Finnish Microarray and Sequencing Centre, Turku Centre for Biotechnology, University of Turku, Turku, Finland.
Nucleic Acids Res. 2012 Jul;40(Web Server issue):W628-32. doi: 10.1093/nar/gks550. Epub 2012 Jun 11.
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.
全基因组关联研究(GWAS)已经发现了许多与常见疾病和数量性状相关的基因座。然而,大多数 GWAS 并未研究基因-基因相互作用(上位性),而这些相互作用在复杂性状遗传中可能很重要。分析 GWAS 中的上位性的主要挑战是分析数十亿个 SNP 组合的巨大计算需求。最近已经开发了几种方法来解决这个问题,其中一些使用配备特定图形处理单元的计算机,大多数仅限于二进制疾病性状,并且都不适合在最广泛使用的操作系统上进行通用使用。我们开发了 BiForce 工具箱来满足对高通量分析 GWAS 中数量性状和疾病性状的成对上位性的需求,这些分析适用于所有常用的计算机系统。BiForce 工具箱是一个独立的 Java 程序,它将位运算与多线程并行化相结合,从而允许通过图形用户界面或命令行快速进行全基因组扫描。此外,BiForce 工具箱还包含了对具有显著边缘效应的 SNP 相互作用的额外测试,这可能会增加检测上位性的能力。BiForce 工具箱易于使用,并已应用于多个大型 GWAS 数据集的上位性研究中,确定了有趣的相互作用信号和途径。