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Related Concept Videos

Genetic Screens02:46

Genetic Screens

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Genetic screens are tools used to identify genes and mutations responsible for phenotypes of interest. Genetic screens help identify individuals or a group of people at risk of developing  genetic diseases and help them with early intervention, targeted therapy, and reproductive options.
Forward genetic screens
Forward or “classical” genetic screens involve creating random mutations in an organism’s DNA using radiation, mutagens, or insertion of additional bases, which...
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Although the genetic makeup of an organism plays a major role in determining the phenotype, there are also several environmental factors, such as temperature, oxygen availability, presence of mutagens, that can alter an organism’s phenotype.
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Updated: Jun 7, 2025

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry
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Genotype Performance Estimation in Targeted Production Environments by Using Sparse Genomic Prediction.

Osval A Montesinos-López1, Paolo Vitale2, Guillermo Gerard2

  • 1Facultad de Telemática, Universidad de Colima, Colima 28040, Colima, Mexico.

Plants (Basel, Switzerland)
|November 9, 2024
PubMed
Summary
This summary is machine-generated.

Sparse testing in plant breeding uses Incomplete Block Design (IBD) to efficiently evaluate genotypes across environments. IBD improves selection accuracy and reduces variability compared to random allocation, optimizing genomic estimated breeding values.

Keywords:
genomic predictionincomplete block designs allocationrandom allocationselection across environmentssparse testing

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Area of Science:

  • Plant Breeding and Genetics
  • Agricultural Science
  • Statistical Genetics

Background:

  • Multi-Environment Trials (METs) are crucial for plant breeding but are resource-intensive.
  • Sparse testing offers a cost-effective alternative by evaluating genotypes in a subset of environments.
  • Integrating genomic data enhances the accuracy of genetic effect estimations in sparse testing.

Purpose of the Study:

  • To evaluate the effectiveness of Incomplete Block Design (IBD) for sparse testing in plant breeding.
  • To compare the performance of IBD against random line allocation for estimating grain yield using Genomic Estimated Breeding Values (GEBVs).
  • To assess the impact of different Genomic Best Linear Unbiased Predictor (GBLUP) methods on GEBV accuracy.

Main Methods:

  • Employed Incomplete Block Design (IBD) for genotype allocation across environments.
  • Compared IBD with random line allocation, ensuring consistent environments per line.
  • Utilized six Genomic Best Linear Unbiased Predictor (GBLUP) methods, including Bayesian GBLUP, to compute GEBVs for grain yield.

Main Results:

  • Computing GEBVs for a target population of environments (TPE) using available phenotypic and marker data proved effective for selection.
  • The IBD method demonstrated superior performance with reduced variability compared to random allocation.
  • Pre-adjustment prediction of missing genotypic data did not consistently improve selection outcomes.

Conclusions:

  • Incomplete Block Design (IBD) enhances selection accuracy and efficiency in sparse testing scenarios.
  • IBD is a more robust method than random allocation for optimizing resource use in METs.
  • The findings support the use of IBD for efficient plant breeding programs and suggest that direct adjustment may be as effective as pre-prediction for missing data.