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Why usefulness is rarely useful.

Fangyi Wang1, Mitchell J Feldmann1, Daniel E Runcie1

  • 1Department of Plant Sciences, University of California Davis, Davis, CA 95616, USA.

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|December 24, 2024
PubMed
Summary
This summary is machine-generated.

Accurate computational phasing can help predict offspring traits in breeding programs. However, the benefit of using the Usefulness Criterion over predicted family means is minimal, even with high accuracy.

Keywords:
GenPredGenomic PredictionShared Data Resourcecross selectionfamily variancephasing errorsusefulness criterion

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Area of Science:

  • Plant breeding
  • Quantitative genetics
  • Genomic selection

Background:

  • Mate selection is crucial for efficient breeding programs.
  • The Usefulness Criterion enhances mate selection by considering offspring mean and standard deviation.
  • Predicting offspring standard deviation is challenging due to allele phasing requirements.

Purpose of the Study:

  • To develop a method for estimating phasing accuracy from unphased genotype data.
  • To evaluate if phasing accuracy is sufficient for predicting family standard deviations.
  • To assess the conditions under which the Usefulness Criterion can be accurately predicted.

Main Methods:

  • Developed a method to estimate phasing accuracy using unphased genotype data.
  • Utilized simulations with diverse genetic architectures.
  • Applied the method to a real strawberry breeding population.

Main Results:

  • Highly accurate computational phasing enables prediction of family standard deviations.
  • The Usefulness Criterion provides benefits over predicted family means only under specific conditions: high selection intensity, high heritability, and few quantitative trait loci (QTL).
  • The overall gain from using family usefulness is marginal.

Conclusions:

  • Accurate phasing is feasible but offers limited advantages in mate selection.
  • The Usefulness Criterion's practical utility is constrained by specific genetic and selection parameters.
  • Future breeding strategies may not significantly benefit from this criterion due to small gains.