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

Power of mixed-model QTL mapping from phenotypic, pedigree and marker data in self-pollinated crops.

TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik - 1 Mar 2006

Arbelbide M, Yu J, Bernardo R

Abstract excerpt

The power of QTL mapping by a mixed-model approach has been studied for hybrid crops but remains unknown in self-pollinated crops. Our objective was to evaluate the usefulness of mixed-model QTL mapping in the context of a breeding program for a self-pollinated crop. Specifically, we simulated a soybean (Glycine max L. Merr.) breeding program and applied a mixed-model approach that comprised three steps: variance...

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 Crops, Agricultural

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Share to Genetics, Population

Share to Glycine max

Share to Inbreeding

Share to Quantitative Trait Loci

Share to Reproduction

Share to Sensitivity and Specificity

Source

Identifiers

PMID: 16402189
DOI: 10.1007/s00122-005-0189-7

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

Power of mixed-model QTL mapping from phenotypic, pedigree and marker data in self-pollinated crops.PMID 16402189Read ↗

Power of in silico QTL mapping from phenotypic, pedigree, and marker data in a hybrid breeding program.Semantic proximity Read ↗

Variation explained in mixed-model association mapping.Semantic proximity Read ↗

QTLNetwork-MP: integrative mapping of additive, epistatic, and G × E effects for complex traits in multiparent advanced generation intercross populations.Semantic proximity Read ↗

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

Mixed model analysis of quantitative trait loci.Semantic proximity Read ↗

An efficient multi-locus mixed-model approach for genome-wide association studies in structured populations.Semantic proximity Read ↗

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

Bayesian association mapping of multiple quantitative trait loci and its application to the analysis of genetic variation among Oryza sativa L. germplasms.Semantic proximity Read ↗

MegaLMM: Mega-scale linear mixed models for genomic predictions with thousands of traitsSemantic proximity Read ↗

Generalized linear mixed models for mapping multiple quantitative trait loci.Semantic proximity Read ↗

An IBD-based mixed model approach for QTL mapping in multiparental populations.Semantic proximity Read ↗

Regularized multi-trait multi-locus linear mixed models for genome-wide association studies and genomic selection in crops.Semantic proximity Read ↗

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