Literature DB >> 27760738

The framework for population epigenetic study.

Linna Zhao, Di Liu, Jing Xu, Zhaoyang Wang, Yang Chen, Changgui Lei, Ying Li, Guiyou Liu, Yongshuai Jiang.   

Abstract

At present, understanding of DNA methylation at the population level is still limited. Here, we first extended the classical framework of population genetics, such as single nucleotide polymorphism allele frequency, linkage disequilibrium (LD), LD block and haplotype, to epigenetics. Then, as an example, we compared the DNA methylation disequilibrium (MD) maps between HapMap CEU (Caucasian residents of European ancestry from Utah) population and YRI (Yoruba people from Ibadan) population (lymphoblastoid cell lines). We analyzed the differences and similarities between CEU and YRI from the following aspects: SMP (single methylation polymorphism) allele frequency, SMP allele association, MD, MD block and methylation haplotype (meplotype) frequency. The results showed that CEU and YRI had similar distribution of SMP allele frequency, and shared many MD block region. We believe that the framework of population genetics can be used in the population epigenetics. The population epigenetic framework also has potential prospects in the study of complex diseases, such as epigenome-wide association study.
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Entities:  

Keywords:  DNA methylation; SMP; epigenome-wide association study; population epigenetics; population genetics; single methylation polymorphism

Mesh:

Year:  2018        PMID: 27760738     DOI: 10.1093/bib/bbw098

Source DB:  PubMed          Journal:  Brief Bioinform        ISSN: 1467-5463            Impact factor:   11.622


  6 in total

1.  EWAS: epigenome-wide association study software 2.0.

Authors:  Jing Xu; Linna Zhao; Di Liu; Simeng Hu; Xiuling Song; Jin Li; Hongchao Lv; Lian Duan; Mingming Zhang; Qinghua Jiang; Guiyou Liu; Shuilin Jin; Mingzhi Liao; Meng Zhang; Rennan Feng; Fanwu Kong; Liangde Xu; Yongshuai Jiang
Journal:  Bioinformatics       Date:  2018-08-01       Impact factor: 6.937

2.  Leveraging DNA-Methylation Quantitative-Trait Loci to Characterize the Relationship between Methylomic Variation, Gene Expression, and Complex Traits.

Authors:  Eilis Hannon; Tyler J Gorrie-Stone; Melissa C Smart; Joe Burrage; Amanda Hughes; Yanchun Bao; Meena Kumari; Leonard C Schalkwyk; Jonathan Mill
Journal:  Am J Hum Genet       Date:  2018-10-25       Impact factor: 11.025

3.  EWASdb: epigenome-wide association study database.

Authors:  Di Liu; Linna Zhao; Zhaoyang Wang; Xu Zhou; Xiuzhao Fan; Yong Li; Jing Xu; Simeng Hu; Miaomiao Niu; Xiuling Song; Ying Li; Lijiao Zuo; Changgui Lei; Meng Zhang; Guoping Tang; Min Huang; Nan Zhang; Lian Duan; Hongchao Lv; Mingming Zhang; Jin Li; Liangde Xu; Fanwu Kong; Rennan Feng; Yongshuai Jiang
Journal:  Nucleic Acids Res       Date:  2019-01-08       Impact factor: 16.971

4.  Leveraging biological and statistical covariates improves the detection power in epigenome-wide association testing.

Authors:  Jinyan Huang; Ling Bai; Bowen Cui; Liang Wu; Liwen Wang; Zhiyin An; Shulin Ruan; Yue Yu; Xianyang Zhang; Jun Chen
Journal:  Genome Biol       Date:  2020-04-06       Impact factor: 13.583

5.  Discrimination between human populations using a small number of differentially methylated CpG sites: a preliminary study using lymphoblastoid cell lines and peripheral blood samples of European and Chinese origin.

Authors:  Patrycja Daca-Roszak; Roman Jaksik; Julia Paczkowska; Michał Witt; Ewa Ziętkiewicz
Journal:  BMC Genomics       Date:  2020-10-12       Impact factor: 3.969

6.  A new approach to decode DNA methylome and genomic variants simultaneously from double strand bisulfite sequencing.

Authors:  Jialong Liang; Kun Zhang; Jie Yang; Xianfeng Li; Qinglan Li; Yan Wang; Wanshi Cai; Huajing Teng; Zhongsheng Sun
Journal:  Brief Bioinform       Date:  2021-11-05       Impact factor: 11.622

  6 in total

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