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

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

Genetic epidemiology - 1 Mar 2015

Zhang Yiwei, Pan Wei

Abstract excerpt

Genome-wide association studies (GWAS) have been established as a major tool to identify genetic variants associated with complex traits, such as common diseases. However, GWAS may suffer from false positives and false negatives due to confounding population structures, including known or unknown relatedness. Another important issue is unmeasured environmental risk factors. Among many methods for adjusting for...

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 Diabetes Mellitus, Type 2
Share to Genetic Variation

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Share to Genome-Wide Association Study

Share to Linear Models

Share to Models, Genetic

Share to Polymorphism, Single Nucleotide

Share to Principal Component Analysis

Source

Open in PubMed Open full text

Identifiers

PMID: 25536929
DOI: 10.1002/gepi.21879
PMCID: PMC4366301

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

Principal component regression and linear mixed model in association analysis of structured samples: competitors or complements?PMID 25536929Read ↗

Limitations of principal components in quantitative genetic association models for human studies.Semantic proximity Read ↗

Limitations of principal components in quantitative genetic association models for human studiesSemantic proximity Read ↗

Simultaneous SNP selection and adjustment for population structure in high dimensional prediction models.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 ↗

Variance component model to account for sample structure in genome-wide association studies.Semantic proximity Read ↗

Genetic association models are robust to common population kinship estimation biases.Semantic proximity Read ↗

Improving the power of GWAS and avoiding confounding from population stratification with PC-Select.Semantic proximity Read ↗

A new approach of dissecting genetic effects for complex traitsSemantic proximity Read ↗

A resource-efficient tool for mixed model association analysis of large-scale data.Semantic proximity Read ↗

A mixed-model approach for genome-wide association studies of correlated traits in structured populations.Semantic proximity Read ↗

Accounting for Population Structure in Gene-by-Environment Interactions in Genome-Wide Association Studies Using Mixed Models.Semantic proximity Read ↗

A comprehensive analysis comparing linear and generalized linear models in detecting adaptive SNPs.Semantic proximity Read ↗

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