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Updated: May 25, 2026
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A Pathway Association Study Tool for GWAS Analyses of Metabolic Pathway Information
Published on: July 1, 2020
QTL/microarray approach using pathway information.
Hirokazu Matsuda1, Yukio Taniguchi, Hiroaki Iwaisaki
1Graduate School of Agriculture, Kyoto University, Kitashirakawa Oiwake-cho, Sakyo-ku, Kyoto, 606-8502, Japan. hmatsuda@kais.kyoto-u.ac.jp.
Algorithms for Molecular Biology : AMB
|January 17, 2012
Summary
This study introduces a novel gene set analysis method to identify biological pathways linked to quantitative traits, improving the detection of moderate gene expression changes and genotype-phenotype relationships.
Area of Science:
- Genomics
- Systems Biology
- Bioinformatics
Background:
- Quantitative trait loci (QTL) and microarray analyses are common for identifying differentially expressed genes.
- Low cutoffs in individual gene analyses can miss moderately expressed genes relevant to traits.
- Understanding genotype-phenotype relationships requires analyzing gene expression in biological pathways.
Purpose of the Study:
- To develop a modified gene set analysis to detect trait-associated pathways.
- To identify pathways with significantly affected expression, even if individual gene changes are less significant.
- To improve the understanding of genotype-phenotype relationships by examining biological pathways.
Main Methods:
- Modified gene set analysis applied to liver tissue gene expression data from four mouse strains.
- Integration of quantitative trait loci (QTL) information for high-density lipoprotein (HDL) cholesterol and insulin-like growth factor 1 (IGF-1) blood concentrations.
- Analysis of publicly available microarray data to detect trait-associated pathways.
Main Results:
- Several metabolic pathways (lipid metabolism, ABC transporters, cytochrome P450) were associated with HDL QTL.
- Signal transduction pathways regulating biological processes were identified for IGF-1 phenotype QTL.
- The modified approach successfully identified trait-associated pathways by analyzing gene sets.
Conclusions:
- A new method was developed to identify pathways associated with quantitative traits using QTL information.
- This approach offers insights into genotype-phenotype relationships at the biological pathway level.
- The method aids in elucidating the genetic architecture underlying phenotypic trait variations.
