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Updated: Nov 11, 2025

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

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

Published on: July 1, 2020

Pathway testing for longitudinal metabolomics.

Mitra Ebrahimpoor¹, Pietro Spitali², Jelle J Goeman¹

¹Medical Statistics, Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, The Netherlands.

Statistics in Medicine

|March 26, 2021

Summary

This summary is machine-generated.

This study introduces a novel top-down pathway analysis for longitudinal metabolite data, identifying biological pathways with changing metabolites. The method effectively handles complex data, including missing values and small sample sizes, improving metabolite data analysis.

Keywords:

global test joint latent process longitudinal analysis mixed model pseudo likelihood

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Last Updated: Nov 11, 2025

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

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A Strategy for Sensitive, Large Scale Quantitative Metabolomics

A Strategy for Sensitive, Large Scale Quantitative Metabolomics

Published on: May 27, 2014

Area of Science:

Metabolomics
Systems Biology
Statistical Bioinformatics

Background:

Longitudinal metabolite data analysis is crucial for understanding dynamic biological processes.
Existing methods often struggle with unbalanced designs, missing data, and small sample sizes.
Top-down approaches can leverage correlations between metabolites for increased statistical power.

Purpose of the Study:

To develop a robust top-down pathway analysis method for longitudinal metabolomics data.
To identify biological pathways exhibiting significant metabolite progression over time.
To address limitations of existing methods in handling complex longitudinal data structures.

Main Methods:

A score test based on a shared latent process mixed model was developed.
The approach explicitly models correlations between metabolites.
A computationally efficient pseudo-likelihood method was proposed for large pathways.

Main Results:

The proposed method successfully identifies pathways with differentially progressing metabolites.
Simulation studies demonstrated the advantages over existing approaches.
The methodology proved effective in analyzing real-world mouse experiment data.

Conclusions:

The developed top-down approach offers a powerful and flexible tool for longitudinal metabolomics pathway analysis.
It provides valid and robust results even with unbalanced designs, missing data, and small sample sizes.
This method enhances the discovery of biologically relevant pathways in dynamic systems.