GIGSEA: Genotype imputed gene set enrichment analysis using GWAS summary level data

Shijia Zhu, Tongqi Qian, Yujin Hoshida, Yuan Shen, Jing Yu, Ke Hao

Research output: Contribution to journalArticle

2 Scopus citations

Abstract

Summary level data of GWAS becomes increasingly important in post-GWAS data mining. Here, we present GIGSEA (Genotype Imputed Gene Set Enrichment Analysis), 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 implementation GIGSEA is implemented in R, and freely available at www.github.com/zhushijia/GIGSEA.

Original languageEnglish (US)
Pages (from-to)160-163
Number of pages4
JournalBioinformatics
Volume35
Issue number1
DOIs
StatePublished - Jan 1 2019

ASJC Scopus subject areas

  • Statistics and Probability
  • Biochemistry
  • Molecular Biology
  • Computer Science Applications
  • Computational Theory and Mathematics
  • Computational Mathematics

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