Discover Community Create You

Loading publication...

Noodle always counts identifier-free feature events for service operation. With your permission, Google Analytics can also estimate audience and engagement. Search text, publication identifiers, cookies, IP addresses and browser fingerprints are never included in Noodle's first-party event counters.

Privacy details

Article

Detecting and Quantifying Changing Selection Intensities from Time-Sampled Polymorphism Data.

G3 (Bethesda, Md.) - 7 Apr 2016

Shim Hyunjin, Laurent Stefan, Matuszewski Sebastian, Foll Matthieu, Jensen Jeffrey D

Abstract excerpt

During his well-known debate with Fisher regarding the phenotypic dataset of Panaxia dominula, Wright suggested fluctuating selection as a potential explanation for the observed change in allele frequencies. This model has since been invoked in a number of analyses, with the focus of discussion centering mainly on random or oscillatory fluctuations of selection intensities. Here, we present a novel method to...

Read the complete abstract on PubMed

Topics

Share this publication in a Topic to start or enrich a Post.

Share to Algorithms
Share to Bayes Theorem
Share to Biological Evolution

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Share to Computer Simulation

Share to Genetics, Population

Share to Host-Pathogen Interactions

Share to ROC Curve

Share to Selection, Genetic

Source

Open in PubMed Open full text

Identifiers

PMID: 26869618
DOI: 10.1534/g3.115.023200
PMCID: PMC4825659

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 5 Sept 2026. Manifest publication-graph-cohort-2026-09-05.1.

Source receipt: pubmed, retrieved 5 Sept 2026.

Related research

Semantic proximity does not establish scientific evidence.

Research trail→

Click a neighbor to travelStep 1 · 12 closest

Detecting and Quantifying Changing Selection Intensities from Time-Sampled Polymorphism Data.PMID 26869618Read ↗

Quantifying Selection with Pool-Seq Time Series Data.Semantic proximity Read ↗

GADMA: Genetic algorithm for inferring demographic history of multiple populations from allele frequency spectrum dataSemantic proximity Read ↗

A comprehensive representation of genotypic selection beyond two alleles, and its estimation from time-series dataSemantic proximity Read ↗

Statistical Inference in the Wright-Fisher Model Using Allele Frequency Data.Semantic proximity Read ↗

Estimating time-varying selection coefficients from time series data of allele frequenciesSemantic proximity Read ↗

Detecting consistent patterns of directional adaptation using differential selection codon modelsSemantic proximity Read ↗

Continuously fluctuating selection reveals fine granularity of adaptation.Semantic proximity Read ↗

Quantifying selection acting on a complex trait using allele frequency time series data.Semantic proximity Read ↗

Bayesian inference of selection in the Wright-Fisher diffusion model.Semantic proximity Read ↗

Allele frequency divergence reveals ubiquitous influence of positive selection in Drosophila.Semantic proximity Read ↗

Uncovering the genetic signature of quantitative trait evolution with replicated time series data.Semantic proximity Read ↗

Forecasting Genomic Change with Time Series Sequence Data.Semantic proximity Read ↗

⌁Select a neighboring publication to make it the new centre.