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

An expectation and maximization algorithm for estimating Q X E interaction effects.

TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik - 1 May 2012

Zhao Fuping, Xu Shizhong

Abstract excerpt

A Markov chain Monte Carlo (MCMC) implemented Bayesian method has been developed to detect quantitative trait loci (QTL) effects and Q x E interaction effects. However, the MCMC algorithm is time consuming due to repeated samplings of QTL parameters. We developed an expectation and maximization (EM) algorithm as an alternative method for detecting QTL and Q x E interaction. Simulation studies and real data...

Read the complete abstract on PubMed

Topics

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

Share to Bayes Theorem
Share to Computer Simulation
Share to Crosses, Genetic

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Share to Hordeum

Share to Markov Chains

Share to Models, Statistical

Share to Monte Carlo Method

Share to Ploidies

Source

Identifiers

PMID: 22297562
DOI: 10.1007/s00122-012-1794-x

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 7 Sept 2026. Manifest publication-graph-cohort-2026-09-07.1.

Source receipt: pubmed, retrieved 7 Sept 2026.

Related research

Semantic proximity does not establish scientific evidence.

Research trail→

Click a neighbor to travelStep 1 · 12 closest

An expectation and maximization algorithm for estimating Q X E interaction effects.PMID 22297562Read ↗

Genotype by environment interaction of quantitative traits: a case study in barley.Semantic proximity Read ↗

Identification of QTL-by-environment interaction by controlling polygenic background effect.Semantic proximity Read ↗

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

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

A stochastic expectation and maximization algorithm for detecting quantitative trait-associated genes.Semantic proximity Read ↗

A fast genomic selection approach for large genomic data.Semantic proximity Read ↗

GbyE: A New Genome Wide Association and Prediction Model based on Genetic by Environmental InteractionSemantic proximity Read ↗

Genomic selection and complex trait prediction using a fast EM algorithm applied to genome-wide markers.Semantic proximity Read ↗

Capturing pair-wise epistatic effects associated with three agronomic traits in barley.Semantic proximity Read ↗

Multi-locus genome-wide association mapping reveals loci underlying malt quality traits in heterogeneous Ethiopian Barley (Hordeum vulgare L.) germplasm.Semantic proximity Read ↗

A Variational Bayes Genomic-Enabled Prediction Model with Genotype × Environment Interaction.Semantic proximity Read ↗

An application of Bayesian QTL mapping to early development in double haploid lines of rainbow trout including environmental effects.Semantic proximity Read ↗

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