Chiara Sabatti Stanford University The framework of knockoffs has been recently proposed to perform variable selection under rigorous type-I error control, without relying on strong modeling assumptions. We extend the methodology of knockoffs to a rich family of problems where the distribution of the covariates can be described by a hidden Markov model. We develop an exact and efficient algorithm to sample knockoff variables in this setting and then argue that, combined with the existing selective framework, this provides a natural and powerful tool for performing principled inference in genome-wide association studies with guaranteed false discovery rate control. This is joint work with Matteo Sesia and Emmanuel Candes.