Evidence map›Paper›PMID 26545922›Full record

ArticleMolecular biology and evolution2016

Computationally Efficient Composite Likelihood Statistics for Demographic Inference.

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

Abstract read
In one paragraph

Article in Molecular biology and evolution, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 69 papers.

0numbers the graph read from it
0cells of the map it votes in
69citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

69 citing papers in PubMed.

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  12. Pervasive and recurrent hybridization prevents inbreeding in Europe's most threatened seabird.Proceedings of the National Academy of Sciences of the United States of America · 2025
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  16. Population history across timescales in an urban archipelago.bioRxiv : the preprint server for biology · 2025
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9 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Alec J CoffmanDepartment of Molecular and Cellular Biology, University of Arizona.
Ping Hsun HsiehDepartment of Ecology and Evolutionary Biology, University of Arizona.
Simon GravelDepartment of Human Genetics and Genome Quebec Innovation Centre, McGill University, Montreal, QC, Canada.
Ryan N GutenkunstDepartment of Molecular and Cellular Biology, University of Arizona rgutenk@email.arizona.edu.

Funding

Canadian Institutes of Health Research MOP-134855
6 · The paper itself

Abstract

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.

Indexed as

DemographyLikelihood FunctionsModels, GeneticComputational BiologyGenetics, PopulationHumanscomposite likelihooddemographic inferencelikelihood ratio testparameter uncertainties

Identifiers

PMID26545922
PMCPMC5854098

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.