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Scientific reports v.6, 2016년, pp.37951 -    SCI SCIE
본 등재정보는 저널의 등재정보를 참고하여 보여주는 베타서비스로 정확한 논문의 등재여부는 등재기관에 확인하시기 바랍니다.

EWAS: epigenome-wide association studies software 1.0 – identifying the association between combinations of methylation levels and diseases

Xu, Jing (College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150086, China ) ; Liu, Di (Training Center for Students Innovation and Entrepreneurship Education of Harbin Medical University, Harbin, China ) ; Zhao, Linna (College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150086, China ) ; Li, Ying (Training Center for Students Innovation and Entrepreneurship Education of Harbin Medical University, Harbin, China ) ; Wang, Zhaoyang (College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150086, China ) ; Chen, Yang (Training Center for Students Innovation and Entrepreneurship Education of Harbin Medical University, Harbin, China ) ; Lei, Changgui (College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150086, China ) ; Gao, Lin (Training Center for Students Innovation and Entrepreneurship Education of Harbin Medical University, Harbin, China ) ; Kong, Fanwu (College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150086, China ) ; Yuan, Lijun (Training Center for Student ) ; Jiang, Yongshuai ;
  • 초록  

    Similar to the SNP (single nucleotide polymorphism) data, there is non-random association of the DNA methylation level (we call it methylation disequilibrium, MD) between neighboring methylation loci. For the case-control study of complex diseases, it is important to identify the association between methylation levels combination types (we call it methylecomtype) and diseases/phenotypes. We extended the classical framework of SNP haplotype-based association study in population genetics to DNA methylation level data, and developed a software EWAS to identify the disease-related methylecomtypes. EWAS can provide the following basic functions: (1) calculating the DNA methylation disequilibrium coefficient between two CpG loci; (2) identifying the MD blocks across the whole genome; (3) carrying out case-control association study of methylecomtypes and identifying the disease-related methylecomtypes. For a DNA methylation level data set including 689 samples (354 cases and 335 controls) and 473864 CpG loci, it takes only about 25 min to complete the full scan. EWAS v1.0 can rapidly identify the association between combinations of methylation levels (methylecomtypes) and diseases. EWAS v1.0 is freely available at: http://www.ewas.org.cn or http://www.bioapp.org/ewas.


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