Related Experiment Video
Updated: Feb 7, 2026
09:35
A Protocol for Using Gene Set Enrichment Analysis to Identify the Appropriate Animal Model for Translational Research
Published on: August 16, 2017
18.3K
GIGSEA: genotype imputed gene set enrichment analysis using GWAS summary level data
Shijia Zhu1, Tongqi Qian1, Yujin Hoshida2
1Department of Genetics and Genomic Sciences and Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
Bioinformatics (Oxford, England)
|July 17, 2018
Summary
Genotype Imputed Gene Set Enrichment Analysis (GIGSEA) infers gene expression from GWAS data and eQTLs to identify trait-associated gene sets. This method improves post-GWAS analysis by accounting for complex genetic regulation and providing robust biological insights.
Area of Science:
- Genomics
- Bioinformatics
- Statistical Genetics
Background:
- Genome-Wide Association Studies (GWAS) generate large datasets crucial for post-GWAS data mining.
- Interpreting GWAS results requires methods that can link single nucleotide polymorphisms (SNPs) to gene expression and biological pathways.
Purpose of the Study:
- To introduce GIGSEA (Genotype Imputed Gene Set Enrichment Analysis), a novel method for post-GWAS data mining.
- To infer differential gene expression and identify gene set enrichment associated with trait-linked SNPs using GWAS summary statistics and eQTL data.
Main Methods:
- GIGSEA integrates GWAS summary statistics with tissue-specific expression quantitative trait loci (eQTL) data.
- A weighted linear regression model is employed for enrichment testing, adjusting for imputation accuracy and model complexities.
- Permutation tests with matrix operations are used for significance assessment, enhancing computational speed.
Main Results:
- GIGSEA effectively infers gene expression patterns from GWAS data.
- The method accounts for complex regulatory factors including gene size, boundaries, and distal/multiple-marker regulation.
- GIGSEA demonstrates appropriate Type I error rates and identifies relevant biological findings in real datasets.
Conclusions:
- GIGSEA provides a robust framework for gene set enrichment analysis using GWAS summary statistics and eQTL data.
- The method enhances the biological interpretation of GWAS findings by connecting SNPs to gene expression and pathways.
- GIGSEA is implemented in R and publicly available, facilitating its application in genetic research.
Related Concept Videos
01:09Design Example: Setting a Curve Using Design Data
251
Designing and plotting a curve using field data requires precise calculations and execution. A horizontal curve with a radius of 200 meters and an intersection angle of 20 degrees is established using the method of perpendicular offsets from the long chord. The long chord, which spans between the curve's endpoints, is calculated to be 69.46 meters in length. To maintain accuracy in plotting, intervals of 3 meters are selected along the chord.The engineer determines the offset distances for each...
251
01:045-Number Summary
5.7K
In a dataset, the 5-number summary includes the minimum data value, the data value of the first quartile, the median data value or data value of the second quartile, the data value of the third quartile, and the maximum data value. These 5 data values can be visualized as a box and whisker plot.
In a box plot, the minimum and maximum data values represent the lower and upper whiskers in the graph, and the median is designated as the center of the box in the chart. The first quartile and third...
In a box plot, the minimum and maximum data values represent the lower and upper whiskers in the graph, and the median is designated as the center of the box in the chart. The first quartile and third...
5.7K
01:31Discharge Summary Forms
1.3K
The discharge summary is crucial as it enables a smooth transition from a healthcare facility to a patient's home or another care setting. This critical document facilitates seamless continuity of care, ensuring patients receive the necessary support and attention.
Here's a detailed look at the key components and guidelines for preparing a discharge summary:
Here's a detailed look at the key components and guidelines for preparing a discharge summary:
1.3K
01:12Analysis of Population Pharmacokinetic Data
758
Analysis of population pharmacokinetic data involves studying the behavior of drugs within diverse populations to understand their pharmacokinetic parameters. Traditional pharmacokinetic methods typically involve collecting samples from a few individuals and estimating these parameters. While these methods are commonly used, they have limitations in capturing the variability in drug response among individuals or heterogeneous populations. Population pharmacokinetics is employed to address these...
758
01:11Genome-wide Association Studies-GWAS
15.7K
Genome-wide association studies or GWAS are used to identify whether common SNPs are associated with certain diseases. Suppose specific SNPs are more frequently observed in individuals with a particular disease than those without the disease. In that case, those SNPs are said to be associated with the disease. Chi-square analysis is performed to check the probability of the allele likely to be associated with the disease.
GWAS does not require the identification of the target gene involved in...
GWAS does not require the identification of the target gene involved in...
15.7K
01:13Overview of Microsoft Excel as a Data Analysis Tool
1.6K
Microsoft Excel is a cornerstone tool for data analysis and statistical operations, offering a wide array of functionalities to manage, analyze, and visualize data efficiently. Recognized for its versatility, Excel facilitates the performance of basic to complex statistical operations, serving as an indispensable asset for analysts, researchers, and students alike. Excel's significance in data analysis emanates from its spreadsheet environment, where data can be organized in rows and...
1.6K