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

On the substructure controls in rare variant analysis: Principal components or variance components?

Genetic epidemiology - 1 Apr 2018

Luo Yiwen, Maity Arnab, Wu Michael C, Smith Chris, Duan Qing, Li Yun, Tzeng Jung-Ying

Abstract excerpt

Recent studies showed that population substructure (PS) can have more complex impact on rare variant tests and that similarity-based collapsing tests (e.g., SKAT) may suffer more severely by PS than burden-based tests. In this work, we evaluate the performance of SKAT coupling with principal components (PC) or variance components (VC) based PS correction methods. We consider confounding effects caused by PS...

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 Confounding Factors, Epidemiologic

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Share to Genetic Association Studies

Share to Genetic Variation

Share to Models, Genetic

Share to Principal Component Analysis

Source

Open in PubMed Open full text

Identifiers

PMID: 29280188
DOI: 10.1002/gepi.22102
PMCID: PMC5851819

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 6 Sept 2026. Manifest publication-graph-cohort-2026-09-06.1.

Source receipt: pubmed, retrieved 6 Sept 2026.

Related research

Semantic proximity does not establish scientific evidence.

Research trail→

Click a neighbor to travelStep 1 · 12 closest

On the substructure controls in rare variant analysis: Principal components or variance components?PMID 29280188Read ↗

Quantifying direct genetic signal captured by principal component adjustment.Semantic proximity Read ↗

Adjustment for population stratification via principal components in association analysis of rare variants.Semantic proximity Read ↗

On rare variants in principal component analysis of population stratification.Semantic proximity Read ↗

Adjusting for principal components can induce spurious associations in genome-wide association studies in admixed populationsSemantic proximity Read ↗

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

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

A spectral component approach leveraging identity-by-descent graphs to address recent population structure in genomic analysis.Semantic proximity Read ↗

Does accounting for gene-environment interactions help uncover association between rare variants and complex diseases?Semantic proximity Read ↗

Adjusting for principal components can induce collider bias in genome-wide association studies.Semantic proximity Read ↗

Assessing the impact of population stratification on association studies of rare variation.Semantic proximity Read ↗

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

[Family-based association tests for rare variants].Semantic proximity Read ↗

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