Related Experiment Video
Updated: Jan 30, 2026
08:50
Predictive Immune Modeling of Solid Tumors
Published on: February 25, 2020
7.6K
Multigenerational prediction of genetic values using genome-enabled prediction
Isabela de Castro Sant' Anna1, Ricardo Augusto Diniz Cabral Ferreira2, Moysés Nascimento1
1Department of Statistics, Federal University of Viçosa, Viçosa, Minas Gerais, Brazil.
Plos One
|January 18, 2019
Summary
Genomic selection (GS) in plant breeding can be improved by using multigenerational prediction strategies. Combining data across generations and mating systems enhances the reliability of identifying elite individuals without costly phenotyping.
Area of Science:
- Plant breeding
- Quantitative genetics
- Genomics
Background:
- Phenotyping is costly, limiting elite individual identification in breeding programs.
- Genomic selection (GS) offers a cost-effective alternative by predicting genetic merit without phenotyping.
- Optimizing GS calibration strategies is crucial for improving prediction accuracy.
Purpose of the Study:
- To propose and evaluate calibration strategies for genomic selection using intergenerational data.
- To investigate the impact of linkage disequilibrium (LD) across different mating systems (outcrossing, self-pollination, hybridization) on GS reliability.
- To assess the influence of genetic architecture (dominance and heritability) on prediction accuracy.
Main Methods:
- Simulated a genome with QTL and generated populations through various mating systems (F2, S1-S4, H1-H4, A1-A4).
- Simulated quantitative traits under different genetic architectures (dominance levels and heritabilities).
- Employed a Ridge Regression-Best Linear Unbiased Prediction (RR-BLUP) model for genomic prediction, training on one generation and testing on subsequent generations.
Main Results:
- Prediction reliability was lowest when dominance (d/a) was high (d/a = 1), irrespective of the training population.
- Multigenerational prediction methodologies consistently improved GS reliability across all evaluated scenarios.
- The study identified optimal calibration strategies for enhancing GS accuracy in diverse breeding populations.
Conclusions:
- Multigenerational prediction is a robust strategy for improving genomic selection reliability in plant breeding.
- Understanding the interplay between mating systems, genetic architecture, and LD is key to optimizing GS.
- The findings provide practical insights for developing more efficient breeding programs through advanced genomic tools.
Related Concept Videos
02:27Predicting Molecular Geometry
45.8K
VSEPR Theory for Determination of Electron Pair Geometries
45.8K
01:03Prediction Intervals
3.4K
The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y.
3.4K
01:07End Point Prediction: Gran Plot
1.2K
A Gran plot is used to predict the equivalence volume or endpoint of a potentiometric or acid-base titration without reaching the endpoint. Typically, titration data is collected as a function of the titrant's volume up to a point less than the equivalence volume and then transformed into a linear format. The straight line is extended to the x-axis, indicating the necessary titrant volume to achieve the equivalence point.
For potentiometric titration, the Gran plot is created by plotting...
For potentiometric titration, the Gran plot is created by plotting...
1.2K
01:13Sensitivity, Specificity, and Predicted Value
1.3K
In healthcare diagnostics, laboratory tests play a crucial role in identifying and diagnosing a wide range of medical conditions. However, interpreting test results is not always straightforward. An abnormal test result does not always confirm the presence of a disease, just as a normal result does not guarantee its absence. To assess the reliability of these diagnostic tools, healthcare practitioners rely on two key statistical indicators: sensitivity and specificity.
Sensitivity is the...
Sensitivity is the...
1.3K
02:24Predicting Reaction Outcomes
10.8K
Kinetics describes the rate and path by which a reaction occurs. In contrast, thermodynamics deals with state functions and describes the properties, behavior, and components of a system. It is not concerned with the path taken by the process and cannot address the rate at which a reaction occurs. Although it does provide information about what can happen during a reaction process, it does not describe the detailed steps of what appears on an atomic or a molecular level. On the other hand,...
10.8K
02:52Predicting Products: Substitution vs. Elimination
14.8K
When a nucleophile and an alkyl halide react, nucleophilic substitution and β-elimination reactions compete to generate products.
The following factors can influence the mechanisms competing against each other:
The following factors can influence the mechanisms competing against each other:
14.8K