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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

Systems genetics: From GWAS to disease pathways.

Marijke R van der Sijde¹, Aylwin Ng², Jingyuan Fu¹

¹University of Groningen, University Medical Centre Groningen, Department of Genetics, The Netherlands.

Biochimica Et Biophysica Acta

|May 7, 2014

Summary

Systems genetics integrates genome-wide association study findings with multi-omics data to understand how genetic variations cause complex diseases. This approach links genotype to phenotype by analyzing molecular pathways and interactions.

Keywords:

Causal inference Disease mechanism Integrative approach Network modelling Post-GWAS Systems genetics

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Area of Science:

Genomics and Systems Biology
Complex Disease Etiology

Background:

Genome-wide association studies (GWAS) identify genetic variants for complex diseases but lack functional insights.
Understanding the link between genetic variants and disease mechanisms requires integrating molecular data.

Approach:

Systems genetics combines genotype information with diverse omics datasets (gene expression, methylation, proteomics, metabolomics).
It integrates environmental and physiological variables to provide a holistic view of genetic effects.
This approach facilitates causal inference from genotype to phenotype.

Key Points:

Post-GWAS research faces challenges in functionally characterizing identified genetic associations.
Multiple molecular levels and their interactions are crucial for understanding disease complexity.
Integrative approaches are needed to combine data across different molecular levels.

Conclusions:

Systems genetics offers a framework to unravel molecular mechanisms underlying genetic variation and disease.
By integrating multi-omics data, it aids in constructing genotype-phenotype relationships.
This approach is essential for advancing our understanding of complex disease aetiology.