ArticleFrontiers in plant science2018
Phenotypic Selection in Ornamental Breeding: It's Better to Have the BLUPs Than to Have the BLUEs.
Article in Frontiers in plant science, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed.
- Prediction-Based Family Selection in Early Stage Sugarcane Breeding: Comparing BLUP, BLUE, Phenotypic Indices, and Machine Learning.Plants (Basel, Switzerland) · 2026Article
- Genetic analysis of biomass yield and related traits for hybrid cultivar development in the leafy vegetable Gynandropsis gynandra (L.) Briq.BMC plant biology · 2026Article
- Dissecting the genetic basis of relevant fruit quality traits in interspecific grapevines (Horticulture research · 2026Article
- A new approach for evaluating maize transgressive segregants and their three-way cross potential in the S4 convergent breeding population.BMC plant biology · 2025Article
- Combining ability and heterosis analysis for mineral content in the leafy vegetable Gynandropsis gynandra (L.) Briq.PloS one · 2025Article
- Genetic Variation and Heritability for Hydrogen Cyanide in Fresh Cassava Roots: Implications for Low-Cyanide Cassava Breeding.Plants (Basel, Switzerland) · 2024Article
- A comprehensive multivariate approach for GxE interaction analysis in early maturing rice varieties.Frontiers in plant science · 2024Article
- High-density linkage mapping and genetic dissection of resistance to broomrape (Frontiers in plant science · 2023Article
- A comparison of classical and machine learning-based phenotype prediction methods on simulated data and three plant species.Frontiers in plant science · 2022Article
- Phenotypic variation in biomass and related traits among four generations advanced lines of Cleome (Gynandropsis gynandra L. (Briq.)).PloS one · 2022Article
- Quinoa Phenotyping Methodologies: An International Consensus.Plants (Basel, Switzerland) · 2021Review
- Average semivariance yields accurate estimates of the fraction of marker-associated genetic variance and heritability in complex trait analyses.PLoS genetics · 2021Article
- Unraveling the Relationship Between Seed Yield and Yield-Related Traits in a Diversity Panel ofFrontiers in plant science · 2021Article
- Factors Influencing Genomic Prediction Accuracies of Tropical Maize Resistance to Fall Armyworm and Weevils.Plants (Basel, Switzerland) · 2020Article
- Article
- PhenoCams for Field Phenotyping: Using Very High Temporal Resolution Digital Repeated Photography to Investigate Interactions of Growth, Phenology, and Harvest Traits.Frontiers in plant science · 2020Article
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3 authors.
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Abstract
Plant breeders always face the challenge to select the best individuals. Selection methods are required that maximize selection gain based on available data. When several crosses have been made, the BLUP procedure achieves this by combining phenotypic data with information on pedigree relationships via an index, known as family-index selection. The index, estimated based on the intra-class correlation coefficient, exploits the relationship among individuals within a family relative to other families in the population. An intra-class correlation coefficient of one indicates that the individual performance can be fully explained based on the family background, whereas an intra-class correlation coefficient of zero indicates the performance of individuals is independent of the family background. In the case the intra-class correlation coefficient is one, family-index selection is considered. In the case the intra-class correlation coefficient is zero, individual selection is considered. The main difference between individual and family-index selection lies in the adjustment in estimating the individual's effect depending on the intra-class correlation coefficient afforded by the latter. Two examples serve to illustrate the application of the BLUP method. The efficiency of individual and family-index selection was evaluated in terms of the heritability obtained from linear mixed models implementing the selection methods by suitably defining the treatment factor as the sum of individual and family effect. Family-index selection was found to be at least as efficient as individual selection in
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