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Marker Imputation Before Genomewide Selection in Biparental Maize Populations.

Amy Jacobson1, Lian Lian1, Shengqiang Zhong2

  • 1Dep. of Agronomy and Plant Genetics, Univ. of Minnesota, 1991 Upper Buford Cir., Saint Paul, MN, 55108.

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Summary

Marker imputation enhances genomewide selection in maize by improving prediction accuracy. Assaying fewer single nucleotide polymorphism (SNP) markers and using imputation with a general combining ability (GCA) model is effective for breeding.

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Area of Science:

  • Plant Breeding
  • Genetics
  • Agricultural Science

Background:

  • Genomewide selection (GWS) is a valuable tool in plant breeding for improving crop traits.
  • Increasing the number of markers can enhance the accuracy of GWS, but high-density genotyping is costly.
  • Marker imputation offers a cost-effective strategy to increase marker density for GWS.

Purpose of the Study:

  • To evaluate if marker imputation improves selection response (R) and prediction accuracy (rMP) in maize progeny.
  • To determine the optimal number of imputed single nucleotide polymorphism (SNP) markers for plateauing prediction accuracy.
  • To identify the minimum number of assayed SNP markers required for imputation without compromising prediction accuracy.

Main Methods:

  • Maize progeny from biparental crosses were genotyped with a low density of SNP markers (49-100).
  • Marker imputation was performed to increase marker density to 2911 SNPs.
  • Prediction accuracy was compared between a general combining ability (GCA) model using imputed markers and a within-population (A/B) model.

Main Results:

  • Marker imputation resulted in a general combining ability (GCA) model that performed as well as or better than the within-population (A/B) model for selection response and prediction accuracy.
  • Prediction accuracy for grain yield plateaued beyond 500 imputed markers, while moisture and test weight plateaued beyond 1000 imputed markers.
  • Assaying approximately 50 polymorphic SNP markers was sufficient for imputation without significant loss of prediction accuracy.

Conclusions:

  • Marker imputation is effective for enhancing genomewide selection in maize.
  • A cost-efficient breeding strategy involves assaying a limited number of SNP markers, imputing to higher density, and utilizing the GCA model.
  • This approach optimizes prediction accuracy for traits like grain yield, moisture, and test weight in biparental crosses.