Title: 

Gene Hunting with Knockoffs for Hidden Markov Models


Abstract:

Modern scientific studies often require the identification of a subset of
relevant explanatory variables, in the attempt to understand an interesting
phenomenon. Several statistical methods have been developed to automate this 
task, but only recently was the framework of knockoffs proposed as a general
solution that can perform variable selection under rigorous type-I error
control, without relying on strong modeling assumptions.

In this paper, 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. Finally, we apply our methodology to datasets on Crohn’s
disease and some continuous phenotypes, e.g. levels of cholesterol.

Link to the paper: https://arxiv.org/abs/1706.04677