BiForce Toolbox: powerful high-throughput computational analysis of gene-gene interactions in genome-wide association studies

被引:23
|
作者
Gyenesei, Attila [2 ,3 ]
Moody, Jonathan [1 ]
Laiho, Asta [2 ,3 ]
Semple, Colin A. M. [1 ]
Haley, Chris S. [1 ]
Wei, Wen-Hua [1 ]
机构
[1] Univ Edinburgh, Western Gen Hosp, MRC Human Genet Unit, MRC Inst Genet & Mol Med, Edinburgh EH4 2XU, Midlothian, Scotland
[2] Univ Turku, Turku Ctr Biotechnol, Finnish Microarray & Sequencing Ctr, Turku 20520, Finland
[3] Abo Akad Univ, FIN-20520 Turku, Finland
基金
英国医学研究理事会; 英国生物技术与生命科学研究理事会;
关键词
MISSING HERITABILITY; SUSCEPTIBILITY; POPULATION; EPISTASIS; DISEASE; ERAP1;
D O I
10.1093/nar/gks550
中图分类号
Q5 [生物化学]; Q7 [分子生物学];
学科分类号
071010 ; 081704 ;
摘要
Genome-wide association studies (GWAS) have discovered many loci associated with common disease and quantitative traits. However, most GWAS have not studied the gene-gene interactions (epistasis) that could be important in complex trait genetics. A major challenge in analysing epistasis in GWAS is the enormous computational demands of analysing billions of SNP combinations. Several methods have been developed recently to address this, some using computers equipped with particular graphical processing units, most restricted to binary disease traits and all poorly suited to general usage on the most widely used operating systems. We have developed the BiForce Toolbox to address the demand for high-throughput analysis of pairwise epistasis in GWAS of quantitative and disease traits across all commonly used computer systems. BiForce Toolbox is a stand-alone Java program that integrates bitwise computing with multithreaded parallelization and thus allows rapid full pairwise genome scans via a graphical user interface or the command line. Furthermore, BiForce Toolbox incorporates additional tests of interactions involving SNPs with significant marginal effects, potentially increasing the power of detection of epistasis. BiForce Toolbox is easy to use and has been applied in multiple studies of epistasis in large GWAS data sets, identifying interesting interaction signals and pathways.
引用
收藏
页码:W628 / W632
页数:5
相关论文
共 50 条
  • [41] A Novel Test for Gene-Ancestry Interactions in Genome-Wide Association Data
    Davies, Joanna L.
    Cazier, Jean-Baptiste
    Dunlop, Malcolm G.
    Houlston, Richard S.
    Tomlinson, Ian P.
    Holmes, Chris C.
    PLOS ONE, 2012, 7 (12):
  • [42] Gene and pathway-based second-wave analysis of genome-wide association studies
    Peng, Gang
    Luo, Li
    Siu, Hoicheong
    Zhu, Yun
    Hu, Pengfei
    Hong, Shengjun
    Zhao, Jinying
    Zhou, Xiaodong
    Reveille, John D.
    Jin, Li
    Amos, Christopher I.
    Xiong, Momiao
    EUROPEAN JOURNAL OF HUMAN GENETICS, 2010, 18 (01) : 111 - 117
  • [43] Pathway-based analysis using reduced gene subsets in genome-wide association studies
    Zhao, Jingyuan
    Gupta, Simone
    Seielstad, Mark
    Liu, Jianjun
    Thalamuthu, Anbupalam
    BMC BIOINFORMATICS, 2011, 12
  • [44] An overview of SNP interactions in genome-wide association studies
    Li, Pei
    Guo, Maozu
    Wang, Chunyu
    Liu, Xiaoyan
    Zou, Quan
    BRIEFINGS IN FUNCTIONAL GENOMICS, 2015, 14 (02) : 143 - 155
  • [45] Using Gene Expression to Improve the Power of Genome-Wide Association Analysis
    Ho, Yen-Yi
    Baechler, Emily C.
    Ortmann, Ward
    Behrens, Timothy W.
    Graham, Robert R.
    Bhangale, Tushar R.
    Pan, Wei
    HUMAN HEREDITY, 2014, 78 (02) : 94 - 103
  • [46] Screen and Clean: A Tool for Identifying Interactions in Genome-Wide Association Studies
    Wu, Jing
    Devlin, Bernie
    Ringquist, Steven
    Trucco, Massimo
    Roeder, Kathryn
    GENETIC EPIDEMIOLOGY, 2010, 34 (03) : 275 - 285
  • [47] Discovering genetic interactions bridging pathways in genome-wide association studies
    Fang, Gang
    Wang, Wen
    Paunic, Vanja
    Heydari, Hamed
    Costanzo, Michael
    Liu, Xiaoye
    Liu, Xiaotong
    VanderSluis, Benjamin
    Oately, Benjamin
    Steinbach, Michael
    Van Ness, Brian
    Schadt, Eric E.
    Pankratz, Nathan D.
    Boone, Charles
    Kumar, Vipin
    Myers, Chad L.
    NATURE COMMUNICATIONS, 2019, 10 (1)
  • [48] Gene expression levels as endophenotypes in genome-wide association studies of Alzheimer disease
    Zou, F.
    Carrasquillo, M. M.
    Pankratz, V. S.
    Belbin, O.
    Morgan, K.
    Allen, M.
    Wilcox, S. L.
    Ma, L.
    Walker, L. P.
    Kouri, N.
    Burgess, J. D.
    Younkin, L. H.
    Younkin, Samuel G.
    Younkin, C. S.
    Bisceglio, G. D.
    Crook, J. E.
    Dickson, D. W.
    Petersen, R. C.
    Graff-Radford, N.
    Younkin, Steven G.
    Ertekin-Taner, N.
    NEUROLOGY, 2010, 74 (06) : 480 - 486
  • [49] Association Signals Unveiled by a Comprehensive Gene Set Enrichment Analysis of Dental Caries Genome-Wide Association Studies
    Wang, Quan
    Jia, Peilin
    Cuenco, Karen T.
    Zeng, Zhen
    Feingold, Eleanor
    Marazita, Mary L.
    Wang, Lily
    Zhao, Zhongming
    PLOS ONE, 2013, 8 (08):
  • [50] GWAS-GMDR: a program package for genome-wide scan of gene-gene interactions with covariate adjustment based on multifactor dimensionality reduction
    Kwon, Min-Seok
    Kim, Kyunga
    Lee, Sungyoung
    Chung, Wonil
    Yi, Sung-Gon
    Namkung, Junghyun
    Park, Taesung
    2011 IEEE INTERNATIONAL CONFERENCE ON BIOINFORMATICS AND BIOMEDICINE WORKSHOPS, 2011, : 703 - 707