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Genome-based trait prediction in multi- environment breeding trials in groundnut.

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Genomic selection (GS) models incorporating genetic markers improve prediction accuracy for groundnut breeding. Naïve and informed interaction models show promise for complex traits, enhancing genetic gains.

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Area of Science:

  • Agricultural Science
  • Genetics
  • Plant Breeding

Background:

  • Groundnut (Arachis hypogaea) breeding faces challenges due to complex traits and significant genotype × environment interactions.
  • Genomic selection (GS) offers a cost-effective approach to capture genetic factors for improved breeding outcomes.

Purpose of the Study:

  • To evaluate the predictive accuracy of different genomic selection (GS) models in groundnut.
  • To identify optimal GS models for breeding programs targeting complex agronomic traits.

Main Methods:

  • A training population of 340 elite groundnut lines was genotyped using an Axiom_Arachis SNP array.
  • Phenotypic data for key agronomic traits were collected across three Indian locations.
  • Four GS models (main effects, naïve interaction, and naïve/informed interaction) were tested using cross-validation schemes (CV0, CV1, CV2).

Main Results:

  • Models incorporating marker information (M2, M3, M4) demonstrated superior prediction accuracy compared to the model without markers (M1).
  • High prediction accuracies (>0.600) were achieved for traits like flowering time, maturity, seed weight, oleic acid, and disease resistance.
  • Medium prediction accuracies (0.400-0.600) were observed for pods per plant, shelling percentage, and total yield per plant.

Conclusions:

  • Naïve interaction and naïve/informed interaction GS models are suitable for enhancing prediction accuracy in groundnut.
  • GS is an efficient breeding strategy for groundnut, capturing both small and large genetic effects to accelerate genetic gains for complex traits like yield and oil content.