ArticleG3 (Bethesda, Md.)2018
Historical Datasets Support Genomic Selection Models for the Prediction of Cotton Fiber Quality Phenotypes Across Multiple Environments.
Article in G3 (Bethesda, Md.), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 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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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
13 citing papers in PubMed.
- Genomic selection: Essence, applications, and prospects.The plant genome · 2025Review
- Identification of QTNs and Their Candidate Genes for Boll Number and Boll Weight in Upland Cotton.Genes · 2024Article
- Integrating genomics and genome editing for orphan crop improvement: a bridge between orphan crops and modern agriculture system.GM crops & food · 2023Review
- Article
- Genomic prediction of cotton fibre quality and yield traits using Bayesian regression methods.Heredity · 2022Article
- Outlook for Implementation of Genomics-Based Selection in Public Cotton Breeding Programs.Plants (Basel, Switzerland) · 2022Article
- Cotton Breeding in Australia: Meeting the Challenges of the 21st Century.Frontiers in plant science · 2022Review
- Unraveling the Genetic Basis of Key Agronomic Traits of Wrinkled Vining Pea (Frontiers in plant science · 2022Article
- Experimental evaluation of genomic selection prediction for rust resistance in sugarcane.The plant genome · 2021Article
- Genetic Diversity, QTL Mapping, and Marker-Assisted Selection Technology in Cotton (Frontiers in plant science · 2021Review
- Evaluation of genomic selection methods for predicting fiber quality traits in Upland cotton.Molecular genetics and genomics : MGG · 2020Article
- Exploring Deep Learning for Complex Trait Genomic Prediction in Polyploid Outcrossing Species.Frontiers in plant science · 2020Article
- Review
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Genomic selection (GS) has successfully been used in plant breeding to improve selection efficiency and reduce breeding time and cost. However, there has not been a study to evaluate GS prediction models that may be used for predicting cotton breeding lines across multiple environments. In this study, we evaluated the performance of Bayes Ridge Regression, BayesA, BayesB, BayesC and Reproducing Kernel Hilbert Spaces regression models. We then extended the single-site GS model to accommodate genotype × environment interaction (G×E) in order to assess the merits of multi- over single-environment models in a practical breeding and selection context in cotton, a crop for which this has not previously been evaluated. Our study was based on a population of 215 upland cotton (
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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 OpenQuestion graph.