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FINEMAP

Definition
AI-generated

FINEMAP is a Bayesian fine-mapping method that uses summary statistics and an LD reference to explore high-dimensional causal configurations—which variants in a locus are causal—via efficient stochastic search (shotgun search), assigning posterior probabilities to configurations and variants.

Topics
Population genetics
Related
Causal Configuration Model Space Credible Set

Why it matters in GWAS

FINEMAP is widely used alongside SuSiE for single-locus fine-mapping from meta-analyzed or cohort GWAS summaries; choice of LD panel and consistency with the discovery sample affect calibration.

Example usage

"We compared FINEMAP and SuSiE credible sets for the T2D locus using the same 1000 Genomes–matched LD matrix."

Causal Configuration Model Space Credible Set Fine-Mapping Fine-Mapping from Summary Statistics SuSiE Linkage Disequilibrium (LD)
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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