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Genomic selection in American mink breeding programs is enhanced by single-step genomic BLUP (ssGBLUP), especially for lowly heritable traits. Optimizing training set sizes is crucial for accurate genomic predictions.

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Area of Science:

  • Animal Genetics
  • Quantitative Genetics
  • Breeding Programs

Background:

  • Genomic selection (GS) offers effective breeding strategies for American mink.
  • Accurate genomic predictions are vital for maximizing genetic gains in economically important traits.
  • Traditional Best Linear Unbiased Prediction (BLUP) and genomic BLUP (GBLUP) methods have limitations in prediction accuracy.

Purpose of the Study:

  • To compare the prediction accuracies of BLUP, multi-step GBLUP, and single-step GBLUP (ssGBLUP) in American mink.
  • To evaluate the influence of heritability, marker density, training set size, and selection design on prediction accuracy.
  • To provide a framework for optimizing genomic selection in mink breeding programs.

Main Methods:

  • Simulated data were used to assess prediction accuracies.
  • Methods compared included BLUP, GBLUP, and ssGBLUP.
  • Factors varied included heritability (h²), marker density, training set (TS) size, and selection designs (phenotypic vs. estimated breeding values).

Main Results:

  • ssGBLUP and GBLUP significantly increased prediction accuracy compared to BLUP, particularly for lowly heritable traits.
  • ssGBLUP showed higher prediction accuracies than GBLUP under phenotypic selection designs for low heritability.
  • Prediction accuracy was not influenced by selection design at high heritability (h² = 0.50).
  • Higher selection intensity increased prediction bias in both pedigree-based and genomic evaluations.
  • A minimum training set size of 3000 individuals is recommended for GBLUP and ssGBLUP to outperform BLUP at h² = 0.50 and marker densities of 10k-50k.

Conclusions:

  • Single-step genomic BLUP (ssGBLUP) is a highly accurate prediction method for American mink breeding, especially for traits with low heritability and low marker density.
  • Optimizing training set sizes is essential and depends on heritability, marker density, selection design, and prediction method.
  • The study provides foundational insights for developing efficient genomic selection strategies in American mink breeding programs.