Institute of Bioinformatics, Zhejiang University, Hangzhou, China.
PLoS One. 2013 Apr 23;8(4):e61943. doi: 10.1371/journal.pone.0061943. Print 2013.
Although genome-wide association studies (GWAS) have identified a significant number of single-nucleotide polymorphisms (SNPs) associated with many complex human traits, the susceptibility loci identified so far can explain only a small fraction of the genetic risk. Among other possible explanations, the lack of a comprehensive examination of gene-gene interaction (G×G) is often considered a source of the missing heritability. Previously, we reported a model-free Generalized Multifactor Dimensionality Reduction (GMDR) approach for detecting G×G in both dichotomous and quantitative phenotypes. However, the computational burden and less efficient implementation of the original programs make them impossible to use for GWAS. In this study, we developed a graphics processing unit (GPU)-based GMDR program (named GWAS-GPU), which is able not only to analyze GWAS data but also to run much faster than the earlier version of the GMDR program. As a demonstration of the program, we used the GMDR-GPU software to analyze a publicly available GWAS dataset on type 2 diabetes (T2D) from the Wellcome Trust Case Control Consortium. Through an exhaustive search of pair-wise interactions and a selected search of three- to five-way interactions conditioned on significant pair-wise results, we identified 24 core SNPs in six genes (FTO: rs9939973, rs9940128, rs9922047, rs1121980, rs9939609, rs9930506; TSPAN8: rs1495377; TCF7L2: rs4074720, rs7901695, rs4506565, rs4132670, rs10787472, rs11196205, rs10885409, rs11196208; L3MBTL3: rs10485400, rs4897366; CELF4: rs2852373, rs608489; RUNX1: rs445984, rs1040328, rs990074, rs2223046, rs2834970) that appear to be important for T2D. Of these core SNPs, 11 in FTO, TSPAN8, and TCF7L2 have been reported to be associated with T2D, obesity, or both, providing an independent replication of previously reported SNPs. Importantly, we identified three new susceptibility genes; i.e., L3MBTL3, CELF4, and RUNX1, for T2D, a finding that warrants further investigation with independent samples.
尽管全基因组关联研究（GWAS）已经确定了许多与多种复杂人类特征相关的单核苷酸多态性（SNP），但迄今为止发现的易感基因座只能解释遗传风险的一小部分。在其他可能的解释中，缺乏对基因-基因相互作用（G×G）的全面研究通常被认为是遗传力缺失的一个原因。以前，我们报道了一种无模型广义多因子降维（GMDR）方法，用于检测二分类和定量表型中的 G×G。然而，原始程序的计算负担和效率较低，使得它们无法用于 GWAS。在这项研究中，我们开发了一个基于图形处理单元（GPU）的 GMDR 程序（命名为 GWAS-GPU），它不仅能够分析 GWAS 数据，而且运行速度比早期版本的 GMDR 程序快得多。作为该程序的演示，我们使用 GMDR-GPU 软件分析了来自惠康信托基金会病例对照联盟的公开可用的 2 型糖尿病（T2D）GWAS 数据集。通过对两两相互作用的详尽搜索和对有显著两两结果的三到五重相互作用的选择性搜索，我们在六个基因中确定了 24 个核心 SNP（FTO：rs9939973、rs9940128、rs9922047、rs1121980、rs9939609、rs9930506；TSPAN8：rs1495377；TCF7L2：rs4074720、rs7901695、rs4506565、rs4132670、rs10787472、rs11196205、rs10885409、rs11196208；L3MBTL3：rs10485400、rs4897366；CELF4：rs2852373、rs608489；RUNX1：rs445984、rs1040328、rs990074、rs2223046、rs2834970），这些 SNP 似乎与 T2D 有关。在这些核心 SNP 中，FTO、TSPAN8 和 TCF7L2 中的 11 个 SNP 已被报道与 T2D、肥胖或两者都有关，这为之前报道的 SNP 提供了独立的验证。重要的是，我们鉴定出三个新的易感基因，即 L3MBTL3、CELF4 和 RUNX1，用于 T2D，这一发现值得进一步用独立样本进行研究。
