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

Exploring efficient linear mixed models to detect quantitative trait locus-by-environment interactions.

G3 (Bethesda, Md.) - 7 Aug 2021

Yamamoto Eiji, Matsunaga Hiroshi

Abstract excerpt

Genotype-by-environment (G × E) interactions are important for understanding genotype-phenotype relationships. To date, various statistical models have been proposed to account for G × E effects, especially in genomic selection (GS) studies. Generally, GS does not focus on the detection of each quantitative trait locus (QTL), while the genome-wide association study (GWAS) was designed for QTL detection. G × E...

Read the complete abstract on PubMed

Topics

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

Share to Chromosome Mapping
Share to Gene-Environment Interaction
Share to Genome-Wide Association Study

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Share to Genotype

Share to Phenotype

Share to Quantitative Trait Loci

Source

Open in PubMed Open full text

Identifiers

PMID: 33871575
DOI: 10.1093/g3journal/jkab119
PMCID: PMC8496289

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

Exploring efficient linear mixed models to detect quantitative trait locus-by-environment interactions.PMID 33871575Read ↗

Genome and Environment Based Prediction Models and Methods of Complex Traits Incorporating Genotype × Environment Interaction.Semantic proximity Read ↗

Improving power and accuracy of genome-wide association studies via a multi-locus mixed linear model methodology.Semantic proximity Read ↗

An efficient hierarchical generalized linear mixed model for mapping QTL of ordinal traits in crop cultivars.Semantic proximity Read ↗

A compressed variance component mixed model framework for detecting small and linked QTL-by-environment interactions.Semantic proximity Read ↗

Multi-trait and multi-environment QTL analyses of yield and a set of physiological traits in pepper.Semantic proximity Read ↗

Hybrid of Restricted and Penalized Maximum Likelihood Method for Efficient Genome-Wide Association Study.Semantic proximity Read ↗

Bayesian Genomic Prediction with Genotype × Environment Interaction Kernel Models.Semantic proximity Read ↗

Two New Strategies for Detecting and Understanding QTL × Environment InteractionsSemantic proximity Read ↗

Reproduction and In-Depth Evaluation of Genome-Wide Association Studies and Genome-Wide Meta-analyses Using Summary Statistics.Semantic proximity Read ↗

Connecting mathematical models to genomes: joint estimation of model parameters and genome-wide marker effects on these parameters.Semantic proximity Read ↗

A framework for simulating genotype-by-environment interaction using multiplicative models.Semantic proximity Read ↗

Impacts of QTL x environment interactions on genetic response to marker-assisted selection.Semantic proximity Read ↗

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