A Fast Lasso-Based Method for Inferring Pairwise Interactions

Large-scale genotype-phenotype screens provide a wealth of data for identifying molecular alternations associated with a phenotype. Epistatic effects play an important role in such association studies. For example, siRNA perturbation screens can be used to identify pairwise gene-silencing effects. In bacteria, epistasis has practical consequences in determining antimicrobial resistance as the genetic background of a strain plays an important role in determining resistance. Existing computational tools which account for epistasis do not scale to human exome-wide screens and struggle with genetically diverse bacterial species such as Pseudomonas aeruginosa. Combining earlier work in interaction detection with recent advances in integer compression, we present a method for epistatic interaction detection on sparse (human) exome-scale data, and an R implementation in the package Pint. Our method takes advantage of sparsity in the input data and recent progress in integer compression to perform lasso-penalised linear regression on all pairwise combinations of the input, estimating up to $200$ million potential effects, including epistatic interactions. Hence the human exome is within the reach of our method, assuming one parameter per gene and one parameter per epistatic effect for every pair of genes. We demonstrate Pint on both simulated and real data sets, including antibiotic resistance testing and siRNA perturbation screens.