Computationally efficient composite likelihood statistics for demographic inference

Alec J. Coffman, Ping Hsun Hsieh, Simon Gravel, Ryan N. Gutenkunst

Research output: Contribution to journalArticlepeer-review

79 Scopus citations


Many population genetics tools employ composite likelihoods, because fully modeling genomic linkage is challenging. But traditional approaches to estimating parameter uncertainties and performing model selection require full likelihoods, so these tools have relied on computationally expensive maximum-likelihood estimation (MLE) on bootstrapped data. Here, we demonstrate that statistical theory can be applied to adjust composite likelihoods and perform robust computationally efficient statistical inference in two demographic inference tools: ∂a∂i and TRACTS. On both simulated and real data, the adjustments perform comparably to MLE bootstrapping while using orders of magnitude less computational time.

Original languageEnglish (US)
Pages (from-to)591-593
Number of pages3
JournalMolecular biology and evolution
Issue number2
StatePublished - Feb 1 2016
Externally publishedYes

Bibliographical note

Funding Information:
This work was supported by National Science Foundation grant DEB-1146074 to R.N.G. This research was undertaken, in part, thanks to funding from the Canada Research Chairs program and the Canadian Institutes of Health Research MOP-134855 (to S.G.).

Publisher Copyright:
© The Author 2015. Published by Oxford University Press on behalf of the Society for Molecular Biology and Evolution.


  • composite likelihood
  • demographic inference
  • likelihood ratio test
  • parameter uncertainties


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