# Characterization of Genetic Diversity and Genomic Prediction of Secondary Metabolites in Pea Genetic Resources

## TL;DR

This study explores genetic diversity and predicts secondary metabolites in pea plants from different regions to help select varieties with desired nutritional traits.

## Contribution

The study provides new insights into the genetic architecture and genomic prediction of secondary metabolites in pea accessions from diverse geographic origins.

## Key findings

- Pea accessions from West Asia had the highest levels of total phenolic compounds and antioxidant activity.
- Genomic prediction models showed moderately high accuracy for most traits except raffinose content.
- A GWAS identified 37 significant SNPs associated with metabolites, indicating a polygenic genetic architecture.

## Abstract

This study aimed to assess the variation, genetic architecture, and genome-enabled prediction of traits with nutritional and health relevance in 156 pea (Pisum sativum L.) accessions of diverse geographic origins. The traits included the total phenolic compounds (TPCs), two saponins (Ssβg, Ss1), sucrose, three raffinose-family oligosaccharides (RFOs), and the in vitro antioxidant activity (AA). An analysis of variance revealed significant effects of regional germplasm pools for all traits. Accessions from West Asia showed the highest TPC and AA levels, while those from the East Balkans and the UK displayed the lowest values. High saponin and RFO concentrations characterized accessions from Germany and the UK. Correlation and PCA analyses highlighted strong associations within compound classes and an overall negative relationship between TPCs/AA and saponins/RFOs. The accessions were clustered into seven metabolically distinct groups, partially reflecting their geographic origin. The linkage disequilibrium decayed rapidly (average of 4.7 kb). A GWAS based on 10,249 SNP markers identified 37 significant SNPs, 35 within annotated genes, associated with the metabolites, indicating a polygenic genetic architecture. Genomic prediction models showed a moderately high predictive ability (>0.40) for all traits except the raffinose content. Our findings can support line selection and the identification of genetic resources with a desired level of secondary metabolites.