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 language | English (US) |
|---|---|
| Pages (from-to) | 160-163 |
| Number of pages | 4 |
| Journal | Bioinformatics |
| Volume | 35 |
| Issue number | 1 |
| DOIs | |
| State | Published - Jan 1 2019 |
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ASJC Scopus subject areas
- Statistics and Probability
- Biochemistry
- Molecular Biology
- Computer Science Applications
- Computational Theory and Mathematics
- Computational Mathematics
Cite this
GIGSEA : Genotype imputed gene set enrichment analysis using GWAS summary level data. / Zhu, Shijia; Qian, Tongqi; Hoshida, Yujin; Shen, Yuan; Yu, Jing; Hao, Ke.
In: Bioinformatics, Vol. 35, No. 1, 01.01.2019, p. 160-163.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - GIGSEA
T2 - Genotype imputed gene set enrichment analysis using GWAS summary level data
AU - Zhu, Shijia
AU - Qian, Tongqi
AU - Hoshida, Yujin
AU - Shen, Yuan
AU - Yu, Jing
AU - Hao, Ke
PY - 2019/1/1
Y1 - 2019/1/1
N2 - 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.
AB - 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.
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U2 - 10.1093/bioinformatics/bty529
DO - 10.1093/bioinformatics/bty529
M3 - Article
C2 - 30010968
AN - SCOPUS:85058750129
VL - 35
SP - 160
EP - 163
JO - Bioinformatics
JF - Bioinformatics
SN - 1367-4803
IS - 1
ER -