Evidence map›Paper›PMID 27182948›Full record

ArticleGenetics2016

Two-Locus Likelihoods Under Variable Population Size and Fine-Scale Recombination Rate Estimation.

John A Kamm, Jeffrey P Spence, Jeffrey Chan, Yun S Song

Abstract read
In one paragraph

Article in Genetics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 44 papers.

0numbers the graph read from it
0cells of the map it votes in
44citing 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

44 citing papers in PubMed.

  1. Article
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  5. GENERAL MOMENT CLOSURE FOR THE NEUTRAL TWO-LOCUS WRIGHT-FISHER DYNAMICS.bioRxiv : the preprint server for biology · 2026
    Article
  6. Fine-scale recombination rates inferred using the canFam4 assembly are strongly correlated with previous maps of dog recombination.Mammalian genome : official journal of the International Mammalian Genome Society · 2025
    Article
  7. Article
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  19. Scaling the Discrete-time Wright Fisher model to biobank-scale datasets.bioRxiv : the preprint server for biology · 2023
    Article
  20. Article
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.

John A KammDepartment of Statistics, University of California, Berkeley, California 94720 Computer Science Division, University of California, Berkeley, California 94720.
Jeffrey P SpenceComputational Biology Graduate Group, University of California, Berkeley, California 94720.
Jeffrey ChanComputer Science Division, University of California, Berkeley, California 94720.
Yun S SongDepartment of Statistics, University of California, Berkeley, California 94720 Computer Science Division, University of California, Berkeley, California 94720 Department of Integrative Biology, University of California, Berkeley, California 94720 Departments of Mathematics and Biology, University of Pennsylvania, Philadelphia, Pennsylvania 19104 yss@berkeley.edu.

Funding

GENOMICST32HG000047 · NHGRI · UNIVERSITY OF CALIFORNIA BERKELEY · PI RASMUS NIELSEN, Daniel Soleyman Rokhsar · 2000 to 2026
$13.5M
Population Genetic Consequences of Recent Explosive Population Growth in HumansR01GM108805 · NIGMS · CORNELL UNIVERSITY · PI CLARK, ANDREW G, KEINAN, ALON · 2014 to 2017
$2.2M
NHGRI NIH HHS T32 HG000047NIGMS NIH HHS R01 GM108805
6 · The paper itself

Abstract

Two-locus sampling probabilities have played a central role in devising an efficient composite-likelihood method for estimating fine-scale recombination rates. Due to mathematical and computational challenges, these sampling probabilities are typically computed under the unrealistic assumption of a constant population size, and simulation studies have shown that resulting recombination rate estimates can be severely biased in certain cases of historical population size changes. To alleviate this problem, we develop here new methods to compute the sampling probability for variable population size functions that are piecewise constant. Our main theoretical result, implemented in a new software package called LDpop, is a novel formula for the sampling probability that can be evaluated by numerically exponentiating a large but sparse matrix. This formula can handle moderate sample sizes ([Formula: see text]) and demographic size histories with a large number of epochs ([Formula: see text]). In addition, LDpop implements an approximate formula for the sampling probability that is reasonably accurate and scales to hundreds in sample size ([Formula: see text]). Finally, LDpop includes an importance sampler for the posterior distribution of two-locus genealogies, based on a new result for the optimal proposal distribution in the variable-size setting. Using our methods, we study how a sharp population bottleneck followed by rapid growth affects the correlation between partially linked sites. Then, through an extensive simulation study, we show that accounting for population size changes under such a demographic model leads to substantial improvements in fine-scale recombination rate estimation.

Indexed as

Genetics, PopulationModels, GeneticRecombination, GeneticAlgorithmsComputer SimulationHumansLikelihood FunctionsPopulation Densitycoalescent with recombinationimportance samplingsampling probabilitytwo-locus Moran model

Identifiers

PMID27182948
PMCPMC4937484

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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.