Selecting causal genes from genome-wide association studies via functionally coherent subnetworks

Area of Science:

• Genetics
• Bioinformatics
• Computational Biology

Background:

• Genome-wide association (GWA) studies identify thousands of disease-associated genetic loci, but pinpointing the specific causal genes and variants remains a significant challenge.
• Traditional methods often focus on genes nearest to associated polymorphisms, which can be biased and miss more distally acting causal genes.
• Prioritizing candidate genes based on existing literature favors well-characterized genes, potentially overlooking novel disease associations.

Purpose of the Study:

• To develop and validate a novel computational strategy for identifying sets of functionally related genes across multiple GWA loci.
• To overcome the limitations of proximity-based and literature-biased candidate gene prioritization in human disease research.
• To improve the identification of causal genes and variants underlying complex diseases, including various cancer types.

Main Methods:

• Development of a 'prix fixe' strategy utilizing genome-scale shared-function networks.
• Implementation of software to identify sets of mutually functionally related genes spanning multiple GWA loci.
• Application of the strategy to association data from approximately 100 GWA studies across ten cancer types.

Main Results:

• The proposed strategy successfully identified sets of functionally related genes across multiple GWA loci.
• The approach demonstrated superior performance in ranking known cancer genes compared to common alternative strategies.
• The method effectively leverages shared biological functions to link distal genetic loci to disease etiology.

Conclusions:

• The 'prix fixe' strategy offers a powerful new approach for prioritizing candidate genes in GWA studies.
• This network-based method enhances the ability to discover causal genes and elucidate the genetic basis of human diseases.
• As the number of identified GWA loci grows, this strategy is expected to significantly increase the power for disease gene discovery.