Distinguishing driver and passenger mutations in an evolutionary history categorized by interference

In many biological scenarios, from the development of drug resistance in pathogens to the progression of healthy cells toward cancer, quantifying the selection acting on observed mutations is a central question. One difficulty in answering this question is the complexity of the background upon which mutations can arise, with multiple potential interactions between genetic loci. We here present a method for discerning selection from a population history that accounts for interference between mutations. Given sequences sampled from multiple time points in the history of a population, we infer selection at each locus by maximizing a likelihood function derived from a multilocus evolution model. We apply the method to the question of distinguishing between loci where new mutations are under positive selection (drivers) and loci that emit neutral mutations (passengers) in a Wright-Fisher model of evolution. Relative to an otherwise equivalent method in which the genetic background of mutations was ignored, our method inferred selection coefficients more accurately for both driver mutations evolving under clonal interference and passenger mutations reaching fixation in the population through genetic drift or hitchhiking. In a population history recorded by 750 sets of sequences of 100 individuals taken at intervals of 100 generations, a set of 50 loci were divided into drivers and passengers with a mean accuracy of >0.95 across a range of numbers of driver loci. The potential application of our model, either in full or in part, to a range of biological systems, is discussed.