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 principal components regression approach to multilocus genetic association studies.

Genetic epidemiology - 1 Feb 2008

Wang Kai, Abbott Diana

Abstract excerpt

With the rapid development of modern genotyping technology, it is becoming commonplace to genotype densely spaced genetic markers such as single nucleotide polymorphisms (SNPs) along the genome. This development has inspired a strong interest in using multiple markers located in the target region...

Read the complete abstract on PubMed

Topics

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

Share to Gene Frequency
Share to Genetic Associations and Epidemiology
Share to Haplotypes

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Share to Linkage Disequilibrium

Share to Models, Biological

Share to Polymorphism, Single Nucleotide

Share to Principal Component Analysis

Share to Regression Analysis

Source

Identifiers

PMID: 17849491
DOI: 10.1002/gepi.20266

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 2 Sept 2026. Prepared record publication-17849491-2026-09-02.2. Manifest publication-graph-cohort-2026-09-02.2.

Source receipt: PubMed via Helena Literature Corpus, retrieved 2 Sept 2026.

Related research

Semantic proximity does not establish scientific evidence.

Research trail→

Click a neighbor to travelStep 1 · 12 closest

A principal components regression approach to multilocus genetic association studies.PMID 17849491Read ↗

A principal components-based clustering method to identify variants associated with complex traits.Semantic proximity Read ↗

Power analysis of principal components regression in genetic association studies.Semantic proximity Read ↗

Maximizing the power of principal-component analysis of correlated phenotypes in genome-wide association studies.Semantic proximity Read ↗

Whole genome-wide association study using affymetrix SNP chip: a two-stage sequential selection method to identify genes that increase the risk of developing complex diseases.Semantic proximity Read ↗

Variance component methods for analysis of complex phenotypes.Semantic proximity Read ↗

Testing association between disease and multiple SNPs in a candidate gene.Semantic proximity Read ↗

Principal component regression and linear mixed model in association analysis of structured samples: competitors or complements?Semantic proximity Read ↗

Logistic Principal Component Analysis for Rare Variants in Gene-Environment Interaction Analysis.Semantic proximity Read ↗

Multiple linear combination (MLC) regression tests for common variants adapted to linkage disequilibrium structure.Semantic proximity Read ↗

Testing association and maternally mediated genetic effects with the principal component analysis in case-parents studies.Semantic proximity Read ↗

Principal components analysis corrects for stratification in genome-wide association studies.Semantic proximity Read ↗

Principal component analysis for selection of optimal SNP-sets that capture intragenic genetic variation.Semantic proximity Read ↗

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