ArticleG3 (Bethesda, Md.)2019
Genomic Bayesian Confirmatory Factor Analysis and Bayesian Network To Characterize a Wide Spectrum of Rice Phenotypes.
Article in G3 (Bethesda, Md.), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed.
- Breeding 5.0: Artificial intelligence (AI)-decoded germplasm for accelerated crop innovation.Journal of integrative plant biology · 2026Article
- 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
- AI-integrated digital breeding for crop improvement.Frontiers in plant science · 2026Review
- 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
- Article
- The related SNPs and genes to body size using GWAS- latent variable modeling in dromedaries.BMC genomics · 2025Article
- Multi-omics integration and immune profiling identify possible causal networks leading to uterine microbiome dysbiosis in dairy cows that develop metritis.Animal microbiome · 2025Article
- 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
- Peeling back the many layers of competitive exclusion.Frontiers in microbiology · 2024Article
- 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
- Multi-trait and multi-environment Bayesian analysis to predict the G x E interaction in flood-irrigated rice.PloS one · 2022Article
- Genomic prediction and QTL mapping of root system architecture and above-ground agronomic traits in rice (Oryza sativa L.) with a multitrait index and Bayesian networks.G3 (Bethesda, Md.) · 2021Article
- Prediction of the importance of auxiliary traits using computational intelligence and machine learning: A simulation study.PloS one · 2021Article
- Improving Genomic Prediction for Seed Quality Traits in Oat (Avena sativa L.) Using Trait-Specific Relationship Matrices.Frontiers in genetics · 2021Article
- Modeling multiple phenotypes in wheat using data-driven genomic exploratory factor analysis and Bayesian network learning.Plant direct · 2021Article
- Deciphering Cattle Temperament Measures Derived From a Four-Platform Standing Scale Using Genetic Factor Analytic Modeling.Frontiers in genetics · 2020Article
- Utilizing trait networks and structural equation models as tools to interpret multi-trait genome-wide association studies.Plant methods · 2019Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
With the advent of high-throughput phenotyping platforms, plant breeders have a means to assess many traits for large breeding populations. However, understanding the genetic interdependencies among high-dimensional traits in a statistically robust manner remains a major challenge. Since multiple phenotypes likely share mutual relationships, elucidating the interdependencies among economically important traits can better inform breeding decisions and accelerate the genetic improvement of plants. The objective of this study was to leverage confirmatory factor analysis and graphical modeling to elucidate the genetic interdependencies among a diverse agronomic traits in rice. We used a Bayesian network to depict conditional dependencies among phenotypes, which can not be obtained by standard multi-trait analysis. We utilized Bayesian confirmatory factor analysis which hypothesized that 48 observed phenotypes resulted from six latent variables including grain morphology, morphology, flowering time, physiology, yield, and morphological salt response. This was followed by studying the genetics of each latent variable, which is also known as factor, using single nucleotide polymorphisms. Bayesian network structures involving the genomic component of six latent variables were established by fitting four algorithms (
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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 OpenQuestion graph.