ArticlePlant direct2021
Modeling multiple phenotypes in wheat using data-driven genomic exploratory factor analysis and Bayesian network learning.
Article in Plant direct, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
7 citing papers in PubMed.
- Bayesian networks and structural equation models reveal genetic causal relationships between productivity, defense, and climate-adaptability traits in interior lodgepole pine.G3 (Bethesda, Md.) · 2026Article
- Selection of Potential Potato Cultivars With Export Quality Through Multitrait Genotype-Ideotype Distance Index (MGIDI).Food science & nutrition · 2026Article
- Genome-wide association study of the reproductive, body size, and carcass-related latent and directly measured traits in admixed beef heifers.Frontiers in genetics · 2026Article
- Multi-trait phenotypic modeling through factor analysis and bayesian network learning to develop latent reproductive, body conformational, and carcass-associated traits in admixed beef heifers.Frontiers in genetics · 2025Article
- Multi-omic data integration for the study of production, carcass, and meat quality traits in Nellore cattle.Frontiers in genetics · 2022Article
- Dissecting Breeders' SenseFrontiers in plant science · 2022Article
- Modeling multiple phenotypes in wheat using data-driven genomic exploratory factor analysis and Bayesian network learning.Plant direct · 2021Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Inferring trait networks from a large volume of genetically correlated diverse phenotypes such as yield, architecture, and disease resistance can provide information on the manner in which complex phenotypes are interrelated. However, studies on statistical methods tailored to multidimensional phenotypes are limited, whereas numerous methods are available for evaluating the massive number of genetic markers. Factor analysis operates at the level of latent variables predicted to generate observed responses. The objectives of this study were to illustrate the manner in which data-driven exploratory factor analysis can map observed phenotypes into a smaller number of latent variables and infer a genomic latent factor network using 45 agro-morphological, disease, and grain mineral phenotypes measured in synthetic hexaploid wheat lines (
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.