Moving toward a system genetics view of disease

Area of Science:

• Genomics
• Systems Biology
• Statistical Genetics

Background:

• DNA variations influence complex traits and diseases through molecular networks.
• Molecular phenotypes, such as gene expression, act as intermediates between DNA variations and clinical disease states.
• Comprehensive monitoring of transcript levels is now feasible, enabling data integration.

Purpose of the Study:

• To review methods for integrating expression quantitative trait loci (eQTLs), gene expression, and clinical data.
• To describe network-based approaches for inferring causal relationships among molecular traits and between molecular and clinical traits.
• To enhance the identification of DNA variations associated with complex diseases and characterize disease-driving molecular networks.

Main Methods:

• Integration of expression quantitative trait loci (eQTLs), gene expression, and clinical data.
• Construction of coexpression gene networks leveraging pairwise gene interaction data.
• Application of a Bayesian algorithm to reconstruct whole-gene networks inferring causal relationships.

Main Results:

• Developed methods to infer causal relationships among gene expression traits and between expression and clinical traits.
• Demonstrated the utility of network approaches in integrating high-dimensional biological data from multiple sources.
• Showcased the potential of integrative genomics to identify key drivers of complex diseases.

Conclusions:

• Integrative genomics approaches, particularly network-based methods, significantly enhance the identification of disease drivers compared to genetic association studies alone.
• These methods facilitate the understanding of how genetic perturbations alter molecular network states, leading to disease.
• Evolving statistical network procedures are critical for extracting information on complex phenotypes and moving beyond single-gene analyses.