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Related Concept Videos

Genome-wide Association Studies-GWAS01:11

Genome-wide Association Studies-GWAS

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...
Genomics02:02

Genomics

Genomics is the science of genomes: it is the study of all the genetic material of an organism. In humans, the genome consists of information carried in 23 pairs of chromosomes in the nucleus, as well as mitochondrial DNA. In genomics, both coding and non-coding DNA is sequenced and analyzed. Genomics allows a better understanding of all living things, their evolution, and their diversity. It has a myriad of uses: for example, to build phylogenetic trees, to improve productivity and...

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Related Experiment Video

Updated: Jun 13, 2026

Using Human Differentially Expressed Gene Lists to Perform Downstream Pathway Enrichment Analysis and Target Prioritization
03:08

Using Human Differentially Expressed Gene Lists to Perform Downstream Pathway Enrichment Analysis and Target Prioritization

Published on: October 3, 2025

Meta-analysis for pathway enrichment analysis when combining multiple genomic studies.

Kui Shen1, George C Tseng

  • 1Department of Computational Biology, University of Pittsburgh School of Medicine, Pittsburgh, PA 15213, USA.

Bioinformatics (Oxford, England)
|April 23, 2010
PubMed
Summary
This summary is machine-generated.

Meta-analysis for pathway enrichment (MAPE) methods were developed to increase statistical power in genomic studies. Two approaches, MAPE_G and MAPE_P, were investigated, with an integrated method (MAPE_I) recommended when complementary advantages are observed.

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A Pathway Association Study Tool for GWAS Analyses of Metabolic Pathway Information
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A Pathway Association Study Tool for GWAS Analyses of Metabolic Pathway Information

Published on: July 1, 2020

Related Experiment Videos

Last Updated: Jun 13, 2026

Using Human Differentially Expressed Gene Lists to Perform Downstream Pathway Enrichment Analysis and Target Prioritization
03:08

Using Human Differentially Expressed Gene Lists to Perform Downstream Pathway Enrichment Analysis and Target Prioritization

Published on: October 3, 2025

A Pathway Association Study Tool for GWAS Analyses of Metabolic Pathway Information
05:01

A Pathway Association Study Tool for GWAS Analyses of Metabolic Pathway Information

Published on: July 1, 2020

Area of Science:

  • Genomics
  • Bioinformatics
  • Statistical analysis

Background:

  • Pathway analysis methods identify biological states in genomic studies.
  • Meta-analysis methods integrate information from multiple genomic studies.
  • Pathway analysis meta-analysis has not been systematically pursued.

Purpose of the Study:

  • To develop and evaluate meta-analysis methods for pathway enrichment (MAPE).
  • To compare the performance of different MAPE approaches.

Main Methods:

  • Investigated two MAPE approaches: combining gene-level (MAPE_G) and pathway-level (MAPE_P) significance.
  • Developed an integrated method (MAPE_I) combining advantages of both.
  • Evaluated methods through simulations and real-world data (breast cancer, lung cancer).

Main Results:

  • Meta-analysis approaches showed increased statistical power compared to single-study analysis.
  • MAPE_G and MAPE_P demonstrated complementary advantages in different scenarios.
  • MAPE_P does not require gene matching across studies.

Conclusions:

  • The integrated MAPE_I method is recommended when MAPE_G and MAPE_P show complementary advantages.
  • MAPE methods enhance the analysis of genomic studies by integrating multiple datasets.