Novel Bayesian Bi-Level Variable Selection Method with Application to Alzheimer’s Disease

Thomas M. Menino Convention & Exhibition Center 

Plasma proteomics offers a noninvasive and biologically informative approach for studying cognitive function in Alzheimer's disease (AD). However, statistical analysis of plasma proteomic data is challenged by extreme high dimensionality, where conventional methods using uniform shrinkage often suffer from systematic bias: they tend to over-shrink small informative signals while under-penalizing large redundant groups. To address this, we propose BSGSSS-HS, a Bayesian bi-level selection framework. By integrating spike-and-slab and horseshoe priors, it performs simultaneous group and within-group selection, achieving group-aware, size-invariant shrinkage under heterogeneous sparsity. For posterior inference, we employ an efficient Gibbs sampler. Extensive simulation studies show that the proposed method consistently outperforms competing approaches across a variety of scenarios, including varying group sizes, signal-to-noise ratios, and dimensionalities. We further apply BSGSSS-HS to real AD data from the Vanderbilt Memory and Aging Project cohort, where the method is able to identify proteins and pathways implicated in AD-related pathological processes and cognitive decline.

High-dimensional

Bi-level selection

Bayesian variable selection

Horseshoe prior

spike and slab prior

Heterogeneous sparsity 

Section on Bayesian Statistical Science