Skip to main content

Advances in the genome-wide association study of chronic hepatitis B susceptibility in Asian population

Abstract

Chronic hepatitis B (CHB) is the most common chronic liver disease resulting from viral infection and has become a serious threat to human health. Each year, about 1.2 million people in the world die from diseases caused by chronic infection of hepatitis B virus. The genetic polymorphism is significantly associated with the susceptibility to chronic hepatitis B. Genome-wide association study was recently developed and has become an important tool to detect susceptibility genes of CHB. To date, a number of CHB-associated susceptibility loci and regions have been identified by scientists over the world. To clearly understand the role of susceptibility loci in the occurrence of CHB is important for the early diagnosis and prevention of CHB.

Background

Chronic hepatitis B (CHB) is the most common chronic liver disease caused by viral infection and has now become a serious threat to human health. The number of chronic HBV-infected patients worldwide has so far risen to 350–400 million, of which 25 to 40% eventually died of liver cirrhosis and liver cancer [1,2,3]. In China, there is a high incidence of hepatitis B with more than 120 million carriers of HBV. Each year, nearly 300,000 HBV carriers die of liver cirrhosis and liver cancer [4]. The causes of CHB are complicated, among which the main risk factors include familial spread, infant infection, infection due to immunologic inadequacy, and a history of other liver diseases. With advances in the epidemiological study and molecular biology research, scientists have found that many gene polymorphisms are actually associated with CHB occurrence [5], which has greatly enriched our understanding in the occurrence and progression of CHB. In recent years, the rapid development of genome-wide association study (GWAS) has become an important tool to search for CHB-related susceptibility genes. GWAS has been applied to screen CHB-related candidate SNPs by using multiple approaches of molecular biology and bioinformatics. The combined effects of important CHB susceptibility loci and the interaction between gene and environment have been further investigated in-depth. To clearly understand the role of each susceptibility loci in the occurrence of CHB will provide a solid theoretical base for early diagnosis and prevention of CHB.

Genome-wide association study

With the completion of Human Genome Project, GWAS became possible and developed rapidly. GWAS was designed to screen the disease-related genetic markers across a complete set of genomic DNA or genome to scrutinize genetic variations or single nucleotide polymorphisms (SNPs) associated with complex diseases. The database of genotypes and phenotypes (dbGAP), an international resource sharing center, was then established to archive and distribute data from studies such as GWAS in which associations between genetic variations and human diseases had been identified. GWAS aims at determining genes and susceptibility loci related to the occurrence of diseases, searching for genetic markers for the disease, therefore conducting early diagnosis and effective individualized treatment, as well as developing new drugs and novel specified disease prevention. GWAS has two types of design: single-stage design and two- or multi-stage design. Single-stage design is used to genotype SNP loci in a one-time manner for all selected case–control samples, and then analyze the association between each SNP and the disease. Two- or multi-stage design is a multi-step process used to genotype all SNP loci on a genome-wide scale in a small sample of population, followed by the selection of a few SNP loci found to be most significantly associated with the studied disease after statistical analysis. In another or multiple other larger independent populations, those selected SNPs will be further genotyped, and then analyzed according to the data obtained from the previous two or multi-stage studies. Single-stage design of GWAS is a costly process due to the large sample size required for genome-wide SNP genotyping. To reduce the number of genotyping and cost, most of investigators prefer to use a two- or multi-stage design. The main advantage of GWAS lies in its ability to comprehensively assess the association between genetic variations and diseases on a genome-wide scale without any biological hypothesis prior to the study.

Several methods such as DNA microarray analysis, exome sequencing, whole-genome sequencing were used while performing GWAS. DNA microarray (or DNA chip) is an important adjunct in performing GWAS because it allows a single sample to be simultaneously screened for variants at ~ 2 million known genetic markers in a single standardized assay at the whole-genome level. However, the high-throughput nature of DNA microarray combined with tremendous amount of datasets may result in a high rate of errors [6]. The reliability and reproducibility of microarrays are generally not satisfactory, which can be resulted from a number of factors such as the complexity of human genome, the quality of DNA prepared, the specificity of probes designed, the sample size, and the methods used for statistical analysis. To improve the reliability and reproducibility of microarray to some extent, all procedures involved should be tightly regulated and stringently quality controlled. Currently, microarray-based genotyping platforms were not widely used in GWAS [7]. Exome sequencing is a transcriptomics technique used to sequence all of the expressed genes in a genome (known as the exome). It is know that human exome only consists of 1% of the human genome, indicating the cost of sequencing can be substantially lowered in exome sequencing, typically only one-sixth to whole-genome sequencing. However, exome sequencing only covers 85% of disease-related variants and the remaining 15% of variants not located within the exon will escape from the screening [8]. Moreover, it is still not clear whether exome sequencing is able to fully capture genetic variants associated with complex disease [7]. Whole-genome sequencing has become cost and time efficient with the advent of next generation sequencing (NGS). Whole-genome sequencing provides greater coverage of the entire genome, which can capture both common and rare variants. However, despite the large drop in sequencing cost for a human genome, large-scale sequencing projects are still costly, which has prevented whole-genome sequencing from being widely used in GWAS that usually require thousands of samples [9]. The quality of the resulting draft sequence from whole-genome sequencing needs to be improved. Different NGS platforms used in GWAS may produce sequencing errors ranging from 0.5 to 2% [10]. Therefore, findings from whole-genome sequencing by using NGS should be further confirmed by more validated technologies.

The application of GWAS to the study of disease susceptibility

Single nucleotide polymorphism (SNP) refers to a DNA sequence polymorphism at the genomic level, caused by only a single nucleotide variation that commonly occurs within a population. SNP is the most common genetic variation in human, accounting for over 90% of all known polymorphisms. In recent years, as the third generation of genetic markers, SNP has been widely used to search for disease-related genes and to explain the difference among individuals and groups in terms of the susceptibility to the disease and the disease progression [11]. Traditional analysis of the association between complex diseases and candidate genes usually selects one or a few disease-related candidate genes based on an understanding of the disease; therefore, genes and SNP loci for study are poorly available.

In recent years, with the completion of two projects HapMap (The International HapMap Project) and 1000 Genomes (The 1000 Genomes Project) as well as the rapid development of high-throughput genotyping technology, GWAS has become an important tool for scientists to search for disease susceptibility-related genetic loci. In 2005, a GWAS for the age-related macular degeneration was first reported in Science, which is a hallmark for the application of GWAS to the medical field [12]. GWAS is generally performed to investigate millions of SNPs across the whole-genome, and is carried out on a large-scare, multi-center basis such that associations between diseases and genes can be repeated and confirmed. GWAS is not limited to a specific gene or chromosomal region, which offers itself a great potential to discover unknown genes related to certain disease under investigation. It also opens a new avenue to systematically study the genetic factors associated with complex diseases. Compared with traditional candidate gene study and family linkage study, GWAS presents to be much more effective in detecting disease-related genetic loci. Currently, GWAS has become a powerful tool to study the molecular etiology of complex diseases, and has greatly advanced the understanding of human diseases, such as macular retinae, breast cancer, prostate cancer, leukemia, coronary heart disease, obesity, diabetes, schizophrenia, and arthritis [13, 14]. These results have been published on many prestigious journals, in which a series of disease-related genes, genetic susceptibility regions and SNPs have been reported. As of December 2015, scientists have totally completed 1692 GWAS for 18 categories of nearly 300 complex diseases and traits, and have identified about 4000 SNPs that are associated with complex diseases and traits (Fig. 1).

Fig. 1
figure 1

The chromosomal distribution of SNPs associated with complex diseases and traits (NHGRI GWA Catalog: http://www.genome.gov/GWAStudies)

Application of GWAS to the study of CHB

The first GWAS on CHB was published in 2009. As of February 2016, a total of eleven GWAS on CHB have been reported. In these studies, a number of CHB-related susceptibility loci and genetic regions have been reported, among which 26 important candidate SNPs were identified (Table 1). As a newly emerging study tool, GWAS certainly needs to be further improved. The GWAS on CHB is recently getting started, therefore to summarize and properly interpret the GWAS data, and to understand the role of these loci in the pathogenesis of CHB in different populations will be critical for the application of GWAS to the study of CHB.

Table 1 GWAS on the genetic mechanism of chronic hepatitis B

Scientists and scholars at home and abroad have recently performed a series of GWAS on CHB and found multiple CHB-related SNPs. In 2009, Kamatani first reported the GWAS on CHB, in which he found two loci, rs9277535 within the HLA-DPB1 region and rs3077 within the HLA-DPA1 region, play important roles in the occurrence of CHB in Japanese population [15]. Following this study, Mbarek further confirmed Kamatani’s findings using GWAS also done with Japanese population [16]. Moreover, he and his colleagues found rs2856718-A within HLA-DQB1 region and rs7453920-G within HLA-DQB2 region are associated with the occurrence of CHB. In 2013, Kim [17] completed the GWAS on CHB using South Korea population, and found that rs652888 in the gene EHMT2, and rs1419881 in the gene TCF19 within the HLA region, are associated with an increased risk of CHB occurrence. In addition, this group also verified that these loci-rs9277535, rs3077, rs7453920, and rs2856718 are all associated with the occurrence of CHB in both Japanese population and Korean population. Cheong et al. [18] recently found that rs9394021 and rs2517459 two SNP loci in the VARS2-SFTA2 gene region are the new genetic markers in the Korean population of CHB in 2015. Nishida N conducted the first GWAS on how to protect from hepatitis B and clear HBV [19]. It was found that rs3077 in HLA-DPA1 region and rs9277542 in HLA-DPB1 region, both have a protective effect on CHB in East Asian population, and both are associated with HBV clearance.

Chinese scholars Liu L et al. first conducted GWAS on CHB with Chinese Han population in 2011 and found that rs11866328 G, located in the GRIN2A gene within 16p13.2 region, is a susceptibility locus associated with disease progression in carriers of HBV [20]. Recently Hu Z and his colleagues found that rs3130542-A in the gene TRNAI25 within 6p21.33 (near the HLA-C) region, and rs4821116-G in the gene UBE2L3 within the 22q11.21 region, are both associated with the occurrence of CHB in Chinese Han population [21]. Moreover, they further confirmed that rs7453920 is associated with the occurrence of CHB in Japanese and Korean populations as previously reported; they also found that this locus is actually associated with high prevalence of CHB in Chinese Han population. in 2015, a GWAS study on CHB performed by He et al. [22] in southwest China revealed 6 novel loci associated with increased prevalence rate of chronic hepatitis B, which are rs352140 on the gene TLR9, rs16944 on the gene IL1B, rs3212227 on the gene IL12B, rs3799488 on the gene IFNGR1, rs1059293 on the gene IFNGR2, rs467960 on the gene MX1, respectively. In addition, they also confirmed that the previously discovered 4 SNP loci namely rs3077, rs2856718, rs9277535, and rs7453920, are associated with CHB in this area. In the same year, another GWAS study led by Jiang et al. [23] in the eastern part of China revealed 5 novel CHB susceptibility loci, including rs12614 on the gene CFB, rs422951 on the gene NOTCH4, rs378352 on the gene HLA-DOA, rs188332 on the gene HLA-C, and rs18838325 on the gene CD40, respectively. In 2016, Zhu et al. fine mapped the histocompatibility complex (MHC) region by using their existing GWAS data and identified four additional susceptibility loci that independently drove the chronic HBV infection in Han Chinese [24]. Nishida et al. applied HLA imputation method to determine HLA alleles by using genome-wide SNP typing data of 1975 Japanese individuals, and found that a SNP located in the HLA-DP locus from GWAS was strongly associated with CHB susceptibility [25]. Xiang et al. found that human leukocyte antigen DP/DQ gene (HLA-DP/DQ) polymorphisms (rs9277471, rs9277535, and rs9277542 in HLA-DP; rs9272346 in HLA-DQ) are associated with chronic hepatitis B in Chinese Han (400 patients) and Uygur (399 patients) populations [26]. In 2017, Shin et al. identified rs1265163 in OCT4 as a novel genetic marker for CHB susceptibility in a 3902 Korean individuals in a follow-up study to their GWAS [27].

In order to evaluate the immune response after injection of hepatitis B vaccine, Png E and his colleagues completed in 2011 the first GWAS on the immune response of hepatitis B vaccine in the population of Indonesia [28]. It was found that rs3135363 within the HLA-DR region, rs9277535 within the HLA-DPB1 region, and rs9267665 within the HLA-DP region are all associated with an immune response after the injection of hepatitis B vaccine. A subsequent GWAS completed by Pan also confirmed that rs3135363 is associated with an immune response after injection of hepatitis B vaccine in the Chinese Han population [29]. However, the locus rs477515-T located within the gene TRNAI25, 15 kb upstream of HLA-DRB1, showed no significant correlation with the immune response of hepatitis B vaccine.

The genetic background among human populations is generally different and the mechanism of pathogenesis can also differ. As a complex disease which involves multi-genes, chronic HBV infection is challenging to work on and the conclusions drew in the studies can be inconsistent due to the differences in the genetic background of populations and area studied. Therefore, the relationship between CHB and SNP loci screened by GWAS still remains unclear. More populations and larger independent samples are needed in order to improve the consistency of GWAS results.

Conclusions

At present, GWAS only analyzes the effect of a single locus on the disease susceptibility. However, such effect on the complex disease is generally very weak, therefore this strategy cannot be simply taken to investigate the causes of complex diseases. Hence, how to find an effective way to perform in-depth analysis for GWAS data and then detect more susceptibility genes has become a new research hotspot [30]. To date, different strategies and approaches have been successively taken in the follow-up studies of GWAS for complex diseases in order to perform in-depth data mining. For example, when testing the correlation between a SNP and certain diseases, the interactions between this SNP and other SNPs will be also thoroughly examined (i.e., epistatic effect). Several analytical methods, such as Bayesian epistasis, classification and regression trees, and multifactor dimensionality reduction, have been widely used in data mining [31,32,33]. The application of these strategies and approaches has compensated for the disadvantage of classical GWAS, and has also deepened our understanding in the genetic mechanisms of complex human diseases. For this reason, the follow-up studies for the GWAS data are therefore of high importance.

Abbreviations

CHB:

chronic hepatitis B

HBV:

hepatitis B virus

GWAS:

genome-wide association study

References

  1. Bhat M, Ghali P, Deschenes M, Wong P. Prevention and management of chronic hepatitis B. Int J Prev Med. 2014;5(Suppl 3):S200–7.

    PubMed  PubMed Central  Google Scholar 

  2. Liaw YF, Kao JH, Piratvisuth T, Chan HL, Chien RN, Liu CJ, Gane E, Locarnini S, Lim SG, Han KH, Amarapurkar D, Cooksley G, Jafri W, Mohamed R, Hou JL, Chuang WL, et al. Asian-Pacific consensus statement on the management of chronic hepatitis B: a 2012 update. Hep Int. 2012;6(3):531–61.

    Article  Google Scholar 

  3. Trepo C, Chan HL, Lok A. Hepatitis B virus infection. Lancet. 2014;384(9959):2053–63.

    Article  CAS  PubMed  Google Scholar 

  4. Cui Y, Jia J. Update on epidemiology of hepatitis B and C in China. J Gastroenterol Hepatol. 2013;28(Suppl 1):7–10.

    Article  PubMed  Google Scholar 

  5. Ezzikouri S, Ozawa M, Kohara M, Elmdaghri N, Benjelloun S, Tsukiyama-Kohara K. Recent insights into hepatitis B virus-host interactions. J Med Virol. 2014;86(6):925–32.

    Article  PubMed  Google Scholar 

  6. Thorat M. Tuning into the genetic orchestra using microarrays: limitations of DNA microarrays in clinical practice. Nat Clin Pract Oncol. 2006;3(12):E1.

    Article  PubMed  Google Scholar 

  7. Lacey S, Chung JY, Lin H. A comparison of whole genome sequencing with exome sequencing for family-based association studies. BMC Proc. 2014;8(Suppl 1 Genetic Analysis Workshop 18Vanessa Olmo):S38.

    Article  PubMed  PubMed Central  Google Scholar 

  8. Lalonde E, Albrecht S, Ha KC, Jacob K, Bolduc N, Polychronakos C, Dechelotte P, Majewski J, Jabado N. Unexpected allelic heterogeneity and spectrum of mutations in Fowler syndrome revealed by next-generation exome sequencing. Hum Mutat. 2010;31(8):918–23.

    Article  CAS  PubMed  Google Scholar 

  9. Petersen BS, Fredrich B, Hoeppner MP, Ellinghaus D, Franke A. Opportunities and challenges of whole-genome and -exome sequencing. BMC Genet. 2017;18(1):14.

    Article  PubMed  PubMed Central  Google Scholar 

  10. Kim SY, Li Y, Guo Y, Li R, Holmkvist J, Hansen T, Pedersen O, Wang J, Nielsen R. Design of association studies with pooled or un-pooled next-generation sequencing data. Genet Epidemiol. 2010;34(5):479–91.

    Article  PubMed  PubMed Central  Google Scholar 

  11. Seal A, Gupta A, Mahalaxmi M, Aykkal R, Singh TR, Arunachalam V. Tools, resources and databases for SNPs and indels in sequences: a review. Int J Bioinform Res Appl. 2014;10(3):264–96.

    Article  CAS  PubMed  Google Scholar 

  12. Klein RJ, Zeiss C, Chew EY, Tsai JY, Sackler RS, Haynes C, Henning AK, SanGiovanni JP, Mane SM, Mayne ST, Bracken MB, Ferris FL, Ott J, Barnstable C, Hoh J. Complement factor H polymorphism in age-related macular degeneration. Science. 2005;308(5720):385–9.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Arking D, Rommens J. Editorial overview: molecular and genetic bases of disease: Enter the post-GWAS era. Curr Opin Genet Dev. 2015;33:77–9.

    Article  CAS  PubMed  Google Scholar 

  14. van der Sijde MR, Ng A, Fu J. Systems genetics: from GWAS to disease pathways. Biochem Biophys Acta. 2014;1842(10):1903–9.

    PubMed  Google Scholar 

  15. Kamatani Y, Wattanapokayakit S, Ochi H, Kawaguchi T, Takahashi A, Hosono N, Kubo M, Tsunoda T, Kamatani N, Kumada H, Puseenam A, Sura T, Daigo Y, Chayama K, Chantratita W, Nakamura Y, et al. A genome-wide association study identifies variants in the HLA-DP locus associated with chronic hepatitis B in Asians. Nat Genet. 2009;41(5):591–5.

    Article  CAS  PubMed  Google Scholar 

  16. Mbarek H, Ochi H, Urabe Y, Kumar V, Kubo M, Hosono N, Takahashi A, Kamatani Y, Miki D, Abe H, Tsunoda T, Kamatani N, Chayama K, Nakamura Y, Matsuda K. A genome-wide association study of chronic hepatitis B identified novel risk locus in a Japanese population. Hum Mol Genet. 2011;20(19):3884–92.

    Article  CAS  PubMed  Google Scholar 

  17. Kim YJ, Kim HY, Lee JH, Yu SJ, Yoon JH, Lee HS, Kim CY, Cheong JY, Cho SW, Park NH, Park BL, Namgoong S, Kim LH, Cheong HS, Shin HD. A genome-wide association study identified new variants associated with the risk of chronic hepatitis B. Hum Mol Genet. 2013;22(20):4233–8.

    Article  CAS  PubMed  Google Scholar 

  18. Cheong HS, Lee JH, Yu SJ, Yoon JH, Lee HS, Cheong JY, Cho SW, Park NH, Park BL, Namgoong S, Kim LH, Shin HD, Kim YJ. Association of VARS2-SFTA2 polymorphisms with the risk of chronic hepatitis B in a Korean population. Liver Int. 2015;35(8):1934–40.

    Article  CAS  PubMed  Google Scholar 

  19. Nishida N, Sawai H, Matsuura K, Sugiyama M, Ahn SH, Park JY, Hige S, Kang JH, Suzuki K, Kurosaki M, Asahina Y, Mochida S, Watanabe M, Tanaka E, Honda M, Kaneko S, et al. Genome-wide association study confirming association of HLA-DP with protection against chronic hepatitis B and viral clearance in Japanese and Korean. PLoS ONE. 2012;7(6):e39175.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Liu L, Li J, Yao J, Yu J, Zhang J, Ning Q, Wen Z, Yang D, He Y, Kong X, Song Q, Chen M, Yang H, Liu Q, Li S, Lin J. A genome-wide association study with DNA pooling identifies the variant rs11866328 in the GRIN2A gene that affects disease progression of chronic HBV infection. Viral Immunol. 2011;24(5):397–402.

    Article  CAS  PubMed  Google Scholar 

  21. Hu Z, Liu Y, Zhai X, Dai J, Jin G, Wang L, Zhu L, Yang Y, Liu J, Chu M, Wen J, Xie K, Du G, Wang Q, Zhou Y, Cao M, et al. New loci associated with chronic hepatitis B virus infection in Han Chinese. Nat Genet. 2013;45(12):1499–503.

    Article  CAS  PubMed  Google Scholar 

  22. He D, Tao S, Guo S, Li M, Wu J, Huang H, Guo X, Yan G, Zhu P, Wang Y. Interaction of TLR-IFN and HLA polymorphisms on susceptibility of chronic HBV infection in Southwest Han Chinese. Liver Int. 2015;35(8):1941–9.

    Article  CAS  PubMed  Google Scholar 

  23. Jiang DK, Ma XP, Yu H, Cao G, Ding DL, Chen H, Huang HX, Gao YZ, Wu XP, Long XD, Zhang H, Zhang Y, Gao Y, Chen TY, Ren WH, Zhang P, et al. Genetic variants in five novel loci including CFB and CD40 predispose to chronic hepatitis B. Hepatology. 2015;62(1):118–28.

    Article  CAS  PubMed  Google Scholar 

  24. Zhu M, Dai J, Wang C, Wang Y, Qin N, Ma H, Song C, Zhai X, Yang Y, Liu J, Liu L, Li S, Yang H, Zhu F, Shi Y, Shen H, et al. Fine mapping the MHC region identified four independent variants modifying susceptibility to chronic hepatitis B in Han Chinese. Hum Mol Genet. 2016;25(6):1225–32.

    Article  CAS  PubMed  Google Scholar 

  25. Nishida N, Ohashi J, Khor SS, Sugiyama M, Tsuchiura T, Sawai H, Hino K, Honda M, Kaneko S, Yatsuhashi H, Yokosuka O, Koike K, Kurosaki M, Izumi N, Korenaga M, Kang JH, et al. Understanding of HLA-conferred susceptibility to chronic hepatitis B infection requires HLA genotyping-based association analysis. Sci Rep. 2016;6:24767.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Xiang X, Guo Y, Yang L, Ge Q, Mijit S, Xu F. Association of human leukocyte antigen DP/DQ gene polymorphisms with chronic hepatitis B in Chinese Han and Uygur populations. Infect Genet Evol J Mol Epidemiol Evolut Genet Infect Dis. 2016;43:407–11.

    CAS  Google Scholar 

  27. Shin JG, Cheong HS, Lee K, Ju BG, Lee JH, Yu SJ, Yoon JH, Cheong JY, Cho SW, Park NH, Namgoong S, Kim LH, Kim YJ, Shin HD. Identification of novel OCT4 genetic variant associated with the risk of chronic hepatitis B in a Korean population. Liver Int. 2017;37(3):354–61.

    Article  CAS  PubMed  Google Scholar 

  28. Png E, Thalamuthu A, Ong RT, Snippe H, Boland GJ, Seielstad M. A genome-wide association study of hepatitis B vaccine response in an Indonesian population reveals multiple independent risk variants in the HLA region. Hum Mol Genet. 2011;20(19):3893–8.

    Article  CAS  PubMed  Google Scholar 

  29. Pan L, Zhang L, Zhang W, Wu X, Li Y, Yan B, Zhu X, Liu X, Yang C, Xu J, Zhou G, Xu A, Li H, Liu Y. A genome-wide association study identifies polymorphisms in the HLA-DR region associated with non-response to hepatitis B vaccination in Chinese Han populations. Hum Mol Genet. 2014;23(8):2210–9.

    Article  CAS  PubMed  Google Scholar 

  30. Begum F, Sharker MH, Sherman SL, Tseng GC, Feingold E. Regionally Smoothed Meta-Analysis Methods for GWAS Datasets. Genet Epidemiol. 2016;40(2):154–60.

    Article  PubMed  Google Scholar 

  31. Mak TS, Best N, Rushton L. Robust bayesian sensitivity analysis for case-control studies with uncertain exposure misclassification probabilities. Int J Biostat. 2015;11(1):135–49.

    Article  PubMed  Google Scholar 

  32. Henrard S, Speybroeck N, Hermans C. Classification and regression tree analysis vs. multivariable linear and logistic regression methods as statistical tools for studying haemophilia. Haemophilia. 2015;21(6):715–22.

    Article  CAS  PubMed  Google Scholar 

  33. Pan Q, Hu T, Moore JH. Epistasis, complexity, and multifactor dimensionality reduction. Methods Mol Biol. 2013;1019:465–77.

    Article  CAS  PubMed  Google Scholar 

Download references

Authors’ contributions

BQ and WJ initiated the review; BQ, WJ, AM, HH, YZ, and LT performed the review, analyzed the data, and wrote the manuscript; MO and KS reviewed and commented on the manuscript, and provided final approval. All authors read and approved the final manuscript.

Acknowledgements

The authors are grateful to Mojtaba Olyaee (Department of Gastroenterology and Hepatology, the University of Kansas Hospital, Kansas, America), Kenji Shimura and Akihiro Miyakawa (Department of gastroenterology, Asahi General Hospital, Chiba, Japan) for providing helpful suggestions, data analysis, and linguistic modification.

Competing interests

The authors declare that they have no competing interests.

Availability of data and materials

The datasets are available from the corresponding author under reasonable request.

Consent for publication

All authors were informed and agreed to publish this work under the current authorship.

Ethics approval and consent to participate

The Ethics Committee of Harbin Medical University approved this study. Consent to participate was not applicable.

Funding

This work was supported by the Postdoctoral Research Start-up Fund of Heilongjiang Province in China (grant no. LBH-Q17160) and the Foundation of Excellent Returned Overseas Students of Heilongjiang Province in China (grant no. 2015-424).

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Bing Qiu.

Rights and permissions

Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Qiu, B., Jiang, W., Olyaee, M. et al. Advances in the genome-wide association study of chronic hepatitis B susceptibility in Asian population. Eur J Med Res 22, 55 (2017). https://doi.org/10.1186/s40001-017-0288-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/s40001-017-0288-3

Keywords