Compositional predictors, such as microbiome abundances, pose unique challenges in variable selection due to their unit-sum constraint and inherent dependencies. Existing approaches often rely on fixed association graphs derived from phylogenetic or ecological distances, which may not reflect outcome-relevant relationships. We propose GRACE (GRaph-Adaptive horseshoe for Compositional rEgression), a fully Bayesian framework that enforces compositional constraints, performs variable selection, and adaptively learns an outcome-driven shrinkage graph. GRACE achieves compositionality through a novel linear reparameterization of regression coefficients, while a structured horseshoe prior induces sparsity and smooths coefficients along the learned graph. Graph learning is accomplished via scaled beta2 priors on edge weights, providing both outcome-specific adaptation and posterior uncertainty quantification. We develop an efficient Gibbs sampler incorporating elliptical slice sampling to ensure scalability in high dimensions. Through extensive simulations, GRACE demonstrates competitive predictive accuracy and improved graph recovery compared with existing methods, particularly under graph misspecification. Application to oral microbiome data from the ORIGINS study identifies taxa associated with insulin resistance and yields an outcome-driven graph summarizing how those taxa relate to the outcome, a structure that differs substantially from phylogenetic or co-occurrence networks. These findings highlight that fixed predictor graphs useful for regularization may not faithfully represent outcome-relevant feature relationships, underscoring the need for adaptive, outcome-informed approaches in compositional regression.
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