BioMed Research International
Volume 2016 (2016), Article ID 2395341, 6 pages
http://dx.doi.org/10.1155/2016/2395341

Research Article

Identifying Liver Cancer-Related Enhancer SNPs by Integrating GWAS and Histone Modification ChIP-seq Data

Tianjiao Zhang,¹ Yang Hu,² Xiaoliang Wu,¹ Rui Ma,¹ Qinghua Jiang,² and Yadong Wang¹

¹School of Computer Science and Technology, Harbin Institute of Technology, Harbin 150001, China
²School of Life Science and Technology, Harbin Institute of Technology, Harbin 150001, China

Received 9 April 2016; Revised 30 May 2016; Accepted 1 June 2016

Academic Editor: Xun Lan

Copyright © 2016 Tianjiao Zhang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract.

Many disease-related single nucleotide polymorphisms (SNPs) have been inferred from genome-wide association studies (GWAS) in recent years. Numerous studies have shown that some SNPs located in protein-coding regions are associated with numerous diseases by affecting gene expression. However, in noncoding regions, the mechanism of how SNPs contribute to disease susceptibility remains unclear. Enhancer elements are functional segments of DNA located in noncoding regions that play an important role in regulating gene expression. The SNPs located in enhancer elements may affect gene expression and lead to disease. We presented a method for identifying liver cancer-related enhancer SNPs through integrating GWAS and histone modification ChIP-seq data. We identified 22 liver cancer-related enhancer SNPs, 9 of which were regulatory SNPs involved in distal transcriptional regulation. The results highlight that these enhancer SNPs may play important roles in liver cancer.

1. Introduction

Single nucleotide polymorphism (SNP) is a variation at a single nucleotide in a DNA sequence [1]. In the last decade, a large number of genome-wide association studies (GWAS) have been published, indicating that thousands of SNPs are associated with diseases. Linkage disequilibrium is the nonrandom association of alleles at different genome locations [2]. There are many SNPs in LD with the causal SNP at specific GWAS locus [3, 4]. Over 90% of these GWAS variants are located in noncoding regions, and approximately 10% are in LD with a protein-coding variant [5, 6]. In protein-coding regions, many studies have shown that some SNPs are associated with numerous diseases by affecting gene expression [7, 8]. However, in noncoding regions, the mechanism of how SNPs contribute to disease susceptibility remains unclear.

Enhancers are the core regulatory components of the genome that act over a distance to positively regulate gene expression [9]. It is estimated that 400,000 to 1 million putative enhancers exist in the human genome [10, 11]. Recently, some studies have shown that disease-related GWAS SNPs are correlated with enhancers marked with special histone modifications [12–15]. Therefore, through integrating GWAS and histone modification ChIP-seq data in a given disorder, we can identify disease-related enhancer SNPs.

We provided a method for identifying liver cancer-related enhancer SNPs through integrating liver cancer GWAS and histone modification ChIP-seq data. We identified 22 enhancer SNPs associated with liver cancer, 9 of which were regulatory SNPs involved in distal transcriptional regulation. The results highlight that these enhancer SNPs may play important roles in liver cancer.

2. Results

2.1. Pipeline of Identifying Liver Cancer-Related Enhancer SNPs

As shown in Figure 1, the pipeline consists of four steps. Firstly, we downloaded liver cancer-related GWAS SNPs from the GRASP [16] database and used LD data from HapMap [17] to infer liver cancer-related LD SNPs. Secondly, we identified enhancer regions in liver cancer through integrating histone modification ChIP-seq data in the HepG2 cell line. Thirdly, we mapped the liver cancer-related LD SNPs to the identified enhancers in liver cancer and obtained liver cancer-related enhancer SNPs. Finally, we used a curated regulatory SNP database named rVarBase [18] to validate our results.

Figure 1: Flowchart identifying liver cancer-related enhancer SNPs. LD SNPs: SNPs located in linkage disequilibrium blocks of GWAS SNPs. Enhancer SNPs: SNPs identified in enhancer regions. rVarBase: a database for variant’s regulatory features.

2.2. Linkage Disequilibrium Analysis with Liver Cancer-Associated SNPs

We obtained 45 liver cancer-associated SNPs from GRASP (Table 1). These SNPs are the raw potential liver cancer-related SNPs. Then, we used LD data from HapMap to achieve liver cancer-associated LD SNPs. The total number of potential liver cancer-related SNPs is 340.

Table 1: Summary of liver cancer-associated SNPs from GRASP database.

SNP ID
value
Chromosome
Populations
PMID

rs17401966
1.20E − 19
1
CHB
20676096

rs1249458
8.30E − 06
2
CHB
22807686

rs1714259
1.10E − 06
2
CHB
22807686

rs2396470
5.10E − 07
2
CHB
20676096

rs7424161
8.80E − 06
2
CHB
22807686

rs7574865
1.70E − 11
2
CHB
23242368

rs3905886
3.70E − 06
3
CHB
22807686

rs1073547
6.80E − 06
4
CHB
22807686

rs17081345
3.70E − 07
6
CHB
20676096

rs9272105
3.30E − 23
6
CHB
22807686

rs9275319
8.70E − 19
6
CHB
23242368

rs9494257
1.10E − 14
6
CHB
20676096

rs12682266
6.70E − 06
8
CHB
22174901

rs1573266
7.40E − 06
8
CHB
22174901

rs2275959
6.40E − 06
8
CHB
22174901

rs7821974
7.00E − 06
8
CHB
22174901

rs7898005
7.00E − 08
10
CHB
20676096

rs10160758
6.00E − 06
11
CHB
22807686

rs10896464
6.50E − 06
11
CHB
22807686

rs2611145
9.30E − 06
11
CHB
22174901

rs3825023
3.10E − 06
11
CHB
22807686

rs7119426
3.50E − 06
11
CHB
22807686

rs402071
8.60E − 06
19
CHB
22807686

rs3092194
4.40E − 06
20
CHB
22807686

rs368007
9.90E − 06
20
CHB
22807686

rs455804
4.40E − 10
21
CHB
22807686

rs1980215
2.30E − 06
3
JPT
21499248

rs2596542
4.20E − 13
6
JPT
21499248

rs9275572
1.40E − 09
6
JPT
21499248

rs1568658
6.90E − 06
7
JPT
21499248

rs952656
2.80E − 06
8
JPT
21499248

rs4363614
4.20E − 07
11
JPT
21499248

rs1957496
4.60E − 06
14
JPT
21499248

rs8019534
3.90E − 06
14
JPT
21499248

rs1794304
3.60E − 06
16
JPT
21725309

rs2208456
3.80E − 06
20
JPT
21499248

rs1012068
1.30E − 14
22
JPT
21725309

rs11703779
4.20E − 06
22
JPT
21725309

rs4820994
3.00E − 06
22
JPT
21725309

rs4820996
4.20E − 06
22
JPT
21725309

rs5753816
4.90E − 06
22
JPT
21725309

rs5753818
9.40E − 06
22
JPT
21725309

rs5998152
1.20E − 07
22
JPT
21725309

rs7287054
3.80E − 06
22
JPT
21725309

rs737084
5.90E − 06
22
JPT
21725309

2.3. Identification of Liver Cancer-Related Enhancer SNPs

Previous studies indicated that the enhancer regions are marked by a strong H3K4me1 signal and a relatively weak H3K4me3 signal [19, 20]. Thus, we used histone modification ChIP-seq data to recognize the enhancer regions in liver cancer. Then, we mapped the liver cancer-related GWAS SNPs to the enhancer regions and obtained 22 enhancer SNPs in liver cancer (Table 2).

Table 2: Summary of predicted enhancer SNPs in liver cancer.

SNP ID
Chromosome
Start
End
Chain
Populations

rs12751375
chr1
10291873
10291874
+
CHB

rs6700866
chr1
10306037
10306038
+
CHB

rs9494257
chr6
135827471
135827472
+
CHB

rs17064474
chr6
135680137
135680138
+
CHB

rs17721919
chr6
135748923
135748924
+
CHB

rs17721931
chr6
135749377
135749378
+
CHB

rs6903949
chr6
135821065
135821066
+
CHB

rs6996881
chr8
37407919
37407920
+
CHB

rs4739519
chr8
37412858
37412859
+
CHB

rs6988263
chr8
37414659
37414660
+
CHB

rs12156293
chr8
37419921
37419922
+
CHB

rs6928810
chr6
31410523
31410524
+
JPT

rs3869132
chr6
31410947
31410948
−
JPT

rs2596562
chr6
31354594
31354595
−
JPT

rs2523475
chr6
31361709
31361710
−
JPT

rs2523467
chr6
31362929
31362930
−
JPT

rs9501387
chr6
31364458
31364459
+
JPT

rs1568658
chr7
29141557
29141558
−
JPT

rs1794304
chr16
12625394
12625395
+
JPT

rs5994449
chr22
32304178
32304179
+
JPT

rs5753816
chr22
32312841
32312842
+
JPT

rs5749339
chr22
32315734
32315735
+
JPT

2.4. Validation as Regulatory SNPs

rVarBase is a database that provides reliable, comprehensive, and user-friendly annotations on variant’s regulatory features [18]. It includes regulatory SNPs (rSNPs), LD-proxies of rSNPs, and genes that are potentially regulated by rSNPs. We used rVarBase to analyze these 22 enhancer SNPs in liver cancer and found that 14 SNPs have evidence of regulatory SNPs and 9 SNPs (rs9494257, rs6903949, rs6996881, rs4739519, rs6988263, rs12156293, rs1568658, rs5994449, and rs5753816) are involved in distal transcriptional regulation (Table 3). Table 4 shows the potential target genes of these 9 SNPs.

Table 3: Summary of liver cancer-related regulatory SNPs validated by rVarBase.

SNP ID
Regulatory SNP
Distal regulation
Chromatin state
Related regulatory elements

rs12751375
Yes
No
Inactive region
n/a

rs6700866
Yes
No
Weak transcription; ZNF genes and repeats; strong transcription; enhancers
n/a

rs9494257
Yes
Yes
Enhancers; flanking active TSS; weak transcription
Chromatin interactive region

rs17064474
Yes
No
Weak transcription; active TSS; flanking active TSS; enhancers
n/a

rs17721919
Yes
No
Weak transcription
n/a

rs17721931
Yes
No
Weak transcription
n/a

rs6903949
Yes
Yes
Weak transcription; enhancers
TF binding region; chromatin interactive region

rs6996881
Yes
Yes
Weak transcription; enhancers
Chromatin interactive region

rs4739519
Yes
Yes
Enhancers; weak transcription
Chromatin interactive region

rs6988263
Yes
Yes
Enhancers; weak transcription; genic enhancers; bivalent enhancer; flanking active TSS
Chromatin interactive region

rs12156293
Yes
Yes
Enhancers; weak transcription; bivalent enhancer; genic enhancers
Chromatin interactive region

rs1568658
Yes
Yes
Weak transcription; enhancers; strong transcription
Chromatin interactive region

rs5994449
Yes
Yes
Weak transcription; strong transcription; ZNF genes and repeats
Chromatin interactive region

rs5753816
Yes
Yes
Weak transcription; enhancers; flanking active TSS
Chromatin interactive region

Table 4: Summary of liver cancer-related regulatory SNPs and potential target genes validated by rVarBase.

SNP ID
Gene symbol
Ensemble ID
Regulation type

rs9494257
BCLAF1
ENSG00000029363
Distal transcriptional regulation

rs9494257
AHI1
ENSG00000135541
Distal transcriptional regulation

rs9494257
LINC00271
ENSG00000231028
Distal transcriptional regulation

rs6903949
MYB
ENSG00000118513
Distal transcriptional regulation

rs6903949
BCLAF1
ENSG00000029363
Distal transcriptional regulation

rs6903949
AHI1
ENSG00000135541
Distal transcriptional regulation

rs6903949
LINC00271
ENSG00000231028
Distal transcriptional regulation

rs6996881
ZNF703
ENSG00000183779
Distal transcriptional regulation

rs6996881
ERLIN2
ENSG00000147475
Distal transcriptional regulation

rs6996881
Null
ENSG00000183154
Distal transcriptional regulation

rs6996881
Null
ENSG00000253161
Distal transcriptional regulation

rs4739519
ZNF703
ENSG00000183779
Distal transcriptional regulation

rs4739519
Null
ENSG00000254290
Distal transcriptional regulation

rs6988263
ZNF703
ENSG00000183779
Distal transcriptional regulation

rs6988263
Null
ENSG00000254290
Distal transcriptional regulation

rs12156293
ZNF703
ENSG00000183779
Distal transcriptional regulation

rs12156293
Null
ENSG00000254290
Distal transcriptional regulation

rs12156293
ERLIN2
ENSG00000147475
Distal transcriptional regulation

rs12156293
Null
ENSG00000183154
Distal transcriptional regulation

rs1568658
Null
ENSG00000228421
Distal transcriptional regulation

rs1568658
TRIL
ENSG00000176734
Distal transcriptional regulation

rs1568658
Null
ENSG00000255690
Distal transcriptional regulation

rs5994449
DEPDC5
ENSG00000100150
Distal transcriptional regulation

rs5994449
FBXO7
ENSG00000100225
Distal transcriptional regulation

rs5994449
SYN3
ENSG00000185666
Distal transcriptional regulation

rs5994449
PRR14L
ENSG00000183530
Distal transcriptional regulation

rs5994449
PISD
ENSG00000241878
Distal transcriptional regulation

rs5994449
EIF4ENIF1
ENSG00000184708
Distal transcriptional regulation

rs5994449
RNU6-28
ENSG00000199248
Distal transcriptional regulation

rs5994449
SFI1
ENSG00000198089
Distal transcriptional regulation

rs5753816
YWHAH
ENSG00000128245
Distal transcriptional regulation

rs5753816
C22orf24
ENSG00000128254
Distal transcriptional regulation

rs5753816
PISD
ENSG00000241878
Distal transcriptional regulation

rs5753816
DEPDC5
ENSG00000100150
Distal transcriptional regulation

rs5753816
RNU6-28
ENSG00000199248
Distal transcriptional regulation

rs5753816
SFI1
ENSG00000198089
Distal transcriptional regulation

rs5753816
EIF4ENIF1
ENSG00000184708
Distal transcriptional regulation

rs5753816
RFPL3S
ENSG00000205853
Distal transcriptional regulation

rs5753816
Null
ENSG00000230736
Distal transcriptional regulation

rs5753816
Null
ENSG00000243519
Distal transcriptional regulation

rs5753816
Null
ENSG00000241954
Distal transcriptional regulation

rs5753816
Null
ENSG00000232218
Distal transcriptional regulation

rs5753816
SYN3
ENSG00000185666
Distal transcriptional regulation

3. Materials and Methods

3.1. GWAS and LD Datasets

We downloaded the human liver cancer-related GWAS SNPs from GRASP. The database includes 26 and 19 liver cancer-associated SNPs () from Han Chinese in Beijing, China (CHB), and Japanese in Tokyo, Japan (JPT), respectively. The URL is https://grasp.nhlbi.nih.gov/Overview.aspx. We obtained all SNPs in LD with GWAS-lead SNPs using LD blocks identified with publicly available HapMap data on the CHB and JPT populations. The LD data can be downloaded from http://hapmap.ncbi.nlm.nih.gov/index.html/.

3.2. Histone Modification Datasets

We downloaded the human histone modification ChIP-seq datasets in the HepG2 cell line from the ENCODE Production Data/Broad Institute. The URL is http://genome.ucsc.edu/ENCODE/downloads.html.

3.3. Linkage Disequilibrium Analysis

In the genome, SNPs located in close proximity tend to be in linkage disequilibrium with each other. The International HapMap Project has established linkage disequilibrium of human genome SNPs. We used LD data from HapMap to achieve liver cancer-associated LD SNPs ().

3.4. Identify Enhancer Regions and Enhancer SNPs

Firstly, we downloaded the human histone modification BAM files (H3K4me1 and H3K4me3) in the HepG2 cell line from the ENCODE project. Then, we used BEDtools [21] to count read coverage for every position of the genome. Through calculating the ratio H3K4me1/H3K4me3 and picking up the regions with log₂(H3K4me1/H3K4me3) > 1.2, we identified the potential enhancer regions. Finally, we mapped the potential LD SNPs to these enhancer regions and achieved liver cancer-related enhancer SNPs.

4. Discussion

Through integrating liver cancer GWAS SNPs from GRASP, LD data from HapMap, and histone modification ChIP-seq data from ENCODE, we explored liver cancer-related enhancer SNPs. We compared our results with rVarBase and found that 9 SNPs (rs9494257, rs6903949, rs6996881, rs4739519, rs6988263, rs12156293, rs1568658, rs5994449, and rs5753816) were regulatory SNPs involved in distal transcriptional regulation. The results highlight that these enhancer SNPs may play important roles in liver cancer.

Compared with protein-coding regions in the human genome, noncoding regions contain much more genetic variations. Some important regulation regions, such as enhancers, have great influence on target gene expression. SNPs located in these regions may disturb gene expression and even cause diseases. Thus, the identification of SNPs in enhancer regions is helpful to understand the mechanism of association between SNPs and diseases.

We presented a method to identify disease-related SNPs located in enhancer regions that gives a new solution to investigate the relationship between SNPs and diseases. The presented method can also be applied to other diseases and will enable biologists to investigate the mechanism of disease risk associated with SNPs.

Competing Interests

The authors declare that there are no competing interests regarding the publication of this paper.

Authors’ Contributions

Tianjiao Zhang collected the data, designed the computational experiments, carried out the statistical analysis, and wrote the paper. Qinghua Jiang participated in the design of the study. Yang Hu, Xiaoliang Wu, and Rui Ma participated in the revision of this paper. Yadong Wang gave comments and revisions to the final version of this paper. All authors read and approved the final paper. Tianjiao Zhang and Yang Hu equally contributed to this paper.

Acknowledgments

This work was partially supported by the National High-Tech Research and Development Program (863) of China (2012AA02A601, 2012AA02A602, 2012AA020404, 2012AA020409, 2012AA02A604, 2014AA021505, 2015AA020101, and 2015AA020108), National Science and Technology Major Project [no. 2013ZX03005012], and the National Natural Science Foundation of China (61571152, 31301089).

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