A Global-Local Approach for Detecting Hotspots in Multiple-Response Regression.

ABSTRACT: We tackle modelling and inference for variable selection in regression problems with many predictors and many responses. We focus on detecting hotspots, that is, predictors associated with several responses. Such a task is critical in statistical genetics, as hotspot genetic variants shape the architecture of the genome by controlling the expression of many genes and may initiate decisive functional mechanisms underlying disease endpoints. Existing hierarchical regression approaches designed to model hotspots suffer from two limitations: their discrimination of hotspots is sensitive to the choice of top-level scale parameters for the propensity of predictors to be hotspots, and they do not scale to large predictor and response vectors, for example, of dimensions 10³-10

SUBMITTER: Ruffieux H 

PROVIDER: S-EPMC7612176 | biostudies-literature | 2020 Jun

REPOSITORIES: biostudies-literature