# ​ Uncovering associations between DUS test traits and biochemical composition in safflower germplasm​​

**Authors:** Lianjia Zhao, Fan Wang, Zhongqing Li, Yundan Cong, Chaohong Deng, Jing Xiao, Guorong Yan, Ning Liu, Yanyan Yang, Shuran He, Axiang Gao, Yue Ma, Yu Song, Wei Wang

PMC · DOI: 10.1038/s41598-025-30993-4 · Scientific Reports · 2025-12-05

## TL;DR

This study explores how geography and genetics influence the biochemical makeup of safflower, offering insights for breeding crops with better nutritional quality.

## Contribution

The study introduces a novel integration of phylogeography, chemometrics, and machine learning to uncover biochemical and genetic associations in safflower.

## Key findings

- Geographic isolation in Xinjiang germplasm leads to biochemical homogenization due to genetic bottlenecks.
- Cationic mineral-amino acid complexes adapt safflower to regional soil geochemistry, driving chemodiversity.
- Machine learning predicts traits like crude fiber content more accurately due to developmental hardwiring.

## Abstract

Safflower (Carthamus tinctorius L.), a globally valued medicinal and oilseed crop, exhibits geographically structured biochemical signatures critical for its nutraceutical quality. Our study reveals safflower nutrient blueprint through an integrated approach combining phylogeography, chemometrics, and machine learning. We identified: (1) Evidence suggestive of genetic bottlenecks​​ in Xinjiang germplasm driving biochemical homogenization; (2) ​​Geography-driven chemodiversity​​ where cationic mineral-amino acid complexes adapt accessions to regional soil geochemistry; (3) ​​Evolutionary tradeoffs​​ manifesting as systemic mineral-fatty acid antagonisms; and (4) ​​Machine learning-enabled trait prediction​​, with crude fiber content showing relatively higher predictability due to developmental hardwiring. We revealed that fiber deposition prioritizes morpho-developmental regulators, while calcium accumulation depends on amino acid-mediated transport. Our findings establish that geographical isolation conserves nutrient signatures through reduced gene flow, while metabolic constraints limit co-optimization of competing traits. Our work provides predictive frameworks for precision breeding of climate-resilient safflower with enhanced nutraceutical value.

The online version contains supplementary material available at 10.1038/s41598-025-30993-4.

## Full-text entities

- **Chemicals:** amino acid (MESH:D000596), mineral (MESH:D008903), fatty acid (MESH:D005227), calcium (MESH:D002118)
- **Species:** Carthamus tinctorius (safflower, species) [taxon 4222]

## Full text

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## Figures

4 figures with captions in the complete paper: https://tomesphere.com/paper/PMC12796480/full.md

## References

9 references — full list in the complete paper: https://tomesphere.com/paper/PMC12796480/full.md

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Source: https://tomesphere.com/paper/PMC12796480