# Genomic prediction and QTL analysis for grain Zn content and yield in Aus-derived rice populations

## TL;DR

This study identifies genetic regions (QTLs) linked to zinc content and yield in rice, aiming to improve zinc biofortification for better nutrition.

## Contribution

The paper reports novel QTLs for grain zinc and yield in rice populations, including a QTL co-located with a known zinc transport gene.

## Key findings

- 56 QTLs for seven traits and 16 QTLs for yield and zinc were identified across four rice populations.
- qZn7.1 had the highest phenotypic variance (17.8%) and additive effect (2.5 ppm) for grain zinc content.
- Multi-trait genomic prediction accuracy was 0.24 for yield and 0.16 for zinc.

## Abstract

Zinc (Zn) biofortification of rice can address Zn malnutrition in Asia. Identification and introgression of QTLs for grain Zn content and yield (YLD) can improve the efficiency of rice Zn biofortification. In four rice populations we detected 56 QTLs for seven traits by inclusive composite interval mapping (ICIM), and 16 QTLs for two traits (YLD and Zn) by association mapping. The phenotypic variance (PV) varied from 4.5% (qPN4.1) to 31.7% (qPH1.1). qDF1.1, qDF7.2, qDF8.1, qPH1.1, qPH7.1, qPL1.2, qPL9.1, qZn5.1, qZn5.2, qZn6.1 and qZn7.1 were identified in both dry and wet seasons; qZn5.1, qZn5.2, qZn5.3, qZn6.2, qZn7.1 and qYLD1.2 were detected by both ICIM and association mapping. qZn7.1 had the highest PV (17.8%) and additive effect (2.5 ppm). Epistasis and QTL co-locations were also observed for different traits. The multi-trait genomic prediction values were 0.24 and 0.16 for YLD and Zn respectively. qZn6.2 was co-located with a gene (OsHMA2) involved in Zn transport. These results are useful for Zn biofortificatiton of rice.

The online version contains supplementary material available at 10.1007/s13562-024-00886-0.