SECA: SNP effect concordance analysis using genome-wide association summary results
SECA: SNP effect concordance analysis using genome-wide association summary results
复制标题
DOI：
10.1093/bioinformatics/btu171
复制
发表时间：
2014-07-15
期刊：
影响因子：
5.8
通讯作者：
Nyholt, Dale R.
中科院分区：
文献类型：
--
作者：
Nyholt, Dale R.
.总结：基因组学时代提供了在测量的基因型水平上评估表型之间的遗传重叠的机会;然而，目前的方法需要在一对GWA样品中的一个或两个中的个体水平的全基因组关联（GWA）单核苷酸多态性（SNP）基因型数据。为了促进多效性效应的发现和研究两种表型的遗传重叠，我开发了一个用户友好的基于Web的应用程序，称为SECA，使用GWA总结结果进行SNP效应一致性分析。该方法使用来自精神病基因组学联盟的公开可用的汇总数据进行验证。
.Summary: The genomics era provides opportunities to assess the genetic overlap across phenotypes at the measured genotype level; however, current approaches require individual-level genome-wide association (GWA) single nucleotide polymorphism (SNP) genotype data in one or both of a pair of GWA samples. To facilitate the discovery of pleiotropic effects and examine genetic overlap across two phenotypes, I have developed a user-friendly web-based application called SECA to perform SNP effect concordance analysis using GWA summary results. The method is validated using publicly available summary data from the Psychiatric Genomics Consortium.