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
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