  Genetic interaction measures how different genes collectively contribute to a
phenotype, and can reveal functional compensation and buffering between
pathways under genetic perturbations. Recently, genome-wide screening for
genetic interactions has revealed genetic interaction networks that provide
novel insights either when analyzed by themselves or when integrated with other
functional genomic datasets. For higher eukaryotes such as human, the above
reverse-genetics approaches are not straightforward since the phenotypes of
interest for higher eukaryotes are difficult to study in a cell based assay. We
propose a general framework for constructing and analyzing human genetic
interaction networks from genome-wide single nucleotide polymorphism (SNP) data
used for case-control studies on complex diseases. Specifically, the approach
contains three major steps: (1) estimating SNP-SNP genetic interactions, (2)
identifying linkage disequilibrium (LD) blocks and mapping SNP-SNP interactions
to block-block interactions, and (3) functional mapping for LD blocks. We
performed two sets of functional analyses for each of the six datasets used in
the paper, and demonstrated that (i) the constructed genetic interaction
networks are supported by functional evidence from independent biological
databases, and (ii) the network can be used to discover pairs of compensatory
gene modules (between-pathway models) in their joint association with a disease
phenotype. The proposed framework should provide novel insights beyond existing
approaches that either ignore interactions between SNPs or model different
SNP-SNP pairs with genetic interactions separately. Furthermore, our study
provides evidence that some of the core properties of genetic interaction
networks based on reverse genetics in model organisms like yeast are also
present in genetic interactions revealed by natural variation in human
populations.
