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Posterior Inclusion Probability (PIP)

Definition
AI-generated

The posterior inclusion probability (PIP) is the posterior probability that a given variant is one of the causal (or driving) variants at a locus under a Bayesian fine-mapping model.

Topics
Population genetics Bayesian statistics
Synonyms
PIP
Related
Bayes factor Credible Set Fine-Mapping

Why it matters in GWAS

PIP-based summaries make uncertainty about causal variants explicit when many candidates are in LD, and they underpin reporting of 95% credible sets and cross-method comparisons in fine-mapping workflows.

Example usage

"The lead SNP had PIP 0.82 while the two secondary tags each had PIP near 0.09 under SuSiE."

Bayes factor Credible Set Fine-Mapping Posterior Probability Sum of Single Effects Model (SuSiE)
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

  • 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
  • Schaid DJ, Chen W, Larson NB. (2018). From genome-wide associations to candidate causal variants by statistical fine-mapping. Nat Rev Genet.
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