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Global-Local Shrinkage Prior

Definition
AI-generated

A global-local shrinkage prior is a hierarchical Bayesian prior in which a global scale parameter pulls many coefficients strongly toward zero while local scale parameters let individual coefficients escape shrinkage when the data demand it.

Topics
Bayesian statistics Regression and multivariate statistics
Related
Bayesian Statistics Conjugate prior Fine-Mapping

Why it matters in GWAS

Sparse genetic architectures at a locus—few large effects among many correlated SNPs—motivate priors that shrink most SNP effects heavily but allow outliers; global-local structure is a standard way to express that in Bayesian regression and variant prioritization.

Example usage

"The hierarchical model used a global-local shrinkage prior on SNP effects within the 500 kb fine-mapping window."

Bayesian Statistics Conjugate prior Fine-Mapping Generalized Linear Mixed Model
Related-term network

Click any node to open that term page.

Node order. The current term is first. Next come every page linked from Related terms, in the same top-to-bottom order as that list. Then new terms appear in two-hop layers: for each of those direct neighbors (in that same order), their related terms are appended if not already listed. A third pass does the same for two-hop nodes. The walk stops when a fixed maximum number of nodes is reached.

References

  • Carvalho CM, Polson NG, Scott JG. (2010). The horseshoe estimator for sparse signals. Biometrika.
  • Li Z, Zhou X. (2025). Towards improved fine-mapping of candidate causal variants. Nat Rev Genet. https://doi.org/10.1038/s41576-025-00869-4
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