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

Separation of the largest eigenvalues in eigenanalysis of genotype data from discrete subpopulations.

Theoretical population biology - 1 Nov 2013

Bryc Katarzyna, Bryc Wlodek, Silverstein Jack W

Abstract excerpt

We present a mathematical model, and the corresponding mathematical analysis, that justifies and quantifies the use of principal component analysis of biallelic genetic marker data for a set of individuals to detect the number of subpopulations represented in the data. We indicate that the power...

Read the complete abstract on PubMed

Topics

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

Share to Genetics, Population
Share to Genotype
Share to Models, Theoretical

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Source

Open in PubMed Open full text

Identifiers

PMID: 23973732
DOI: 10.1016/j.tpb.2013.08.004
PMCID: PMC3825268

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 4 Sept 2026. Prepared record publication-23973732-2026-09-04.2. Manifest publication-graph-cohort-2026-09-04.2.

Source receipt: PubMed via Helena Literature Corpus, retrieved 4 Sept 2026.

Related research

Semantic proximity does not establish scientific evidence.

Research trail→

Click a neighbor to travelStep 1 · 8 closest

Separation of the largest eigenvalues in eigenanalysis of genotype data from discrete subpopulations.PMID 23973732Read ↗

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

Using eigenvalues as variance priors in the prediction of genomic breeding values by principal component analysis.Semantic proximity Read ↗

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

Comparison of populations as a function of confidence intervals of gene probability.Semantic proximity Read ↗

Accounting for Sampling Error in Genetic Eigenvalues Using Random Matrix Theory.Semantic proximity Read ↗

Microarrays, pattern recognition and exploratory data analysis.Semantic proximity Read ↗

Codominant analysis of banding data from a dominant marker system by normal mixtures.Semantic proximity Read ↗

Establishment of a standardized system to perform population structure analyses with limited sample size or with different sets of SNP genotypes.Semantic proximity Read ↗

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