HiView: an integrative genome browser to leverage Hi-C results for the interpretation of GWAS variants

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  • Xu, Zheng
    • Affiliation: College of Arts and Sciences, Department of Computer Science, Gillings School of Global Public Health, Department of Biostatistics
  • Zhang, Guosheng
    • Affiliation: School of Medicine, Curriculum in Bioinformatics and Computational Biology, Department of Genetics
  • Duan, Qing
    • Affiliation: School of Medicine, Department of Genetics
  • Chai, Shengjie
    • Affiliation: School of Medicine, Curriculum in Bioinformatics and Computational Biology
  • Zhang, Baqun
    • Other Affiliation: School of Statistics, Renmin University of China, Beijing 100872, China
  • Wu, Cong
    • Other Affiliation: College of Veterinary Medicine, Nanjing Agricultural University, Nanjing 210095, Jiangsu, China
  • Jin, Fulai
    • Other Affiliation: Department of Genetics and Genome Sciences, Case Western Reserve University, Cleveland, OH 44106, USA
  • Yue, Feng
    • Other Affiliation: Department of Biochemistry and Molecular Biology, Institute for Personalized Medicine, Pennsylvania State University College of Medicine, Hershey, PA 17033, USA
  • Li, Yun
    • Affiliation: College of Arts and Sciences, Department of Computer Science, Gillings School of Global Public Health, Department of Biostatistics
  • Hu, Ming
    • Other Affiliation: Division of Biostatistics, Department of Population Health, New York University School of Medicine, New York, NY 10016, USA
Abstract
  • Abstract Background Genome-wide association studies (GWAS) have identified thousands of genetic variants associated with complex traits and diseases. However, most of them are located in the non-protein coding regions, and therefore it is challenging to hypothesize the functions of these non-coding GWAS variants. Recent large efforts such as the ENCODE and Roadmap Epigenomics projects have predicted a large number of regulatory elements. However, the target genes of these regulatory elements remain largely unknown. Chromatin conformation capture based technologies such as Hi-C can directly measure the chromatin interactions and have generated an increasingly comprehensive catalog of the interactome between the distal regulatory elements and their potential target genes. Leveraging such information revealed by Hi-C holds the promise of elucidating the functions of genetic variants in human diseases. Results In this work, we present HiView, the first integrative genome browser to leverage Hi-C results for the interpretation of GWAS variants. HiView is able to display Hi-C data and statistical evidence for chromatin interactions in genomic regions surrounding any given GWAS variant, enabling straightforward visualization and interpretation. Conclusions We believe that as the first GWAS variants-centered Hi-C genome browser, HiView is a useful tool guiding post-GWAS functional genomics studies. HiView is freely accessible at: http://www.unc.edu/~yunmli/HiView .
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  • Article
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  • In Copyright
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  • Xu et al.
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  • English
Bibliographic citation
  • BMC Research Notes. 2016 Mar 11;9(1):159
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  • BioMed Central

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