article · The Plant Genome
Quinoa is an increasingly important crop due to its high nutritional value and environmental resilience, but traditional breeding progress has been slow. To accelerate genetic improvement, whole-genome resequencing was carried out on 610 accessions, generating approximately 1.8 million single-nucleotide polymorphisms. Researchers evaluated genomic prediction using four statistical and machine learning models across six field trials conducted in Australia and Pakistan, assessing seven phenological and yield-related traits. The models demonstrated broadly similar performance, with no single method consistently outperforming the others across all traits. Prediction accuracy was strongly linked to trait heritability and environmental correlations, achieving the highest accuracy for developmental traits and the lowest for seed yield. Even for challenging traits, ranking metrics confirmed that genomic selection remains effective for identifying superior plant lines, establishing key benchmarks for global breeding initiatives.
Quinoa is a highly nutritious and hardy crop, but developing better varieties through conventional breeding takes considerable time. By proving that genomic selection can accurately predict plant performance across diverse international environments, this research helps breeders identify high-performing lines much faster, potentially boosting crop yields and climate adaptability.
This research provides applied benchmarks that could be used by commercial seed companies and plant breeding programmes seeking to accelerate quinoa improvement. The findings demonstrate practical utility for selecting top-performing genotypes under different cross-validation schemes. This represents applied research tested across multiple field environments, serving as a ready foundation for integration into active genomic selection pipelines.
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Quinoa (Chenopodium quinoa Willd.) is gaining global importance for its nutritional value and adaptability; however, breeding progress remains limited. Genomic selection (GS), combined with rapid generation cycles, offers a strategy to accelerate genetic improvement. We conducted whole-genome resequencing of 610 accessions and present the first evaluation of genomic prediction in quinoa evaluated across six field trials in Australia and Pakistan for seven phenological and yield-related traits. Using ∼1.8 million single-nucleotide polymorphisms, we compared four models-genomic best linear unbiased prediction, reproducing kernel Hilbert space, BayesC, and light gradient boosting machine-for genotype ranking under four cross-validation schemes: predicting new genotypes (CV1), sparse testing (CV2), leave-one-location-year-out (CV0), and across locations. Model performance was evaluated using Pearson's correlation for overall accuracy and normalized discounted cumulative gain (NDCG@10) for ranking top performers. The four models were similar, with no method dominating across traits. NDCG@10 scores revealed that predictions remained useful for selecting superior genotypes even for difficult traits. Prediction accuracy was strongly associated with heritability and trait correlations across and within- location environments. Accuracy was highest for developmental traits and lowest for seed yield, while seed traits showed location-specific responses with higher accuracy in Australia. These findings support GS as a promising tool for quinoa breeding and provide benchmarks for global implementation.
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DOI: 10.1002/tpg2.70277
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