JAG: A Computational Tool to Evaluate the Role of Gene-Sets in Complex Traits

Area of Science:

• Genetics
• Bioinformatics
• Statistical genomics

Background:

• Complex traits are influenced by many genes, making individual genetic variant analysis challenging.
• Genome-Wide Association (GWA) studies often identify variants with small effect sizes, necessitating advanced analytical approaches.
• Gene-set analysis is crucial for understanding the polygenic architecture of complex diseases.

Purpose of the Study:

• To introduce Joint Association of Genetic variants (JAG), a novel tool for gene-set analysis.
• To test the joint effect of single nucleotide polymorphisms (SNPs) within specified gene sets or biological pathways.
• To evaluate the performance and power of JAG compared to existing gene-set analysis methods.

Main Methods:

• JAG assigns SNPs to genes and performs self-contained and/or competitive tests.
• Gene-set significance is assessed using permutation analysis.
• Permutation implicitly accounts for linkage disequilibrium, sample size, gene size, and gene-set composition.

Main Results:

• Power analysis using Wellcome Trust Case Control Consortium (WTCCC) Crohn's disease data demonstrated JAG's effectiveness.
• JAG successfully identified validated gene-sets associated with Crohn's disease.
• JAG exhibited superior power compared to other available gene-set analysis tools.

Conclusions:

• JAG is a powerful and novel tool for gene-set analysis in GWA studies.
• The method effectively handles the complexities of polygenic traits and small effect sizes.
• JAG is freely available for download, facilitating its use in genetic research.