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Model Space

Definition
AI-generated

In Bayesian fine-mapping, the model space is the set of all causal configurations (and any other discrete structural choices) that the method considers at a locus—such as which subset of SNPs may be causal or how many independent signals are allowed.

Topics
Bayesian statistics Population genetics
Related
Causal Configuration FINEMAP Fine-Mapping

Why it matters in GWAS

The size and structure of the model space determine what “exploring configurations” means in methods like FINEMAP, how computationally hard search is, and how posterior mass is spread across multi-SNP explanations under LD.

Example usage

"We limited the model space to at most two causal variants per locus to keep the shotgun search tractable."

Causal Configuration FINEMAP Fine-Mapping Posterior Inclusion Probability (PIP)
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

  • Benner C, et al. (2016). FINEMAP: efficient variable selection using summary data from genome-wide association studies. Bioinformatics.
  • 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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