Background: Genetic interactions involving more than two loci have been thought to affect
quantitatively inherited traits and diseases more pervasively than previously appreciated. However, the
detection of such high-order interactions to chart a complete portrait of genetic architecture has not
been well explored.
Methods: We present an ultrahigh-dimensional model to systematically characterize genetic main effects
and interaction effects of various orders among all possible markers in a genetic mapping or association
study. The model was built on the extension of a variable selection procedure, called
iFORM, derived from forward selection. The model shows its unique power to estimate the magnitudes
and signs of high-order epistatic effects, in addition to those of main effects and pairwise epistatic
Results: The statistical properties of the model were tested and validated through simulation studies.
By analyzing a real data for shoot growth in a mapping population of woody plant, mei (Prunus
mume), we demonstrated the usefulness and utility of the model in practical genetic studies. The model
has identified important high-order interactions that contribute to shoot growth for mei.
Conclusion: The model provides a tool to precisely construct genotype-phenotype maps for quantitative
traits by identifying any possible high-order epistasis which is often ignored in the current genetic