A global-local approach for detecting hotspots in multiple-response regression
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Peer-reviewed
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Abstract
We tackle modelling and inference for variable selection in regression
problems with many predictors and many responses. We focus on detecting
hotspots, i.e., 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, e.g., of dimensions
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1941-7330