Modeling multiple phenotypes in wheat using data-driven genomic exploratory factor analysis and Bayesian network learning
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 pheno...
| Autores principales: | , , , , |
|---|---|
| Formato: | Journal Article |
| Lenguaje: | Inglés |
| Publicado: |
Wiley
2021
|
| Acceso en línea: | https://hdl.handle.net/10568/164356 |
Ejemplares similares: Modeling multiple phenotypes in wheat using data-driven genomic exploratory factor analysis and Bayesian network learning
- Variance heterogeneity genome‐wide mapping for cadmium in bread wheat reveals novel genomic loci and epistatic interactions
- Divergent phenotypic response of rice accessions to transient heat stress during early seed development
- Report on Bayesian network livelihood models
- Mapping QTL for multiple traits using Bayesian statistics
- Targeting agricultural policies and interventions to serve multiple stakeholder goals: A new modeling approach using Bayesian Network models
- SeedExtractor: An open-source GUI for seed image analysis