Evidence map›Paper›PMID 27010022›Full record

ArticleGenetics2016

Bayesian Inference of Natural Selection from Allele Frequency Time Series.

Joshua G Schraiber, Steven N Evans, Montgomery Slatkin

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 65 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
65citing papers in PubMed, 1 pooled it
–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

65 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Direct estimation of genotype fitness from time series.bioRxiv : the preprint server for biology · 2026
    Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Article
  9. Article
  10. Article
  11. Article
  12. Article
  13. Inferring fitness seascapes from evolutionary histories.bioRxiv : the preprint server for biology · 2025
    Article
  14. Article
  15. Article
  16. A binary trait model reveals the fitness effects of HIV-1 escape from T cell responses.Proceedings of the National Academy of Sciences of the United States of America · 2025
    Article
  17. Article
  18. Computational Genomics and Its Applications to Anthropological Questions.American journal of biological anthropology · 2024
    Review
  19. Article
  20. Article

5 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

3 authors.

Joshua G SchraiberDepartment of Genome Sciences, University of Washington, Seattle, Washington 98195 schraib@uw.edu.
Steven N EvansDepartment of Statistics, University of California, Berkeley, California Department of Mathematics, University of California, Berkeley, California.
Montgomery SlatkinDepartment of Integrative Biology, University of California, Berkeley, California 94720.

Funding

MODELS OF COMPLEX GENETIC SYSTEMSR01GM040282 · NIGMS · UNIVERSITY OF CALIFORNIA BERKELEY · PI SLATKIN, MONTGOMERY WILSON · 1988 to 2017
$4.4M
Methods for inference of complex demography and selection from genomic dataR01GM109454 · NIGMS · UNIVERSITY OF CALIFORNIA BERKELEY · PI SONG, YUN S · 2013 to 2016
$1.2M
NIGMS NIH HHS R01 GM040282NIGMS NIH HHS R01 GM109454
6 · The paper itself

Abstract

The advent of accessible ancient DNA technology now allows the direct ascertainment of allele frequencies in ancestral populations, thereby enabling the use of allele frequency time series to detect and estimate natural selection. Such direct observations of allele frequency dynamics are expected to be more powerful than inferences made using patterns of linked neutral variation obtained from modern individuals. We developed a Bayesian method to make use of allele frequency time series data and infer the parameters of general diploid selection, along with allele age, in nonequilibrium populations. We introduce a novel path augmentation approach, in which we use Markov chain Monte Carlo to integrate over the space of allele frequency trajectories consistent with the observed data. Using simulations, we show that this approach has good power to estimate selection coefficients and allele age. Moreover, when applying our approach to data on horse coat color, we find that ignoring a relevant demographic history can significantly bias the results of inference. Our approach is made available in a C++ software package.

Indexed as

Gene FrequencyModels, GeneticSelection, GeneticSoftwareAnimalsBayes TheoremDiploidyHorsesSkin PigmentationBayesian inferencediffusion theorynatural selectionpath augmentation

Identifiers

PMID27010022
PMCPMC4858794

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.