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

Efficient permutation-based genome-wide association studies for normal and skewed phenotypic distributions.

Bioinformatics (Oxford, England) - 16 Sept 2022

John Maura, Ankenbrand Markus J, Artmann Carolin, Freudenthal Jan A, Korte Arthur, Grimm Dominik G

Abstract excerpt

MOTIVATION: Genome-wide association studies (GWAS) are an integral tool for studying the architecture of complex genotype and phenotype relationships. Linear mixed models (LMMs) are commonly used to detect associations between genetic markers and a trait of interest, while at the same time allowing to account for population structure and cryptic relatedness. Assumptions of LMMs include a normal distribution of...

Read the complete abstract on PubMed

Topics

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

Share to Genetic Markers
Share to Genome-Wide Association Study

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Share to Genotype

Share to Linear Models

Share to Phenotype

Source

Open in PubMed Open full text

Identifiers

PMID: 36124808
DOI: 10.1093/bioinformatics/btac455
PMCID: PMC9486594

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 5 Sept 2026.

Related research

Semantic proximity does not establish scientific evidence.

Research trail→

Click a neighbor to travelStep 1 · 12 closest

Efficient permutation-based genome-wide association studies for normal and skewed phenotypic distributions.PMID 36124808Read ↗

The benefits of permutation-based genome-wide association studies.Semantic proximity Read ↗

PERMORY: an LD-exploiting permutation test algorithm for powerful genome-wide association testing.Semantic proximity Read ↗

Fast and flexible linear mixed models for genome-wide geneticsSemantic proximity Read ↗

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

Optimized permutation testing for information theoretic measures of multi-gene interactions.Semantic proximity Read ↗

LiMMBo: a simple, scalable approach for linear mixed models in high-dimensional genetic association studiesSemantic proximity Read ↗

Efficient p-value estimation in massively parallel testing problems.Semantic proximity Read ↗

GWA-X: An Extensible GPU Accelerated Package for Permutation Testing in Genome-Wide Association StudiesSemantic proximity Read ↗

Ludicrous Speed Linear Mixed Models for Genome-Wide Association StudiesSemantic proximity Read ↗

lrgpr: interactive linear mixed model analysis of genome-wide association studies with composite hypothesis testing and regression diagnostics in R.Semantic proximity Read ↗

Asymptotically exact fit for linear mixed model in genetic association studies.Semantic proximity Read ↗

LMM-22: An Enhanced Linear Mixed Model (LMM) Approach for Genome-Wide Association Studies (GWAS) for the Prediction of Diseases and Traits among Humans from Genomics DataSemantic proximity Read ↗

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