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

Geographic sampling scheme as a determinant of the major axis of genetic variation in principal components analysis.

Molecular biology and evolution - 1 Feb 2013

DeGiorgio Michael, Rosenberg Noah A

Abstract excerpt

Principal component (PC) maps, which plot the values of a given PC estimated on the basis of allele frequency variation at the geographic sampling locations of a set of populations, are often used to investigate the properties of past range expansions. Some studies have argued that in a range expansion, the axis of greatest variation (i.e., the first PC) is parallel to the axis of expansion. In contrast, others...

Read the complete abstract on PubMed

Topics

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

Share to Computer Simulation
Share to Gene Frequency
Share to Genetic Variation

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Share to Genetics, Population

Share to Geography

Share to Models, Genetic

Share to Principal Component Analysis

Source

Open in PubMed Open full text

Identifiers

PMID: 23051843
DOI: 10.1093/molbev/mss233
PMCID: PMC3548310

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

Geographic sampling scheme as a determinant of the major axis of genetic variation in principal components analysis.PMID 23051843Read ↗

Principal component analysis under population genetic models of range expansion and admixture.Semantic proximity Read ↗

Guidelines for standardizing the application of discriminant analysis of principal components to genotype data.Semantic proximity Read ↗

Exploring Population Structure with Admixture Models and Principal Component Analysis.Semantic proximity Read ↗

A genealogical interpretation of principal components analysis.Semantic proximity Read ↗

A roadmap to robust discriminant analysis of principal components.Semantic proximity Read ↗

Modelling complex population structure using <i>F</i> -statistics and Principal Component AnalysisSemantic proximity Read ↗

Inferring the geographic origin of a range expansion: Latitudinal and longitudinal coordinates inferred from genomic data in an ABC framework with the program x-origin.Semantic proximity Read ↗

Relationships among evolutionary distance, the variance–covariance matrix, multidimensional scaling, and principal component analysisSemantic proximity Read ↗

Theoretical Analysis of Principal Components in an Umbrella Model of Intraspecific EvolutionSemantic proximity Read ↗

A comparison of individual-based genetic distance metrics for landscape genetics.Semantic proximity Read ↗

Accurate inference of subtle population structure (and other genetic discontinuities) using principal coordinates.Semantic proximity Read ↗

An Eigenvalue test for spatial principal component analysis.Semantic proximity Read ↗

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