Genomic prediction in quinoa across contrasting environments using statistical and machine learning models

Abstract

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.