Merle Behr, Karl Kumbier, Aldo Cordova-Palomera, Matthew Aguirre, Omer Ronen, Chengzhong Ye, Euan Ashley, Atul J Butte, Rima Arnaout, Ben Brown, James Priest, Bin Yu
Detecting epistatic drivers of human phenotypes is a considerable challenge. Traditional approaches use regression to sequentially test multiplicative interaction terms involving pairs of genetic variants. For higher-order interactions and genome-wide large-scale data, this strategy is computationally intractable. Moreover, multiplicative terms used in regression modeling may not capture the form of biological interactions. Building on the Predictability, Computability, Stability (PCS) framework, we introduce the epiTree pipeline to extract higher-order interactions from genomic data using tree-based models...
2024: PloS One