SConES

SConES selects connected explanatory SNPs by performing network-guided multi-locus association mapping to identify sets of genetic loci maximally associated with phenotypes and interconnected within a biological network.

Key Features:

• Network-guided approach: Leverages gene-gene interaction networks, which can be constructed based on proximity or other criteria, so neighboring SNPs are selected together.
• Multi-locus mapping efficiency: Employs a minimum cut reformulation to select features under sparsity and connectivity constraints, providing an exact and rapid solution.
• Scalability: Scales to hundreds of thousands of genetic loci for genome-wide analyses.
• Higher detection power: Demonstrates superior power in simulation studies to detect causal single nucleotide polymorphisms (SNPs) compared with other methods.
• Biological relevance: Integrates biological pathways and networks to facilitate interpretation of detected loci in terms of gene functions and interactions.

Scientific Applications:

• Complex trait mapping: Identifies sets of loci contributing jointly to complex phenotypes involving multiple genetic factors.
• Phenotype prediction and validation: Applied to flowering time data from Arabidopsis thaliana to predict phenotypes with results supported by existing literature.
• Hypothesis generation and interpretation: Supports interpretation of multi-locus associations in the context of biological pathways and gene interactions.

Methodology:

Formulates locus selection as a minimum cut problem on gene-gene interaction networks under sparsity and connectivity constraints.