 CT 33820033/33820134 (rs12026290)
5 AK4 GTXT 177820861/177821366 (rs3131313)/177822084/177823423/177824537
6 AK4 TCTGAGCC 246142250/246142279/246144436 (rs4654179)/246146137/246146178 (rs1538293)/246146444/246148791 (rs4654180)/246149166
6 AK4 CTA 246152107 (rs13376134)/246152121/246152129
6 AK4 TA 246141157 (rs12024270)/246141458
2 8 AK5 TT 222280602 (rs13013934)/222281275
4 11 AK6 GT 53133408 (rs729476)/53134293
12 AK7 TC 133942227 (rs13122167)/133942537
16 AK4 CGCC 182065282 (rs6824854)/182065314 (rs6825217)/182065341 (rs6824888)/182065351
16 AK5 CCGG 182065152/182065165/182065180 (rs6824986)/182065232 (rs4637448)
5 18 AK6 AGC 124683338/124683352/124683815 (rs10060296)
18 AK6 TG 124702595 (rs12654336)/124702788
18 AK6 CA 124708465/124709413 (rs925896)
18 AK6 CA 124719059/124720340 (rs1988043)
19 AK7 GG 160212103/160212114 (rs11135120)
21 AK7 AC 169356966 (rs6896240)/169357240
7 25 AK5 ACTTC 135329004(rs6979439)/135329107/135329269/135329690 (rs12540688)/135332379/
25 AK6 GC 135333854 (rs12540273)/135334111
25 AK5 TGA 135325910/135325947 (rs11984203)/135326104
25 AK5 GT 135334991/135335373 (rs4291211)
25 AK7 GT 135328689 (rs6975251)/135328873
8 26 AK6 CAA 5172935 (rs6993835)/5172992/5173185
26 AK5 ATTC 5176649/5176753/5176782/5176920 (rs1004161)
27 AK6 AAAACGCA 5211617 (rs7819482)/5212030/5212240/5212243/5212413 (rs2052334)/5212537/5212562 (rs7846604)/5212579
12 33 AK6 GGG 128182752 (rs10773418)/128183164/128184294
13 34 AK4 XTCG 55551581/55552706/55552879/55553277 (rs9536931)
14 36 AK4 GGGG 55158376/55158424/55158851/55160290 (rs2884684)
36 AK7 CC 55157430 (rs10483637)/55158376
15 38 AK6 GCAT 94581157/94581942/94581996 (rs2388442)/94582024
39 AK6 TCG 94590208/94590228 (rs1031912)/94591144
18 41 AK4 TACTC 4078560 (rs12967692)/4078698 (rs12967842)/4078802/4078845/4078852
41 AK4 CCTC 4079990/4081009/4081017/4081867 (rs6506177)
41 AK5 TAG 4084949/4085221 (rs6506178)/4085466
41 AK5 TGG 4087117/4087308/4087331 (rs7506389)
44 AK4 TCCT 57826273/57829899/57830095 (rs649721)/57831468 (rs633265)
44 AK4 CACA 57835406/57836482/57836715 (rs2051312)/57837028
44 AK4 CC 57843875 (rs559623)/57844375
45 AK4 CCGT 57871154/57872075/57872361 (rs2051312)/57872449 (rs8084515)
45 AK7 CG 57872956/57872989 (rs12955983)
46 AK4 XAXC 57903745/57904011 (rs590654)/57904088 (rs590215)/57907787
46 AK4 TT 57896038 (rs477181)/57896924
46 AK4 TT 57898600 (rs8089366)/57900630
46 AK7 GG 57893330/57893618 (rs8083289)
48 AK4 AC 57945105/57945953 (rs11152219)

SNP, single-nucleotide polymorphism; CHR, chromosome; LD, linkage disequilibrium; BMI, body mass index; GWA, genome-wide association.

a The underlined letters represent alleles without haplotype-phased results data but were filled in manually from the mapped data.

The bold letters represent SNPs that are significantly associated with BMI from the GWA analysis results, and the non-bold ones do not exist in the GWA analysis results.

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