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Optimizing genomic predictions in maize using a diversity panel and a multiparental population.

A López-Malvar1,2, R Santiago3, A Butrón3

  • 1Facultad de Biología, Departamento de Biología Vegetal y Ciencias del Suelo, Agrobiología Ambiental, Calidad de Suelos y Plantas, Universidad de Vigo, As Lagoas Marcosende, Unidad Asociada a la Misión Biológica de Galicia (CSIC), Vigo, Spain.

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Summary

Genomic selection is less effective in diverse maize populations due to genetic differences. Optimizing training sets based on relatedness is crucial for improving prediction accuracy in varied germplasm.

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Area of Science:

  • Plant breeding
  • Quantitative genetics
  • Genomics

Background:

  • Genomic selection (GS) predicts genetic values using SNP markers.
  • GS effectiveness relies on heritability, genetic similarity, and population structure.
  • Applying GS to diverse germplasm is challenging.

Purpose of the Study:

  • Evaluate genomic prediction models for stover quality traits in two distinct maize populations.
  • Assess intra-population and cross-population predictive abilities.
  • Investigate the impact of population structure on genomic selection efficiency.

Main Methods:

  • Genomic Best Linear Unbiased Prediction (GBLUP) models were used.
  • Two maize populations were analyzed: a diversity panel and a MAGIC population.
  • Agronomic and biochemical-structural traits related to stover quality were evaluated.

Main Results:

  • The diversity panel showed higher heritability (≥0.88 for flowering traits) and intra-population predictive ability (PA = 0.15-0.75) than the MAGIC population (PA = 0.14-0.37).
  • Cross-population prediction drastically reduced predictive ability (PA < 0.05) due to allele frequency and linkage disequilibrium differences.
  • Combining populations in the training set did not improve, and sometimes reduced, prediction accuracy.

Conclusions:

  • Genetic heterogeneity and linkage disequilibrium variations between populations hinder stable marker effect prediction.
  • Optimizing training set composition considering genetic relatedness and population structure is vital for effective genomic selection in diverse germplasm.