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BMC Res Notes. 2016 Mar 11;9:159. doi: 10.1186/s13104-016-1947-0.

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

Xu Z1,2,3, Zhang G2,4, Duan Q2, Chai S4, Zhang B5, Wu C6, Jin F7, Yue F8, Li Y9,10,11, Hu M12.

Author information

1
Department of Biostatistics, University of North Carolina, Chapel Hill, NC, 27599, USA.
2
Department of Genetics, University of North Carolina, Chapel Hill, NC, 27599, USA.
3
Department of Computer Science, University of North Carolina, Chapel Hill, NC, 27599, USA.
4
Curriculum in Bioinformatics and Computational Biology, University of North Carolina, Chapel Hill, NC, 27599, USA.
5
School of Statistics, Renmin University of China, Beijing, 100872, China.
6
College of Veterinary Medicine, Nanjing Agricultural University, Nanjing, 210095, Jiangsu, China.
7
Department of Genetics and Genome Sciences, Case Western Reserve University, Cleveland, OH, 44106, USA.
8
Department of Biochemistry and Molecular Biology, Institute for Personalized Medicine, Pennsylvania State University College of Medicine, Hershey, PA, 17033, USA.
9
Department of Biostatistics, University of North Carolina, Chapel Hill, NC, 27599, USA. yunli@med.unc.edu.
10
Department of Genetics, University of North Carolina, Chapel Hill, NC, 27599, USA. yunli@med.unc.edu.
11
Department of Computer Science, University of North Carolina, Chapel Hill, NC, 27599, USA. yunli@med.unc.edu.
12
Division of Biostatistics, Department of Population Health, New York University School of Medicine, New York, NY, 10016, USA. ming.hu@nyumc.org.

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 .

KEYWORDS:

GWAS variants; Hi-C data; Integrative genome browser

PMID:
26969411
PMCID:
PMC4788823
DOI:
10.1186/s13104-016-1947-0
[Indexed for MEDLINE]
Free PMC Article

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