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Genomic selection optimization in blueberry: Data-driven methods for marker and training population design.

Paul Adunola1, Luis Felipe V Ferrão1, Juliana Benevenuto1

  • 1Blueberry Breeding and Genomics Lab, Horticultural Sciences Department, University of Florida, Gainesville, Florida, USA.

The Plant Genome
|August 1, 2024
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Summary
This summary is machine-generated.

Genomic prediction optimizes blueberry breeding by using data-driven methods for marker selection and training populations. This improves genetic gain and selection accuracy, making breeding programs more efficient.

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Area of Science:

  • Plant breeding
  • Quantitative genetics
  • Bioinformatics

Background:

  • Genomic prediction (GP) enables accurate genetic merit assessment of unphenotyped individuals.
  • Efficient resource allocation for genotyping and phenotyping is crucial for GP implementation in breeding programs.
  • Blueberry (Vaccinium corymbosun L.) breeding programs can benefit from optimized GP strategies.

Purpose of the Study:

  • To integrate genetic and data-driven methods for optimal resource allocation in genomic prediction.
  • To evaluate the impact of data-driven marker selection on prediction accuracy and long-term genetic gain.
  • To compare optimization algorithms for selecting training populations to enhance predictive performance.

Main Methods:

  • Utilized a blueberry breeding dataset (>3000 individuals) with probe-based target sequencing and phenotypic data for three fruit quality traits.
  • Applied genetic data-driven methods for optimal marker selection and compared with random sampling.
  • Investigated simulation studies to assess long-term genetic gain over 30 breeding cycles.
  • Compared various optimization algorithms for training population selection to improve predictive accuracy.

Main Results:

  • Data-driven marker selection slightly improved prediction results for all fruit quality traits.
  • Simulations showed data-driven methods yield slightly higher genetic gain over 30 cycles compared to random marker sampling.
  • Optimization algorithms for training population selection effectively increased predictive performances.

Conclusions:

  • A data-oriented approach combining statistical and genetic methods aids critical breeding decisions for resource allocation in genomic prediction.
  • Optimized genotyping and phenotyping strategies enhance the efficiency and accuracy of plant breeding programs.
  • This study provides a framework for breeders to improve resource allocation for maximizing genetic gains through genomic prediction.