Manhattan plus plus : displaying genome-wide association summary statistics with multiple annotation layers

被引:6
|
作者
Grace, Christopher [1 ,2 ]
Farrall, Martin [1 ,2 ]
Watkins, Hugh [1 ,2 ]
Goel, Anuj [1 ,2 ]
机构
[1] Univ Oxford, John Radcliffe Hosp, Radcliffe Dept Med, Div Cardiovasc Med, Oxford OX3 9DU, England
[2] Univ Oxford, Wellcome Ctr Human Genet, Oxford OX3 7BN, England
基金
英国惠康基金;
关键词
Manhattan plot; GWAS; Meta-analysis; R; Software; CRAN; GENETIC RISK; LOCI;
D O I
10.1186/s12859-019-3201-y
中图分类号
Q5 [生物化学];
学科分类号
071010 ; 081704 ;
摘要
Background: Over the last 10 years, there have been over 3300 genome-wide association studies (GWAS). Almost every GWAS study provides a Manhattan plot either as a main figure or in the supplement. Several software packages can generate a Manhattan plot, but they are all limited in the extent to which they can annotate genenames, allele frequencies, and variants having high impact on gene function or provide any other added information or flexibility. Furthermore, in a conventional Manhattan plot, there is no way of distinguishing a locus identified due to a single variant with very significant p-value from a locus with multiple variants which appear to be in a haplotype block having very similar p-values. Results: Here we present a software tool written in R, which generates a transposed Manhattan plot along with additional features like variant consequence and minor allele frequency to annotate the plot and addresses these limitations. The software also gives flexibility on how and where the user wants to display the annotations. The software can be downloaded from CRAN repository and also from the GitHub project page. Conclusions: We present a major step up to the existing conventional Manhattan plot generation tools. We hope this form of display along with the added annotations will bring more insight to the reader from this new Manhattan++ plot.
引用
收藏
页数:5
相关论文
共 50 条
  • [1] Manhattan++: displaying genome-wide association summary statistics with multiple annotation layers
    Christopher Grace
    Martin Farrall
    Hugh Watkins
    Anuj Goel
    BMC Bioinformatics, 20
  • [2] Partitioning heritability by functional annotation using genome-wide association summary statistics
    Hilary K Finucane
    Brendan Bulik-Sullivan
    Alexander Gusev
    Gosia Trynka
    Yakir Reshef
    Po-Ru Loh
    Verneri Anttila
    Han Xu
    Chongzhi Zang
    Kyle Farh
    Stephan Ripke
    Felix R Day
    Shaun Purcell
    Eli Stahl
    Sara Lindstrom
    John R B Perry
    Yukinori Okada
    Soumya Raychaudhuri
    Mark J Daly
    Nick Patterson
    Benjamin M Neale
    Alkes L Price
    Nature Genetics, 2015, 47 : 1228 - 1235
  • [3] Partitioning heritability by functional annotation using genome-wide association summary statistics
    Finucane, Hilary K.
    Bulik-Sullivan, Brendan
    Gusev, Alexander
    Trynka, Gosia
    Reshef, Yakir
    Loh, Po-Ru
    Anttila, Verneri
    Xu, Han
    Zang, Chongzhi
    Farh, Kyle
    Ripke, Stephan
    Day, Felix R.
    Purcell, Shaun
    Stahl, Eli
    Lindstrom, Sara
    Perry, John R. B.
    Okada, Yukinori
    Raychaudhuri, Soumya
    Daly, Mark J.
    Patterson, Nick
    Neale, Benjamin M.
    Price, Alkes L.
    NATURE GENETICS, 2015, 47 (11) : 1228 - +
  • [4] Multiple phenotype association tests using summary statistics in genome-wide association studies
    Liu, Zhonghua
    Lin, Xihong
    BIOMETRICS, 2018, 74 (01) : 165 - 175
  • [5] Comparison of Multiple Phenotype Association Tests Using Summary Statistics in Genome-wide Association Studies
    Sitlani, Colleen M.
    Baldassari, Antoine R.
    Highland, Heather M.
    Hodonsky, Chani J.
    McKnight, Barbara
    Avery, Christy L.
    GENETIC EPIDEMIOLOGY, 2019, 43 (07) : 909 - 910
  • [6] Comparison of adaptive multiple phenotype association tests using summary statistics in genome-wide association studies
    Sitlani, Colleen M.
    Baldassari, Antoine R.
    Highland, Heather M.
    Hodonsky, Chani J.
    McKnight, Barbara
    Avery, Christy L.
    HUMAN MOLECULAR GENETICS, 2021, 30 (15) : 1371 - 1383
  • [7] UPC plus plus for Bioinformatics: A Case Study Using Genome-Wide Association Studies
    Kaessens, Jan C.
    Gonzalez-Dominguez, Jorge
    Wienbrandt, Lars
    Schmidt, Bertil
    2014 IEEE INTERNATIONAL CONFERENCE ON CLUSTER COMPUTING (CLUSTER), 2014, : 248 - 256
  • [8] A Unifying Framework for Imputing Summary Statistics in Genome-Wide Association Studies
    Wu, Yue
    Eskin, Eleazar
    Sankararaman, Sriram
    JOURNAL OF COMPUTATIONAL BIOLOGY, 2020, 27 (03) : 418 - 428
  • [9] Adjustment for covariates using summary statistics of genome-wide association studies
    Wang, Tao
    Xue, Xiaonan
    Xie, Xianhong
    Ye, Kenny
    Zhu, Xiaofeng
    Elston, Robert C.
    GENETIC EPIDEMIOLOGY, 2018, 42 (08) : 812 - 825
  • [10] A comprehensive comparison of multilocus association methods with summary statistics in genome-wide association studies
    Shao, Zhonghe
    Wang, Ting
    Qiao, Jiahao
    Zhang, Yuchen
    Huang, Shuiping
    Zeng, Ping
    BMC BIOINFORMATICS, 2022, 23 (01)