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

A Bayesian approach to the identification of panmictic populations and the assignment of individuals.

Genetical research - 1 Aug 2001

Dawson K J, Belkhir K

Abstract excerpt

We present likelihood-based methods for assigning the individuals in a sample to source populations, on the basis of their genotypes at co-dominant marker loci. The source populations are assumed to be at Hardy-Weinberg and linkage equilibrium, but the allelic composition of these source populations and even the number of source populations represented in the sample are treated as uncertain. The parameter of...

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 Genes, Dominant

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Share to Genetic Linkage

Share to Likelihood Functions

Share to Models, Statistical

Share to Monte Carlo Method

Share to Reproducibility of Results

Source

Identifiers

PMID: 11556138
DOI: 10.1017/s001667230100502x

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 7 Sept 2026. Manifest publication-graph-cohort-2026-09-07.1.

Source receipt: pubmed, retrieved 7 Sept 2026.

Related research

Semantic proximity does not establish scientific evidence.

Research trail→

Click a neighbor to travelStep 1 · 12 closest

A Bayesian approach to the identification of panmictic populations and the assignment of individuals.PMID 11556138Read ↗

Analytical bayesian approach for assigning individuals to populations.Semantic proximity Read ↗

A likelihood-free estimator of population structure bridging admixture models and principal components analysisSemantic proximity Read ↗

Bayesian identification of admixture events using multilocus molecular markers.Semantic proximity Read ↗

Fast and accurate population admixture inference from genotype data from a few microsatellites to millions of SNPs.Semantic proximity Read ↗

Algorithms for selecting informative marker panels for population assignment.Semantic proximity Read ↗

Bayesian models for the analysis of genetic structure when populations are correlated.Semantic proximity Read ↗

Evaluating DNA Mixtures with Contributors from Different Populations Using Probabilistic Genotyping.Semantic proximity Read ↗

On inferring presence of an individual in a mixture: a Bayesian approach.Semantic proximity Read ↗

Bayesian association mapping for quantitative traits in a mixture of two populations.Semantic proximity Read ↗

A fast least-squares algorithm for population inference.Semantic proximity Read ↗

Use of genetic data to infer population-specific ecological and phenotypic traits from mixed aggregations.Semantic proximity Read ↗

The number of alleles in DNA mixtures with related contributors.Semantic proximity Read ↗

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