Title:An Ultrahigh-Dimensional Mapping Model of High-order Epistatic Networks for Complex Traits
VOLUME: 19 ISSUE: 5
Author(s):Kirk Gosik, Lidan Sun, Vernon M. Chinchilli and Rongling Wu*
Affiliation:Department of Public Health Sciences, Penn State College of Medicine, Hershey, PA 17033, Department of Public Health Sciences, Penn State College of Medicine, Hershey, PA 17033, Department of Public Health Sciences, Penn State College of Medicine, Hershey, PA 17033, Department of Public Health Sciences, Penn State College of Medicine, Hershey, PA 17033
Keywords:Variable selection, iFORM, Epistasis, High-order interactions, Quantitative trait, Woody plant, Prunus mume.
Abstract: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
effects.
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
literature.
