GIGSEA: genotype imputed gene set enrichment analysis using GWAS summary level data.
GIGSEA: genotype imputed gene set enrichment analysis using GWAS summary level data.
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GIGSEA：使用 GWAS 汇总水平数据进行基因型估算基因集富集分析。
DOI：
10.1093/bioinformatics/bty529
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发表时间：
2019
期刊：
影响因子：
5.8
通讯作者：
Hao Ke
中科院分区：
文献类型：
--
作者：
Zhu Shijia;Qian Tongqi;Hoshida Yujin;Shen Yuan;Yu Jing;Hao Ke
摘要 GWAS 的摘要级数据在后 GWAS 数据挖掘中变得越来越重要。在这里，我们提出了 GIGSEA（GenotypeImpulatedGeneSetEnrichmentAnalysis），这是一种使用 GWAS 汇总统计和 eQTL 来推断差异基因表达并询问性状相关 SNP 基因集富集的新方法。通过结合疾病相关组织的经验 eQTL，GIGSEA 自然地考虑了基因大小、基因边界、SNP 远端调控和多标记调控等因素。使用加权线性回归模型进行富集测试，适当调整不同基因集中的插补精度、模型不完整性和冗余。富集的显着性水平通过排列测试来评估，其中采用矩阵运算来显着提高计算速度。 GIGSEA 具有适当的 I 类错误率，并在真实数据集上发现合理的生物学发现。可用性和实现GIGSEA 在 R 中实现，可在 www.github.com/zhushijia/GIGSEA 上免费获取。补充信息补充数据可在 Bioinformaticsonline 上获得。
SummarySummary level data of GWAS becomes increasingly important in post-GWAS data mining. Here, we present GIGSEA (GenotypeImputedGeneSetEnrichmentAnalysis), a novel method that uses GWAS summary statistics and eQTL to infer differential gene expression and interrogate gene set enrichment for the trait-associated SNPs. By incorporating empirical eQTL of the disease relevant tissue, GIGSEA naturally accounts for factors such as gene size, gene boundary, SNP distal regulation and multiple-marker regulation. The weighted linear regression model was used to perform the enrichment test, properly adjusting for imputation accuracy, model incompleteness and redundancy in different gene sets. The significance level of enrichment is assessed by the permutation test, where matrix operation was employed to dramatically improve computation speed. GIGSEA has appropriate type I error rates, and discovers the plausible biological findings on the real data set.Availability and implementationGIGSEA is implemented in R, and freely available at www.github.com/zhushijia/GIGSEA.Supplementary informationSupplementary data are available atBioinformaticsonline.