title: Learning monotonic genotype-phenotype maps.
abstract: Evolutionary escape of pathogens from the selective pressure of immune responses and from medical interventions is driven by the accumulation of mutations. We introduce a statistical model for jointly estimating the dynamics and dependencies among genetic alterations and the associated phenotypic changes. The model integrates conjunctive Bayesian networks, which define a partial order on the occurrences of genetic events, with isotonic regression. The resulting genotype-phenotype map is non-decreasing in the lattice of genotypes. It describes evolutionary escape as a directed process following a phenotypic gradient, such as a monotonic fitness landscape. We present efficient algorithms for parameter estimation and model selection. The model is validated using simulated data and applied to HIV drug resistance data. We find that the effect of many resistance mutations is non-linear and depends on the genetic background in which they occur.
authors.affiliation: ETH Zürich. niko.beerenwinkel@bsse.ethz.ch
authors.forename: Niko
authors.initials: N
authors.lastname: Beerenwinkel
authors.forename: Patrick
authors.initials: P
authors.lastname: Knupfer
authors.forename: Achim
authors.initials: A
authors.lastname: Tresch
mesh_terms: D000465:Algorithms; D001499:Bayes Theorem; D003198:Computer Simulation; D024882:Drug Resistance, Viral / Q000235:genetics*; D019143:Evolution, Molecular; D005838:Genotype; D006678:HIV / Q000187:drug effects / Q000235:genetics; D006801:Humans; D015233:Models, Statistical; D009154:Mutation / Q000235:genetics; D010641:Phenotype; D012043:Regression, Psychology