Ultrahigh-dimensional variable selection method for whole-genome gene-gene interaction analysis.
Ultrahigh-dimensional variable selection method for whole-genome gene-gene interaction analysis.
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DOI：
10.1186/1471-2105-13-72
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发表时间：
2012-05-03
影响因子：
3
通讯作者：
Tamiya G
中科院分区：
文献类型：
--
作者：
Ueki M;Tamiya G
利用单核苷酸多态性（SNPs）进行全基因组基因-基因相互作用分析是鉴定人类复杂疾病易感性遗传成分的一种有吸引力的方法。然而，在普通全基因组关联研究（common genome-wide association study， GWAS）中，SNP-SNP对的个体假设检验由于相关结构复杂，难以确定总体p值，即多重检验问题，导致不可接受的假阴性结果。大量的SNP-SNP对大于样本量，即所谓的大p小n问题，妨碍了使用多元回归进行同时分析。因此需要克服上述问题的方法。我们采用一种最新的超高维变量选择方法，称为确定独立筛选（SIS），通过将它们作为逻辑回归中的预测变量来适当处理大量SNP-SNP相互作用。我们提出了利用有前途的虚拟编码方法和遵循SIS方法中的变量选择程序的排序策略，该方法对基因-基因相互作用分析进行了适当的修改。我们还使用具有成本效益的GPGPU（图形处理单元通用计算）技术在软件程序EPISIS中实现了这些程序。EPISIS可以在几个小时内完成标准GWAS数据集中SNP-SNP相互作用的穷举搜索。该方法在仿真实验和实际WTCCC （Wellcome Trust Case-control Consortium）数据中均取得了良好的效果。该方法基于机器学习原理，对基因-基因相互作用的各种模式进行了强大而灵活的全基因组搜索。
Genome-wide gene-gene interaction analysis using single nucleotide polymorphisms (SNPs) is an attractive way for identification of genetic components that confers susceptibility of human complex diseases. Individual hypothesis testing for SNP-SNP pairs as in common genome-wide association study (GWAS) however involves difficulty in setting overall p-value due to complicated correlation structure, namely, the multiple testing problem that causes unacceptable false negative results. A large number of SNP-SNP pairs than sample size, so-called the large p small n problem, precludes simultaneous analysis using multiple regression. The method that overcomes above issues is thus needed. We adopt an up-to-date method for ultrahigh-dimensional variable selection termed the sure independence screening (SIS) for appropriate handling of numerous number of SNP-SNP interactions by including them as predictor variables in logistic regression. We propose ranking strategy using promising dummy coding methods and following variable selection procedure in the SIS method suitably modified for gene-gene interaction analysis. We also implemented the procedures in a software program, EPISIS, using the cost-effective GPGPU (General-purpose computing on graphics processing units) technology. EPISIS can complete exhaustive search for SNP-SNP interactions in standard GWAS dataset within several hours. The proposed method works successfully in simulation experiments and in application to real WTCCC (Wellcome Trust Case–control Consortium) data. Based on the machine-learning principle, the proposed method gives powerful and flexible genome-wide search for various patterns of gene-gene interaction.
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DOI：
10.1038/nrg2579
发表时间：
2009-06
期刊：
Nature reviews. Genetics
影响因子：
--
作者：
Cordell HJ
通讯作者：
Cordell HJ
影响因子：
5.8
作者：
He, Qianchuan;Lin, Dan-Yu
通讯作者：
Lin, Dan-Yu
影响因子：
4.5
作者：
Libioulle C;Louis E;Hansoul S;Sandor C;Farnir F;Franchimont D;Vermeire S;Dewit O;de Vos M;Dixon A;Demarche B;Gut I;Heath S;Foglio M;Liang L;Laukens D;Mni M;Zelenika D;Van Gossum A;Rutgeerts P;Belaiche J;Lathrop M;Georges M
通讯作者：
Georges M
影响因子：
4.5
作者：
Fan, JQ;Peng, H
通讯作者：
Peng, H
影响因子：
4.5
作者：
Hoggart, Clive J.;Whittaker, John C.;De Iorio, Maria;Balding, David J.
通讯作者：
Balding, David J.